HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY Office of International Study Programs Faculty of Geology and Petroleum Engineering Petroleum Geology Department A thesis submitted in accordance with the requirements for the degree of BACHELOR OF ENGINEERING (Petroleum Engineering) BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHODS IN FIELD X, BLOCK Y, CUU LONG BASIN BY HUYNH THANH TOAN Academic supervisor: MSc. Thai Ba Ngoc Industry Supervisor: MSc. Pham Huynh Kieu Trinh June, 2022 VIETNAM NATIONAL UNIVERSITY HCMC UNIVERSITY OF TECHNOLOGY SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom – Happiness THESIS OBJECTIVES FACULTY OF GEOLOGY & PETROLEUM ENGINEERING DEPARTMENT: PETROLEUM GEOLOGY STUDENT: HUYNH THANH TOAN ID: 1652610 MAJOR: PETROLEUM ENGINEERING CLASS: CC18DK11 1. Thesis title: “BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN” 2. Thesis objectives: - Depending on geological, seismic and geophysical drilling well to build geological modeling for X field. Geological Modeling contributes to classify the geological picture and the distribution of reservoir parameters of studies objects. - Systematize the basic theory of model building methods. - Presenting the classification of fault systems as the basis for the attribute model. 3. Date of assignment: January 2022 4. Date of submission: June 2022 5. Supervisors’ name: MSc. Thai Ba Ngoc MSc. Pham Huynh Kieu Trinh The content and request of the thesis has been approved by the Department of Petroleum Geology, Faculty of Geology and Petroleum Engineering. Ho Chi Minh city, …………………… 2022 HEAD OF DEPARTMENT ACADEMIC SUPERVISOR PART FOR DEPARTMENT, FACULTY Approver (preliminary remark): ............................................................................ Department: .......................................................................................................... Date of defense: .................................................................................................... Overall score: ....................................................................................................... Storage destination: ………….................................................................................. VIETNAM NATIONAL UNIVERSITY HCMC UNIVERSITY OF TECHNOLOGY FACULTY OF GEOLOGY & PETROLEUM ENGINEERING SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom – Happiness ----------O---------- THESIS ASSESSMENT (For academic supervisor 1) Student: HUYNH THANH TOAN Department: PETROLEUM GEOLOGY 1. Thesis title ID: 1652610 Class: CC18DK11 “BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN” 2. Supervisor’s name: MSc. Thai Ba Ngoc 3. Overview of thesis: Number of pages: 118 Number of chapters: 4 Number of tables: 16 Number of figures: 63 Number of references: 25 Software: Petrel 4. Overview of drawings: Number of drawings: 0 A1: 0 A2: 0 Others: Number of hand-made drawings: 0 Number of digital drawings: 0 5. The main advantages of thesis: ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… 6. The main disadvantages of thesis: ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… 7. Recommend: Accept to defend: Add more to defend: Cannot defend: 8. Questions that student must answer: a)………………………………………………………………………………………… ……………………………………………………………………………………........... b)………………………………………………………………………………………… …………………………………………………………………………………………... c)………………………………………………………………………………………… ……………………………………………………………………………………........... 9. General evaluation (by word: Great-Excellent-Fair-Pass). Score: /10 Signature VIETNAM NATIONAL UNIVERSITY HCMC UNIVERSITY OF TECHNOLOGY FACULTY OF GEOLOGY & PETROLEUM ENGINEERING SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom – Happiness ----------O---------- THESIS ASSESSMENT (For Reviewer) Student: HUYNH THANH TOAN Department: PETROLEUM GEOLOGY 1. Thesis title ID: 1652610 Class: CC18DK11 “BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN” 2. Reviewer’s name: 3. Overview of thesis: Number of pages: 118 Number of chapters: 4 Number of tables: 16 Number of figures: 63 Number of references: 25 Software: Petrel 4. Overview of drawings: Number of drawings:0 A1: 0 A2: 0 Others: Number of hand-made drawings: 0 Number of digital drawings: 0 5. The main advantages of thesis: ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… 6. The main disadvantages of thesis: ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… 7. Recommend: Accept to defend: Add more to defend: Cannot defend: 8. Questions that student must answer: a)………………………………………………………………………………………… ……………………………………………………………………………………........... b)………………………………………………………………………………………… …………………………………………………………………………………………... c)………………………………………………………………………………………… ……………………………………………………………………………………........... 9. General evaluation (by word: Great-Excellent-Fair-Pass). Score: /10 Signature ABSTRACT The oil reservoir in the Fractured Basement Reservoirs of the Cuu Long basin has great oil and gas potential but has a rather complex geological structure, is an area consisting of many intrusive magmatic rock systems divided by many fault systems. tectonic fracture through many different stages. That leads to the construction of geological models more difficult, complex and plays a decisive role for future forecasting periods. Depending on geological conditions and data system, many methods have been applied with distinct advantages and disadvantages. These include: effective porosity method, continuous cracking model method, discrete cracked network method, artificial neural network method, Halo method, and improved Halo method. These fractured reservoirs contain porosity and permeability systems that are dependent on tectonic systems and are thus segregated into structural Block. Two types of modelling approaches are the Net Pore Volume Model (NPV) and the Halo Fault Model. The Net Pore Volume Model is used to generate OIIP volumes in a structural Block based model using porosity and net/gross to describe the rock volume in a probabilistic method. The Halo Fault Model is used to characterize the reservoir with a fracture enhanced halo around lineaments described by seismic in the reservoir. A fractured halo fault is applied to the lineaments and porosity and permeability volume is produced. This is used to simulate fluid flow modelling. Both models are matched against well test and historical data and or dynamic data to ensure quality and matched against conventional volumetric approach. Thus topic: “BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN” is chosen in my thesis. ACKNOWLEDGEMENT I am immensely grateful to my supervisors MSc. Thai Ba Ngoc for their supervision and support throughout the course of the project, especially their comments play an important role in helping me to solve my report effectively. Therefore, I would like to thank them a lots for their suggestions which have a great influence on addressing my personal problem. I also would like to thank MSc. Pham Huynh Kieu Trinh, Engineer of CLJOC for providing me with this opportunity to work on this project and her guiding instructions through it. Moreover, thanks to the useful provision of Schlumberger Company about Petrel software, I am able to construct geology modeling which is the premise to support for reserve assessment of reservoir. Without this support, it is very difficult for me to complete my project. In conclusion, this internship is a positive opportunity for me to learn more real experiences and knowledge which are not taught in university. So that I must try my best to finish this project more successfully. Hồ Chí Minh city, June 2022 Student HUYNH THANH TOAN TABLE OF CONTENTS ABSTRACT --------------------------------------------------------------------------------------------ACKNOWLEDGEMENT---------------------------------------------------------------------------LIST OF FIGURES ----------------------------------------------------------------------------------LIST OF TABLES ------------------------------------------------------------------------------------ABBREVIATIONS -----------------------------------------------------------------------------------NOMENCLATURES --------------------------------------------------------------------------------CHAPTER 1. INTRODUCTION----------------------------------------------------------------- 1 1.1. Rationale --------------------------------------------------------------------------------------- 1 1.1.1. Research activities ---------------------------------------------------------------------- 1 1.1.2. Thesis objectives ---------------------------------------------------------------------------------- 2 1.1.3. Methodology---------------------------------------------------------------------------------------- 2 1.2. Research area characteristic. ---------------------------------------------------------------- 2 1.2.1. Geographical location of Field X (Block 9-2), Cuu Long basin. ------------------- 2 1.2.2. Structural - tectonic features ------------------------------------------------------------------- 5 1.2.3. Features of petrographic stratigraphy ------------------------------------------------------ 11 1.3. Literature review ----------------------------------------------------------------------------- 20 1.3.1. International researches ------------------------------------------------------------------------ 20 1.3.2. Studies in Viet Nam ----------------------------------------------------------------------------- 21 CHAPTER 2. METHODOLOGY OF FRACTURED BASEMENT RESERVOIR MODEL ----------------------------------------------------------------------------------------------- 24 2.1. Overview of fractured granite basement ------------------------------------------------- 24 2.1.1. Status of research and exploitation --------------------------------------------------------- 24 2.1.2. The formation of fractured granite basement in the Cuu Long basin ------------ 25 2.2. Methodology of fractured granite basement model. ------------------------------------ 29 2.2.1. Types of fractured basement reservoir models------------------------------------------ 29 2.2.2. NPV (Net Pore Volume) method------------------------------------------------------------ 32 2.2.3. CFM (Continuous Fracture Modeling) method ----------------------------------------- 34 2.2.4. DFN (Discrete Fracture Network) method ----------------------------------------------- 35 2.2.5. The method of artificial neural network (ANN) ---------------------------------------- 37 2.2.6. Halo method --------------------------------------------------------------------------------------- 38 2.2.7. Advanced Halo method ------------------------------------------------------------------------ 41 2.3. The geological properties support for the NPV and Halo method ------------------- 45 2.3.1. The factors of fractured and faults ---------------------------------------------------------- 45 2.3.2. Basis of Fault classification ------------------------------------------------------------------- 55 CHAPTER 3. BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN----------------------------------------------------------------------------- 67 3.1. Structure Definition -------------------------------------------------------------------------- 67 3.2. Workflow for 3D Geological Modelling ------------------------------------------------- 70 3.2.1. Structural Modelling ---------------------------------------------------------------------------- 71 3.2.2. Property Modelling ------------------------------------------------------------------------------ 72 3.2.3. Net Pore Volume Model (NPV) ------------------------------------------------------------- 74 3.2.4. Halo Fault Model --------------------------------------------------------------------------------- 98 3.3. Volumetric Calculation ------------------------------------------------------------------- 103 3.4. Upscaling ------------------------------------------------------------------------------------ 107 CHAPTER 4. CONCLUSION AND RECOMMENDATIONS ------------------------ 111 4.1. Conclusions: -------------------------------------------------------------------------------------------- 111 4.2. Recommendations: ----------------------------------------------------------------------------------- 112 REFERENCES ------------------------------------------------------------------------------------ 113 LIST OF FIGURES Figure 1.1: Cuu Long basin 3 Figure 1.2: Cuu Long Basin and Tertiary Sedimentary Basins off the coast of Vietnam 4 Figure 1.3: X field is located in Block 9-2 5 Figure 1.4: Map of foundation roof structure in Cuu Long Basin 9 Figure 1.5: Structural elements of the Cuu Long basin 10 Figure 1.6: General stratigraphic column of Cuu Long Basin 11 Figure 2.1. The diagram of the distribution of reserves in the basement of the Cuu Long basin 25 Figure 2.2: Model of oil and gas trap for fractured basement of Cuu Long basin: (1) rock containing fractured basement; (2) Lightning set D – barrier and aquifer; (3) Rocks containing sandstone 27 Figure 2.3: Classification of basement rocks according to geological and petrographic units 27 Figure 2.4: Ideal fractured system in two porosities, two permeability model 29 Figure 2.5: Relationship of porosity and NTG according to reservoir depth 33 Figure 2.6: Fractured model by DFN method 36 Figure 2.7: Fractured model by Halo method 39 Figure 2.8: Effect of fault on porosity distribution 41 Figure 2.9: Horizontal porosity distribution 42 Figure 2.10: Diagram of porosity distribution with depth 43 Figure 2.11: Mechanism for creating cracks under the action of compression, separation and horizontal sliding 46 Figure 2.12: Mechanism of formation of fractured with faults 46 Figure 2.13: Model of fractures, freezing fractures and volume shrinkage of magmatic rock 48 Figure 2.14: Thermodynamic activity mode 49 Figure 2.15: Weathering model 50 Figure 2.16: The geological features of the fault 50 Figure 2.17: Orientation of the fault elements on the horizontal plane 51 Figure 2.18: Forward fault model 52 Figure 2.19: Inverse fault model 53 Figure 2.20: Sliding fault model 54 Figure 2.21: The swing arm swings and slides along the fault plane (slump) 54 Figure 2.22: The suspension wing is pulled away from the cylinder under the action of the tension force 55 Figure 2.23: Wells image 58 Figure 2.24: FMI measuring tool 58 Figure 2.25: PLT measuring tool 60 Figure 2.26: Log lines of PLT 61 Figure 2.27: The tectonic phase from the Jurassic to the present of the Cuu Long basin 66 Figure 3.1: Basement Structural Blocks of X field 69 Figure 3.2: Segments in the Structural Block A of X field 70 Figure 3.3: Segments in the Structural Block A of the X field at slice 4200m with in terpreted Faults 70 Figure 3.4: 3D Reservoir Modelling Workflow 72 Figure 3.5: Basement Porosity vs. Depth of X-1X well 78 Figure 3.6: Basement Porosity vs. Depth of X-2X well 79 Figure 3.7: Basement Porosity vs. Depth of X-2XST well 80 Figure 3.8: Basement Porosity vs. Depth of X-3X well 81 Figure 3.9: Basement Porosity vs. Depth of X-1X, X-3X & SD-6PST wells 82 Figure 3.10: Porosity vs. Depth in the Basement of Cuu Long Basin 85 Figure 3.11: Establishing the Background, Peak and Cut-off Gas values 88 Figure 3.12: Basement NTG vs. Depth of X-1X 89 Figure 3.13: Basement NTG vs. Depth of X-2X 90 Figure 3.14: Basement NTG vs. Depth of X-2XST 91 Figure 3.15: Basement NTG vs. Depth of X-3X 92 Figure 3.16: Basement NTG vs. Depth of X-1X and X-3X 93 Figure 3.17: Basement NTG vs. Depth in the Cuu Long Basin 96 Figure 3.18: Basement Porosity Model for X Field 98 Figure 3.19: Basement NTG Model for X Field 99 Figure 3.20: Porosity and NTG cross sections through X-1X well (Most likely case), X Field 99 Figure 3.21: Porosity and NTG strike sections (Most likely case), X Field 100 Figure 3.22: HALO Fault Model 101 Figure 3.23: Vertical Porosity Distribution Function in HALO Fault Model 102 Figure 3.24: Lateral Porosity Distribution Function in HALO Fault Model 102 Figure 3.25: Porosity Sections in HALO Fault Model of X Field 104 Figure 3.26: Porosity Sections in HALO Fault Model of X Field 104 Figure 3.27: Porosity Sections in HALO Fault Model of X Field 104 Figure 3.28: Static values and histograms of pre and after Upscaling of 3D porosity model for low case of Case 16 111 Figure 3.29: Porosity in Halo Fault Model before and after Upscaling 111 Figure 3.30: Porosity in Halo Fault Model before and after Upscaling Section along X-3X well 112 LIST OF TABLES Table 2.1: Criteria for classifying faults cut by wells 61 Table 3.1: 3D grid configuration of X model 76 Table 3.2.: Functions of Basement Porosity Vs Depth for all Wells in X 82 Table 3.3: Porosity – Depth relationship applied for X Segments and Blocks 83 Table 3.4: Summary of NTG - Depth relationship of all wells in X 94 Table 3.5: NTG – Depth relationship for X Basement Segments and Blocks 95 Table 3.6: NTG and Porosity Functions Applied to each X Basement Segments and Blocks Table 3.7: Scenario #1 in the X Field: All faults are conductive 97 103 Table 3.8: Scenario #2 in the X Field: Bounding faults – Non-conductive and Other faults – conductive 103 Table 3.9: Values of Applied Parameters 105 Table 3.10: OIIP Estimated from NPV Model 106 Table 3.11: OIIP Estimated from Halo Fault model – Case #1 106 Table 3.12: OIIP Estimated from Halo Fault model – Case #2 107 Table 3.13: OIIP Estimated from Halo Fault model – Case #3 107 Table 3.14: OIIP of X Basement Estimated from Halo Fault Models 108 Table 3.15: OIIP Estimated from Halo Fault Model (Variogram method) 108 Table 3.16: Difference between pre and after upscaling 112 ABBREVIATIONS ANN Artificial Neural Network BODP Barrels of oil per day CFM Continuous Fracture Modeling CLJOC Cửu Long Joint Operating Company DFN Discrete Fracture Network DST Drill Stem Test DTF Distance to fault FMI Formation Micro Image GR Gamma ray MD Measured Depth NPV Net Pore Volume NTG Net To Gross OIIP Oil initial in place PLT Production logging tool PSDM Pre stack depth migration RAI Relative Acoustic Impedance RMS Root mean square SB STOIIP TOB Sói Bạc Stock tank oil initial in place Top of basement TVDSS SIS True vertical depth sub sea Sustainability Indicators System BODP Barrels of oil per day CFM Continuous Fracture Modeling HVJOC Hoang Vu Joint Operating Company DST Drill Stem Test DTF Distance to fault FMI Formation Micro Image GR Gamma ray MD Measured Depth NPV Net Pore Volume NTG Net To Gross OIIP Oil initial in place PLT Production logging tool OWC Oil-Water Contact SGS The Sequential Gaussian Simulation STOIIP TOB TVDSS Stock tank oil initial in place Top of basement True vertical depth sub sea NOMENCLATURES Boi: Initial Oil Formation Volume Factor Ar: Area of reservoir. N: Net formation thickness G: Gross formation thickness ht: Total formation thickness of the oil zone. Φ: Porosity of the oil zones. Swi: Initial water saturation Bachelor Thesis CHAPTER 1. INTRODUCTION This section presents the rationale, objectives, research activities and methodology in order to complete this project. Besides, the project background is also represented in this chapter. 1.1. Rationale Oil and gas exploitation in Fractured basement reservoir is an issue that has been widely studied in Vietnam and International. Currently, in Vietnam, there are many oil fields that have been discovered and are being exploited in Fractured basement reservoir such as: Bach Ho, Rang Dong, Rong, Phuong Dong, Su Tu Vang, Su Tu Den, Su Tu Trang, Thang Long, etc. For the Cuu Long basin, the oil and gas potential in the basement is very large, but the heterogeneous and complex fracture system makes it difficult for exploration. If drilling is not right in the well fractured area of the field, it will cause great economic loss. Therefore, the predictive analysis of the fracture system and the construction of geological models for the basement reservoir in the Cuu Long basin is a very important role in the prediction of exploitation and investment in construction of production equipment. To better understand the properties of fractured basement rock, students choose to study the topic “BUILDING GEOLOGICAL MODEL FOR BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN”. This is a highly urgent research paper, providing a lot of knowledge about the fault system in the fractured basement reservoir. 1.1.1. Research activities Object: The fractured basement reservoir, X field, Block 9-2, Cuu Long basin Scope of the research: The thesis focuses on studying the building of parameters model of the basement reservoir using the copyright Petrel software of Schlumberger company by NPV and Halo methods. HUYNH THANH TOAN 1 Bachelor Thesis 1.1.2. Thesis objectives - Collect, synthesize and process necessary information and data (geology, seismology, well geophysics...) of the X field to solve the requirements of the NPV and Halo methods. - Systematize the basic theory of model building methods. Detailed study on the theoretical basis and application process of the NPV and Halo methods. - Presenting the classification of fault systems as the basis for the paramaters model. - Conduct the process of model building and model quality control with Petrel support software, copyrighted and sponsored by Schlumberger, provided to the Faculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology. 1.1.3. Methodology - Synthesize and analyze well geophysical data, seismic, regional geology and some related documents to serve the classification of fault systems. - Analyze the advantages, disadvantages and limitations of the existing methods being applied to build a model of porosity-permeability distribution for the fractured granite rock. - Research on the formation of the basement rock, the formation and development of the fracture system in the basement. - Systematize the theoretical basement and application process of the Halo modeling method. 1.2. Research area characteristic. 1.2.1. Geographical location of Field X (Block 9-2), Cuu Long basin. Cuu Long basin is located mainly on the southern continental shelf of Vietnam, part of the Mekong estuary area, with coordinates 90 - 110 north latitude, 106030' - HUYNH THANH TOAN 2 Bachelor Thesis 1090 east longitude, 400 km long in the direction Northeast - Southwest, from the coast of Phan Thiet to the mouth of the Hau River. The distribution area of the basin is about 36,000 km2, including blocks 09, 15, 16, 17 and a part of lots 01, 02, 25 and 31. [14] Figure 1.1: Cuu Long basin [4] Morphologically, the Cuu Long basin has an oval shape, curves towards the sea and lies along the coast of Vung Tau - Binh Thuan. The Cuu Long basin is adjacent to the mainland to the northwest, separated from the Nam Con Son basin by the Con Son uplift, the southwest is separated from the Malay - Tho Chu basin by the Khorat Natuna uplift and the northeast is the Khorat - Natuna uplift zone Tuy Hoa slip cut is separated from Phu Khanh basin (Figure 2.2). This is a basin with closed sediments typical of Vietnam Bon, which is filled mainly by Oligocene - Miocene terrigenous sediments and Pliocene - Quaternary shelf mantle. Their greatest thickness at the center of the basin can reach 7-8km. [14] HUYNH THANH TOAN 3 Bachelor Thesis Figure 1.2: Cuu Long Basin and Tertiary Sedimentary Basins off the coast of Vietnam [4] The X structure is located in Block 9-2 (Figure 2.6) in the Cuu Long Basin, southeast of Vung Tau City, offshore Vietnam. The block covers an area of 1,370km². HUYNH THANH TOAN 4 Bachelor Thesis Figure 1.3: X field is located in Block 9-2 [11] Oil and gas extracted from X field is transported by a 25km-long undersea pipeline system to oil and gas processing equipment at Bach Ho field. Crude oil is processed and stored in a floating storage, then sold to tankers for transport to refineries. The energy-rich gas from this field is processed offshore by AJOC and then transported to onshore gas stations for distribution for domestic demand for natural gas, liquefied petroleum gas and condensate. According to AJOC, the development of X field connected to Bach Ho field Lot 9-1 is the first project in Vietnam using existing facilities of Bach Ho field to minimize investment costs, creating a new way of developing offshore oil and gas fields in Vietnam. [14] Currently, A JOC has drilled 4 development wells at X field, and is conducting assessment and locating wells for additional development wells at the cracked foundation containing products. It is expected to exploit this mine within 20 years. 1.2.2. Structural - tectonic features Structural features The Cuu Long basin is morphologically oval shaped extending from the Northeast to the Southwest, the eastern limit is the East Sea of Vietnam, the West is the Mekong Delta, the North is the uplifted zones of the Da Lat mass if, Kon Tum. In the HUYNH THANH TOAN 5 Bachelor Thesis Cuu Long basin, the seismic strata that have been linked and mapped by contractors are summarized as follows: Foundation, F, E, D, C, Bach Ho clay, BI.1, BI.2, BII, A. The main structural elements in the Cuu Long Basin are clearly shown on the maps from the base of the foundation to the top of the E. Cuu Long basin is divided into 02 structural: - Lower structural: This architectural layer is created by the eruption and intrusive formations of Triassic Kreta age, including granite, biotite, granodiorite, diorite and many places appear rhyolite in the Hon Khoai, Dinh Quan and Ca Na complexes. - Upper structural: This architectural layer is the Cenozoic sedimentary cover, made up mainly of Eocene - Quaternary terrigenous formations. It is divided into 03 structural sub-layers with the boundaries of non-conforming surfaces: o Lower structure subsequence: Including Ca Coi Formation and Tra Cu Formation (Eocene - Lower Oligocene). Most of the coarse-grained sediments are located at the bottom, interspersed with fine-grained particles above containing many VLHCs of continental origin, distributed in narrow trenches running along the center of the basin (East and West of Bach Ho structure), thick from 800 ÷ 2,200 m, formed when the collision between the Indian microplate and the Eurasian plate in the middle Eocene period caused continental crust cracking. The layers are distributed diagonally and irregularly overlapping the ancient rocks. o Middle structure subsequence: Including Tra Tan Formation and Bach Ho Formation (Upper Oligocene - Lower Miocene) with a wide distribution, covering up to the central trench, thickness from 900 ÷ 3600 m. The rather large thickness accumulated during this time is probably related to the spreading activity of the East Sea floor (17 ÷ 32 million years), so this is temporarily called the period of rift co-generation. The mostly fine-grained sediments HUYNH THANH TOAN 7 Bachelor Thesis contain many VLHCs, which are quite good local barrier layers. The sedimentary environment is mainly freshwater lakes, deltas, brackish coastal waters and shallow seas. The uncomforting overlays on the ancient formations are less oblique, but tend to increase in thickness at the center, and angular disconformity is evident at the edge, especially at the northeast edge. o Upper structure subsequence: Consisting of 03 formations Con Son, Dong Nai and East Sea (middle Miocene Quaternary) uncomfortably covered the middle and lower architectural sub-layers. The area is expanded quite a lot, related to the continental shelf development and the recent spreading period is clearly shown on the maps from the base of the foundation to the top of the E. Fault system: The fault system in the Cuu Long basin is divided into 4 main groups according to the respective directions: Northeast - Southwest, East - West, North - South and small faults in different directions of the East Sea (Pliocene - Quaternary). The sediments are mainly shallow sea interspersed along the shore, with a general thickness of 1,800 ÷ 2,200 m with horizontal sediments, covering ancient sediments. In the early Tertiary, the collision between the Indo-Australian and Eurasian plates caused the formation and development of sedimentary basins in Southeast Asia, including the Cuu Long basin. Most of the important faults in this basin are forward faults inherited from the foundation, co-sedimentation development and all disappeared in the late Oligocene. Two fault systems with the Northeast - Southwest and East - West directions play a key role in controlling the geological development history and the main architectural elements of the basin. In which, the fault system with the Northeast Southwest direction is associated with the rifting stage and is the main factor controlling the Dragon - Bach Ho central uplift zone. The East-West fault system is younger than the previous fault system, and in many places, the East-West horizontal shift is clearly visible. Especially at the intersection of fault systems, local compression often occurs, HUYNH THANH TOAN 8 Bachelor Thesis leading to the appearance of reverse sloping faults. Each fault system has different formation time, activity intensity, and displacement amplitude. However, most of them were inactive at the end of the Early Oligocene, with only a few faults remaining active until the Early Miocene such as in the central and northeastern parts of the basin. Figure 1.4: Map of foundation roof structure in Cuu Long Basin [4] Tectonic features The division of structural units is based on the geological structure characteristics of each area, corresponding to the difference in sediment thickness. These structural units are usually bounded by faults or fault systems of considerable amplitude. If the Cuu Long basin is considered as a structural unit of level I, then the structural units of level II are: - Main of Cuu Long basin: occupies more than 3/4 of the area. This is the main subsidence part of the basin - where most of the oil and gas prospects are concentrated. They include: Northeast ridge, West Bach Ho basin, East Bach Ho HUYNH THANH TOAN 9 Bachelor Thesis basin, Northwest inclined slope, Southeast inclined slope, Central uplift zone, Northeast divergent zone and Southwest divergent zone. The boundaries of the structural units are shown in Figure 2.5. Figure 1.5: Structural elements of the Cuu Long basin [5] - Bac Lieu differential basin: located at the southwest end of the Cuu Long basin with an area of 3,600 km2. - Ca Coi differential basin: located mainly at the mouth of Hau river, has a small area with a small thickness of sediment, about 2,000 m2. - Cuu Long uplift: located to the east of the Bac Lieu and Ca Coi differential basin, separating these two basins from the main basin of the Cuu Long basin. - Phu Quy uplift: is an extension of Con Son uplift, located to the northeast of blocks 01&02. The local positive structures of grade IV are the main objects of oil and gas prospecting and exploration of the basin. HUYNH THANH TOAN 10 Bachelor Thesis 1.2.3. Features of petrographic stratigraphy The stratigraphy of the Cuu Long basin consists of pre-Cenozoic foundation rocks and Cenozoic mantle sediments. The petrographic - sedimentary and petrographic features of each stratigraphic unit are shown in the combined stratigraphic column of the basin (Figure 5). To facilitate the work, search, exploration and extraction of oil and gas, stratigraphic units are compared with seismic sets. The seismic reflectors all coincide with the boundaries of the stratigraphic units. [15] Dầu Khí Figure 1.6: General stratigraphic column of Cuu Long Basin [5] HUYNH THANH TOAN 11 Bachelor Thesis Pre - Cenozoic The basement rock complex in the Cuu Long basin, which is pre-Cenozoic, has heterogeneous composition, mainly intrusive magmatic rocks, including granite, granodiorite, quartz granodiorite, monzonite, diorite, quartz diorite, tonalities with magmatic and metamorphic rocks. The main mineral compositions include quartz, potassium feldspar, plagioclase, biotite, muscovite, amphibole and hornblende. Secondary minerals formed by hydrothermal activity are mainly zeolite, quartz, calcite, sericite, chlorite, kaolinite. The surface layer of the basement rock is often strongly weathered with a thickness of 4 m - 55 m. [15] The lode rocks cut into the basement or fill the fractures, in some places cover directly on the bedrock surface (block 16-1). The main composition is diabase, to a lesser extent basalt in the Bach Ho and Rong fields, andesite and dacite in blocks 15-1, 15-2, with a thickness ranging from a few meters to several tens of meters. The basement rock complex was discovered to accumulate oil and gas of industrial value for the first time at Bach Ho field and was exploited in 1986, since then the basement rock has become an important object of oil and gas prospection and exploration in Vietnam. Cuu Long basin in particular, the continental shelf of South Vietnam in general. [16] In terms of lithology, they can be classified into two main groups: granite and granodiorite - diorite. Based on the comparison of studies at many wells drilled deep into the foundation with the results of research on intrusive magma complexes on land, according to petrographic characteristics and absolute age, they are classified into 03 complexes: Hon Khoai, Dinh Quan and Ca Na. [16] Cenozoic sediments Lying unconformably on the eroded, weathered crystalline basement rock surface are Cenozoic or volcanic formations. Stratigraphy is described from bottom to top, from ancient to young and is summarized in the stratigraphic column. HUYNH THANH TOAN 12 Bachelor Thesis Ca Coi Formation (E2cc) The sediments of this formation were formed in the continental environment: deluvi, proluvi, alluvi with a thickness of 200 ÷ 400 m, described by Le Van Cu at well CL-1X in the subsidence area of the Hau river. They include mainly: gravelstone, multimineral sandstone, interspersed with thin layers of siltstone and hydromica chlorite - sericite clay. The lower part of the formation is cobblestone, gravel (main components of gravel and gravel are andesite, rhyolite, mica slab and quacsite) while the upper part is light-colored coarse-grained sand, gray-green claystone. [20] Fossils of pollen spores were discovered including: Klukiosporites, Triporopollenites, Trudopollis, Plicapollis ... belonging to the group of dry plants, common in the Eocene. Tra Cu Formation (E31tc) – Seismic set F and seismic set EI The Tra Cu Formation was established by Le Van Cu in 1981, 1982 at the Cuu Long - 1X borehole, with a thickness of 250 m in Ca Coi area, Tra Cu district, Tra Vinh province. The sediments of this formation include gravel beds interspersed with layers of coarse-grained sand, powder and clay with colorful colors, thick layered structure, bulk form and deposited in the environment of rivers, lakes, swamps or rivers. Delta. The pebbles have andesite eruption rock composition, are well rounded but have poor selectivity and grow widely in the northwest of the tank. Sandstone has poor roundness, most of the purple particles with clay and calcite are cementitious materials. [21] The top part also alternates layers of coal clay, coal lenses. Fossils of pollen spores include: Trudopollis, Ephedra, Cycas, Ginggo... The paleontological analyzes of VDK for today's wells all determine the early Oligocene age. These formations have a thickness of 0 ÷ 1,500 m, covering the eroded surface of the foundation at a depth of 2,500 ÷ 4,000 m. The upper boundary is improperly covered by the Tra Tan Formation and extended to the edge. Tra Tan Formation (E32tt) – Seismic set E, seismic set D and seismic set C The Tra Tan Formation was established by Ngo Thuong San et al (1980) in well 15A–1X (drilled on Tra Tan structure/ structure 15A). The sediments of this formation HUYNH THANH TOAN 13 Bachelor Thesis consist of thick black clay layers, alternating gray to ash-gray sand layers and black, gray to light gray powder layers. The upper part also encounters coal lenses, coal debris, even layers of coal clay 5 ÷ 7m thick, with pyrite, glauconitic. Sandstone has a fine to medium grain, sometimes coarse grained with the main composition of quartz (accounting for 25-35%), feldspar (from 25-40%) and rock fragments (from 10-25%) is usually ackoz - grauvac sandstone, in some places, it is found in some places with single-mineral quartz sandstone with silicon-cement composition. The siltstone, claystone with thick layering in the form of blocks, with the addition of hydro mica clay, chlorite and a little kaolinite. Pollen spore fossils include: F. Trilobite, Verutricolporites, Ciccatricosiporites. Current VDK paleontological analyzes for new wells all determine the Late Oligocene age for this formation. [21] The Tra Tan Formation is directly covered on the foundation rock complex around the large trench basin, or directly on the Tra Cu Formation in the center of the trench valley, which is inappropriately covered by the Bach Ho Formation. The Tra Tan Formation is divided into 3 different layers from bottom to top: the lower Tra Tan layer, the middle Tra Tan layer and the upper Tra Tan layer. - Lower Tra Tan layer: Associated with seismic set E consisting mainly of sandstone interlaced with claystone, siltstone. Arkose, lithic arkose sandstone, fine-grained, medium - very coarse and granular, planarity from semi-angular to semi-circular, fairly firmly bonded by carbonate cement, clay, quartz. Dark brown - dark brown clay rich in organic matter. The ratio of sandstone/claystone (sandstone accounts for 45 - 65%), increases gradually from the center of the basin to the southwest in blocks 16 and 17. The sediments of the strata can be inclined with large slope angles., which is accumulated in lakeside plains, freshwater lakes and alluvi plains. The lower Tra Tan layer is an important oil reservoir. - Middle Tra Tan layer: Associated with seismic set D consisting mainly of claystone, sandstone intercalated siltstone and coal thin layers. Dark brown-black-brown thick claystone rich in organic HUYNH THANH TOAN 14 Bachelor Thesis matter. The sediments of the strata can lie slightly inclined - highly variable, deposited in the lake environment, deep lakes to bays, coastal plains, relatively wide distribution area almost throughout the basin. The middle Tra Tan layer plays the role of the main oil/gas generation layer, as well as the regional barrier layer of the Cuu Long basin. - Upper Tra Tan layer: Associated with seismic set C consisting mainly of sandstone interlaced with claystone, siltstone. Coarse-grained, gray-white sandstone, Dark-brown-black claystone rich in organic matter rich in humid and saprobe, deposited in brackish lagoons, freshwater pools and alluvial plains, poor Bosedinia spp, predominates in well sections. The upper Tra Tan sediments are distributed throughout the basin, may be inclined - little change. In many wells in blocks 01/97 and block 02/97, there are magmatic rocks, alternating basalt layers, widely distributed. The upper Tra Tan sediments are important oil and gas aquifers. [23] Tra Tan Formation sediments are mainly accumulated in the environment of the alluvial - coastal plain (lake) in the lower Tra Tan layer, gradually moving to deep lakes, brackish lakes/pools - coastal plains in the stratosphere. Tra Tan in the middle and river - coastal plain, lake in the upper Tra Tan layer. The sediments thicken towards the center of the Cuu Long basin. Magma rock found in many drilled wells in the area of block 01/97 with the main composition is andesite, andesite - basalt. [23] These sedimentary formations are of Oligocene age, widely distributed throughout the Cuu Long basin, mainly thick in the central sinkholes and gradually thinning towards the edge with a thickness of about 1,300 m, the upper boundary is a regional mismatch. corresponding to the roof of the seismic set C. The area is wider than the ancient strata. According to petroleum geologists, the claystone of this formation has a very high to very high content and quality of VLHC, especially the middle Tra Tan layer. They are good petroleum-producing strata as well as a good barrier for fractured foundation rock in the Cuu Long Basin. Although the interlocking sandstone layer has a permeability, porosity quality and a continuum that varies from HUYNH THANH TOAN 15 Bachelor Thesis poor to good, it is also the object of the basin's remarkable oil and gas search. [23] Bach Ho Formation (N11bh) – BI seismic set The Bach Ho Formation was established by Ngo Thuong San and Ho Dac Hoai in 1981 and named after the Bach Ho-1 (BH-1) well drilled by Mobil Company in 1974. The sediments of the Bach Ho formation with a thickness of 100 ÷ 1,500 m are widely distributed throughout the Cuu Long basin, corresponding to the BI seismic set, encountered mostly in drilled wells from a depth of about 1,800 ÷ 2,000 m to about 2,800 ÷ 3,000 m. This formation includes all unconformable cover sediments above the Tra Tan Formation and below the Con Son Formation. The lower boundary is determined to be unconformable at the roof of the Tra Tan Formation – the roof of the set C. The upper boundary is the roof of the "Rotalia clay layer" - the roof of the BI set. Rotalia clay layer has a thickness of 30 m - more than 300 m (mainly in the range of 10 m - 150 m). Geologists of Deminex (1980) have called this clay layer Rotalia band. The formation has a sediment thickness varying from 100 m to 1500 m (quite stable from 400 m to 800 m). The characteristic fossils discovered: Rotalia, Ammonia... show that the sedimentary environment is a coastal plain - shallow sea, in the upper part there is much clay and much sand in the lower part. Current VDK paleontological analyzes for new wells all date to the Early Miocene age. The Bach Ho Formation is divided into 2 floors, including the lower Bach Ho layer and the upper Bach Ho layer. - Lower Bach Ho layer: The sediments are mainly sandstone, siltstone (accounting for over 60%), interspersed with claystone layers. Sandstone white, opaque pink, slightly gray, fine-to-medium grain, medium to coarse, very coarse, half-edge to semi-circular wear, moderate to good selectivity, poor cohesion. Claystone dark gray, dark brown, reddish brown, yellow, red. The cementation is kaolinite clay with little calcite cement, hydro mica, sericite and carbonate. Gray to brown, light green to grey, siltstone containing carbonate clay, HUYNH THANH TOAN 16 Bachelor Thesis porous to medium hard siltstone, rich in kaolinite, containing biotite and clay cement. The sediments are accumulated in the marshy, riverside environment, they belong to the middle part of the triangulation far from the estuary. Eruptive magmatic rocks are found on the roof of the strata, common in the north of the basin, mainly in blocks 01/97, 02/97, a little in the Ruby structure, consisting mainly of basalt, andesite - basalt, trachyte - basalt, andesite and tuff. [22] - Upper Bach Ho layer: The sediments consist mainly of gray clay, greenish gray alternating sandstone and siltstone. In the lower part, there is much sand, in the upper part there is much clay, at the top is a claystone layer containing Rotalia "Rolatia clay layer" covering the whole tank with a thickness of 30 m - more than 300 m (mainly in the range of 10 m - 150 m). formed in the coastal plain environment - shallow sea. This is a very good regional barrier layer for the central and northern part of the Cuu Long basin, gradually reducing the ability to block to the southwest when the clay layer has changed into continental mixed clay and powder. Red-brown claystone mixed with gray-green, white-gray, yellow-gray, gray-pink, lilac, green speckled, thinly layered with little lime, some coals in some places. Limestone-free, brittle, fragile, weakly bound siltstone and sandstone, amorphous, clumps, sometimes hard, and schist containing mica flakes formed in shallow marine environments, brackish puddles - coastal plains in canals and rivers in the delta of the triangle. [22] The sediments of the Bach Ho Formation deposited in the river, marsh, and coastal plain environments in the lower part are transformed into shallow coastal sediments in the upper part. The Bach Ho Formation has sand layers interspersed with claystone layers, with good permeability, porosity, and cohesion, which is considered an important petroleum prospecting object in the Cuu Long basin. Oil is currently being extracted from these sand layers, especially in the Bach Ho, Ruby, Rang Dong and Su Tu Den fields. HUYNH THANH TOAN 17 Bachelor Thesis Con Son Formation (N12cs) – Seismic set BII The Con Son Formation was established by Ngo Thuong San in 1980, Do Bat (1993) determined including the Rolalia clay layer) identified the Con Son Formation in the well 15B-1X. The Con Son Formation is associated with the BII seismic set, which includes all the sediments that do not conform to weak angles on the sediments of the Bach Ho Formation. Sudden change of sedimentary material from Rotalia clay to solid block sandstone, cement rich in lime, calcite, anhydrite and interspersed with thin layers of claystone. The lower boundary is clearly shown on paleontological analyzes through the sudden change of the sedimentary environment as well as the richness of paleontological complexes when crossing the boundary. The lower boundary is defined as the roof of the Bach Ho Formation (roof of the Rotalia clay layer) - the roof of the BI set. [21] The upper boundary is defined as the thick sandy bottom of the Dong Nai Formation sediments - the roof of the horizontal BII set. Con Son Formation sediments mainly consist of thick layers of ackoz - lithic sandstone with clay-cement, carbonate, rich in dolomite, anhydrite and calcite in the lower part of the formation. fine to coarse grain, semi-sharp to round edge abrasion, poor to moderate selectivity, poor to hard cohesion - very hard, bulk, alternating layers of siltstone, layered clayey, Clay limestone accounts for nearly 5% and sometimes meets thin coal layers, rich in glauconitic. Lots of rock shards, coal material, little pyrite. Formation porosity ranges from 15 ÷ 20% with weak cohesion and permeability. [19] However, there are no regional barriers here, so this formation and younger formations do not have oil and gas prospects. Pollen spores belong to the complex Florschuetzia, Acrostichum, Rhizosphere, abundant Foraminifera. Current VDK paleontological analyzes for new wells all determine the Middle Miocene age for this formation. The sedimentary environment is alluvial in the west, to the east is swamp - coastal plain. The thickness varies between 250 ÷ 900 m. HUYNH THANH TOAN 18 Bachelor Thesis Dong Nai Formation (N13đn) – Seismic set BIII The Dong Nai Formation was established by Ngo Thuong San in 1980 at the 15G-1X well. The Dong Nai Formation includes all of the weakly unconformable cover sediments on the Con Son Formation and below the East Sea Formation. The lower boundary is adjacent to the Con Son Formation - the roof of the BII set, which is determined by the thick sand layer at the bottom of the Dong Nai Formation with lower gamma. The upper boundary is adjacent to the East Sea formation - the roof of the BIII set, which is located at the bottom of the sand layer with thick layering, block form and low gamma value. [19] The formation has a thickness ranging from 500 to 750 m with the main composition of sandstone interspersed with thin layers of claystone, limestone, dolomite and thin layers of coal, containing many marine petrification. The lower part consists of sand layers interspersed with thin clay layers, in some places there are small-sized pebbles. The upper part is quartz sand of large size with poor selection, sharp grain. In general, the level of cohesion is weak, sometimes even disjointed. Gray, light gray, brownish grey, medium to coarse grained sandstone with occasional pebbles, composed mainly of quartz, a few fragments of metamorphic rock, tuff and mounted pyrite crystals with carbonate-clay, with thick layer or block structure, medium-poor selectivity and round grinding. The brown montmorillonite clay layers are sticky, up to 20 m thick. [19] In clay, sometimes brown coal or light gray powder is also encountered. The weakly cohesive sediments of the Dong Nai Formation are formed in the marsh environment - coastal plain in the western part of the basin and shallow sea riverbed in the eastern part, with typical petrification such as Dacrydium, Operculum. The formation sediments are almost horizontal, inclined gently and without displacement, without petroleum potential. Bien Dong Formation (N2 – Qbđ) – Seismic set A The Bien Dong Formation was established in 1982 by Le Van Cu and Ho Dac Hoai. The sediments of the East Sea formations cover the Miocene sediments HUYNH THANH TOAN 19 Bachelor Thesis inappropriately, widely distributed throughout the continental shelf of Vietnam, lying almost horizontally, gently inclined, not displaced, with a thickness varying from 400 ÷ 700 m, the degree of elevation. increasing thickness towards the East Sea. The main composition of the formation is fine sand - powder - clay, the upper part of the sand becomes coarser and the sand is mixed with powder, pink sand. [19] The top part is Quaternary formations consisting of loose sand interspersed with light gray clay containing a large amount of Foraminifera: ackoz sand, blue and white quartz sand with medium roundness, poor selection, and containing many minerals glauconitic. The mineral composition includes quartz, oligoclaz, octoclaz, mica with bright colored carbonate-cement, mass in some sets. 1.3. Literature review 1.3.1. International researches The construction of geological models has appeared in the world for a long time and is built based on two-dimensional (2D) geological models. Some of the first threedimensional (3D) geological models were built in the 1940s, but they were mostly static and still very simple (eg, the Sullivan Mine Model in Canada). The real threedimensional geological simulation model developed when building on computers combined with geostatistical methods, marked in 1972 by G.G.Walton conducted on the GSI Seiscrop Table computer. In the following years, the development of information technology also markedly changes in methods and approaches to serve the construction of three-dimensional geological models. Some typical research projects carried out by: The topic: "Structural and Tectonic Modeling and its Application to Petroleum Geology" (1992) Research on the application of structural and tectonic models to solving geological problems Oil and Gas. This study focuses on the North Sea continental shelf and surrounding areas. Approaches research issues cover many aspects and different levels from the development of the sedimentary basin, to the fault fracture system and HUYNH THANH TOAN 20 Bachelor Thesis the oil and gas production process. (RMLarsen, H. Brekkle) The project: “Geoscore; a method for quantitative uncertainty in field reserve estimates” (1997). Researched and developed a tool called Geoscore, Geoscore is a series of procedures that allow to identify and measure uncertainty in reserve estimates, before deciding for development. The higher the Geoscore result, the more complex the mine is, with more uncertainty and risk factors. (P.Dromgoole and R.Speers) The topic: "Linking Geostatistics with Basin and Petroleum System Modeling: Assessment of Spatial Uncertainties" (2010) Research and apply multipoint geostatistics to solve uncertainty in building structural models (uncertainty in the time-depth transition) and in the construction of geological facies (facies distribution, petrology). The results of the study help to add to the system of methods for calculating uncertainty in addition to calculating the uncertainty by the usual Monte Carlo method. (Bin Jia) In addition, research papers combining geology and geostatistics were published in turn. Among them, it is impossible not to mention publications such as: - "Geostatistical Reservoir Modeling" by Michael J. Pyrcz, Clayton V. Deutsch, 2nd edition 2014. - "Modeling the earth for oil exploration: Final Report of the CEC's Geoscience I" was compiled and edited by Klaus Helbig, published in 1994. - “Stochastic Modeling and Geostatistics: Principle, Methods, and Case Studies” by Timothy C. Coburn, Jeffrey M. Yarus, R. L. Chambers was published by AAPG in 2006. 1.3.2. Studies in Viet Nam Up to now, the researches related to building three-dimensional geological models used in the oil and gas industry in Vietnam mainly apply the approaches and research results in the world. The results applied to domestic oil and gas subjects are HUYNH THANH TOAN 21 Bachelor Thesis presented mainly through field development reports at oil and gas projects and master's and doctoral theses. In Vietnam, petroleum objects located in the fractured basement have different characteristics from traditional reservoir objects such as sandstone or carbonate. This geological object is highly heterogeneous and poses many challenges for oil and gas companies operating in Vietnam as well as in the world in studying the foundation. Therefore, the research method for building three-dimensional geological models in the foundation is developing and applied a lot in the country. Besides, the terrigenous sedimentary objects previously considered as stratosphere, marginal position or tight cirrhotic seam are also considered and studied in the exploration and development of small mines. Some recent studies are presented as follows: The topic: "Research and application of seismic properties in building 3D geological facies model of X Field in blocks 103-107 area of Song Hong basin, Vietnam" (2016) The 3D geological modeling method being studied and deployed in Vietnam and some results of the application of seismic properties in building facies model for the Middle Miocene sandstone reservoir of the X field, Lot 103-107, Song Hong basin of Vietnam. The study shows that the combination of seismic properties into the 3D geological facies model will better reflect the petrographic facies distribution and the sedimentary environment, closer to reality, which is of great significance in the field of geomorphology oil and gas exploration and production. (Nguyen Hien Phap, Nguyen Tien Hung) The topic: "Reservoir modeling and application of seismic properties to forecast the distribution of Miocene sediments in Su Tu Den field, block 15-1, Cuu Long basin" (2016). The study shows that the approach to assessing seismic data, well data plays an important role in interpreting sedimentary patterns for reservoir modeling. (Nguyen HUYNH THANH TOAN 22 Bachelor Thesis Tien Thinh, Nguyen Handsome) The topic: “Modeling the fractured basement reservoir of field X by the method of artificial neural network (Artificial Neural Network)”. (2008) The author has systematized the theoretical background as well as the advantages and disadvantages of the cracked foundation modeling methods being used. In this thesis, the author has focused on the application of ANN technique to build a model of porosity distribution and permeability of mine X based on real data sources of seismic, geophysical wells, FMI with the supported by Schlumberger's Petrel software. (Do Tuan Khanh) The topic: “The oil body in the basement rock before the Tertiary Su Tu Den and Su Tu Vang mines and geological factors affecting the ability to recover oil”. (2014) The authors presented features of the complex geological structure of the Su Tu Den - Su Tu Vang mine cluster, and analyzed geological factors affecting the ability to recover oil in the field cluster such as: homogenous, characteristically associated fault and fracture system, division into many blocks with separate hydrodynamic regime, side pressure water from Oligocene invasive formations during mining. (ThS Dang Ngoc Quy, PGS.TS Hoang Van Quy) The topic: "Advanced geological model for granite fractured basement in Hai Su Den field area, Cuu Long basin". (2012) The author used the method of building an Advanced Halo geological model based on the fault system and the depth of the object compared to the established foundation surface, on the basis of inheriting the traditional Halo method, based on Based on seismic data combined with well data, three fault systems and three types of fields with different levels of containment have been distinguished to overcome the limitations existing in previous geological models. (Nguyen Quoc Quan) HUYNH THANH TOAN 23 Bachelor Thesis CHAPTER 2. METHODOLOGY OF FRACTURED BASEMENT RESERVOIR MODELING This section discusses the methodology used in process simulation. The workflow of the methodology is shown in the figure below and described in more details in the following subsections. 2.1. Overview of fractured granite basement 2.1.1. Status of research and production. Petroleum in fractured basement rock has been discovered in the early years of the 20th century. Up to now, petroleum has been discovered in more than 30 countries around the world in most continents and in most basement rocks, from young granite rocks of Mezozoic age (in the Cuu Long basin or other areas of Southeast Asia) to preCambrian metamorphic deposits (some deposits in the Middle East such as Azura, Libi) or in the oldest stone of Proterozoic age (in the East Siberian area). Some typical examples of petroleum found in basement rock include: sedimentary basin in Argentina (Cuyo and Neuquen), sedimentary basin in Yemen (DNO block 43, Nexen block 14, Total block 10, etc.) ONV Lot S2), sedimentary basin in Vietnam (Cuu Long) …. It can be seen that petroleum is detected in the basement rock, in rocks ranging from low-level metamorphic sediments to high-grade metamorphic sediments or in magmatic rocks. The flow properties in different basement rock groups are also different, in which the greatest possibility for flow is fractured granitoid basement rocks. Some typical mines of the granitoid basement rock group in the world can be mentioned as: Mezozoic-aged granitoid basement rock in the Kora mine of New Zealand. In Vietnam, the large deposits in the cracked garnitoid foundation rock can be mentioned as: the pre-Third granitoid foundation at the Bach Ho, Rong, Su Tu Den, Su Tu Trang, Su Tu Vang, and Hai Su Den quarries, Yellow Tuna, Phuong Dong, Rang Dong... The largest oil stored in basement rocks belongs to the Cuu Long basin, with HUYNH THANH TOAN 24 Bachelor Thesis an estimated potential of 6,400 million barrels. So far, 25 structures have been detected out of 42 structures drilled to the foundation object, of which 16 have been put into development. Figure 2.1. The diagram of the distribution of reserves in the basement of the Cuu Long basin [10] 2.1.2. The formation of fractured granite basement in the Cuu Long basin Refers to the basement rock as a general term used to refer to the rocks formation before establish reservoir and bottom of reservoir. The basement rock of the Cuu Long basin is rock formations, metamorphic rock, magmatic rock that intruded and erupted before the Cenozoic. In which, intrusive magmatic rock is a special reservoir rock object, called "fractured granitoid basement". This basement confined aquifer formed at least through three stages: Late Triassic (Hon Khoai Complex), Late Jurassic (Dinh Quan Complex) and Late Cretaceous (Ca Na Complex). HUYNH THANH TOAN 25 Bachelor Thesis - Late Triassic (Hon Khoai Complex) This is considered the oldest magmatic rock complex in the Cuu Long basin, with a late Triassic age (about 195 ÷ 250 million years). Hon Khoai granite consists mainly of amphybol - biotite - diorite, monzolite and adamelite. They are modified, strongly crushed and most of the fractured are filled with secondary minerals: calcite epidot - zeolite. The distribution area is mainly in the wings of the raised basement blocks such as the northeast wing of Bach Ho mine. - Late Jurassic (Dinh Quan Complex) The rock in this complex belongs to the lime-alkaline type, with a moderate acidic composition (SiO2 ranges from 63-67%). The formations have a high degree of variability and rupture. Most of the fissures are filled with secondary minerals: calcite, zeolite, quartz, and chlorite. The absolute age ranges from 130 to 150 million years, quite a lot in the structure of Bach Ho (Northern arch), Ba Vi, Tam Dao and Wolf. - Late Cretaceous (Ankroet Complex) Late Cretaceous – Early Paleocene (about 60 ÷ 95 million years old), developed and found most commonly in the whole tank. The lithological characteristics of the complex are hydro-mica granite and biotite, belonging to the sodium-potassium type with excess aluminum, silicon and low calcium. The granitoid blocks of this complex form cognac formations, are distributed along the axial direction of the basin and dissolve older rocks. They are strongly fractured with a stronger secondary change than the above two complexes, characterized by zeolite process, secondary quartz. HUYNH THANH TOAN 26 Bachelor Thesis Figure 2.2: Model of oil and gas trap for fractured basement of Cuu Long basin: (1) rock containing fractured basement; (2) Clay set D – unconfined and confined aquifer; (3) Rocks containing sandstone. [4] The petrographic composition of granitoid includes granite, monzonite, granodiorite, quartz diorite, monzodiorite, diorite, gabrodiorite... (Figure 2.3). Neutral Bazo - Neutral Bazo Neutral Acid - Acid Eruptive Shallow penetration Penetration Relative petrographic composition of the complexes Eruptive Cu Mong Complex Shallow penetration Penetration Phan Rang Complex Dinh Quan Complex Deo Ca Complex Ankroet Complex Figure 2.3: Classification of basement rocks according to geological and petrographic units [6] HUYNH THANH TOAN 27 Bachelor Thesis Due to the chemical composition and content of easily modified minerals as well as the mechanical properties and especially the degree of destruction at each location are very different, making the permeability and the petroleum product line of the company very different foundation rock complexes are very variable. The mining results show that the boreholes with the largest flow are usually located within the distribution of granite of the Ankroet Complex. Lesser products are granodiorite and quartz monzonite of the Dinh Quan Complex and the lowest are diorite and quartz monmodiorite of the Hon Khoai Complex. Intrusion activities, cleavage and rock shrinkage during hardening also have certain effects. The studies of Ospov M.A. (1974) showed that the hardening process due to gradual cooling of intrusive granite bodies reduces the rock volume and increases the viscosity of the molten substances. When cooled, the rocks first solidify at the top of the dome, under the influence of gravity, the molten substances separate and settle to create separate zones, cracks and low-density areas with diameter can reach several hundred meters. The volume reduction due to shrinkage of magma during crystallization generally fades with depth, but the system of caverns and cracks due to this process varies in space due to uneven deformation and cooling. The post-magmatic process with variable metamorphic activity such as chloritization, ceridification, etc., which occurs with the active participation of solutions of magma origin can also play some role in rock formation. contain nails. The porosity created by this process is initially negligible, however, they also partially make the rock more porous and create channels for the solution capable of transforming the basement rock. HUYNH THANH TOAN 28 Bachelor Thesis 2.2. Methodology of fractured granite basement modeling. 2.2.1. Types of fractured basement reservoir models Continuous fractured model The continuous fractured model is based on grid cells and does not take into account individual fractured. The inhomogeneity of the fractured properties is analyzed by dividing the object into small grid cells. In which each grid cell is assigned by property constants (porosity, permeability). In the single porosity model, the flow only passes through the joint fracture system and is not concerned with the bulk porosity. The porosity constants assigned to each grid cell are the average of the porosity of all fractured through that cell. In the two-porosity and two-permeability model, the bulk voids are considered and the bulk voids and fractured are considered as two overlapping media. The system of fractured and voids is described through the following three ideal models: block, layer, vertical column. A flow equation through these two media is constructed to evaluate the porosity and permeability values. Figure 2.4: Ideal fractured system in porosities and permeability model [6] HUYNH THANH TOAN 29 Bachelor Thesis Continuous fracture modeling uses simple geotechnical and mathematical equations to describe complex fracture systems. Therefore, this model is also not suitable partly because of idealization, partly because it ignores the properties of the important fractured system. Another limitation of the continuous fractured model is the approximation method. The properties assigned on the grid cell are averaged from the individual fractured. Because fractured system data are limited and depend on different resolutions of the data, this approximation is not suitable for a narrow range of resolutions. Discontinuous fractured model Discontinuous fractured models describe individual fractured properties and predict flow behavior from their shape to their degree of conductivity. This model can use deterministic simulation or statistical methods. - Definite discontinuous fractured model The distribution of fractured and their orientation is evaluated directly from the stress, strain, development history of the fractured and tectonic. However, the relationship between stress field and fractured growth is still unclear and remains to be studied. Even when the direction of fractured development is related to the stress, the actual observed results are only at the reference level. Often naturally fractured seams are subjected to various stresses at different stages. The current direction of fractured development may not be caused by the current stress field. - Random discontinuous fractured model The fractured characteristic values (direction, opening, density) are determined at the well. By statistical analysis on the data of these attributes, the distribution outside the well can be predicted. The fractured system is generated from random probability distributions, from which attributes are assigned to each individual fractured. This process will stop when the number of fractured, or fractured density, is satisfied. One advantage of the stochastic discontinuity model is that the fractured properties are not averaged, and the problem of resolution of the data is solved through HUYNH THANH TOAN 30 Bachelor Thesis probability distributions. This model is also capable of integrating information from many different sources and creating a detailed system for each fractured and their properties. However, because the distribution of fractured outside the well is risky, the confidence level of the models is limited to close to the well wall. In addition, the stochastic simulation method is based on the probability distributions of the fractured properties. This has two problems: one is that these distributions do not describe all cases, the other is that it is difficult to describe all probabilistic information. Fracture model combines stochastic and deterministic methods Describing natural fractures is a complex process and as argued above models with deterministic and stochastic simulation methods have not been successful. The combination of different modeling methods may offer more advantages. Combining mathematical, geomechanical equations with probability distributions provides a better tool for describing the complexity of the basement. The method can use many different data sources. This model depicts in two large and small scale. First, the geological structure of the reservoir is explained from seismic. This is a large-scale descriptive data source and only recognizes large, layered surface faults. At the bore well, the fracture systems were interpreted from core sample documents and well-wall images. Next, medium-scale cracks are constructed in two ways. A small number of fractures are created from the well to the surrounding areas in a definite direction (explained earlier). A large number of micro- fractures were also created at the wells to correct for mining as well as field documentation. In addition, another technique to describe the fractures system under two sets of small fractures at two different scales. First, all faults and fractures are created according to interpretation from seismic data at the whole seam scale. In the next step, the fracture properties are generated on a grid model based on well or field geophysical data. Another random simulation step to describe the position of fractures on the grid. The fractures model results have been combined from two sources with different scales. HUYNH THANH TOAN 31 Bachelor Thesis 2.2.2. NPV (Net Pore Volume) method This is a simple method that belongs to the continuous fractured model. Reservoir properties are calculated through mathematical equations. The porosity distribution is built through the relationship between porosity and Net To Gross (NTG) according to the depth of the reservoir. [15] Building relationship of porosity according to the depth of the reservoir This method assumes that the porosity decreases with the reservoir depth. Therefore, the relationship between porosity and depth is established as equation 2.1: y = a.xb (2.1) [15] Due to the heterogeneous character of the fractured basement reservoir, in order to minimize errors, the three main curves are established with the maximum, average and minimum values respectively. Where: y: distance from the top of the basement. x: porosity. a, b: coefficients depending on the change in depth and porosity values. Building the relationship of NTG according to the depth of the reservoir NTG is the ratio of effective thickness to reservoir thickness. When calculating NTG from drilled wells, the trio of curves representing the relationship between NTG and reservoir depth are also established as for porosity corresponding to the largest, average and smallest cases. Combined with the porosity distribution function in depth, it helps to calculate the effective storage volume, thereby serving the calculation of OIIP according to the NPV model. [15] The function used to illustrate the relationship between NTG and depth is a logarithmic function: HUYNH THANH TOAN 32 Bachelor Thesis y = c.ln(x) + d (2.2) [15] Where: y: depth from the top of the basement. x: ratio of effective thickness/total thickness of basement rock measured through each well. c, d: coefficient of variation with depth and NTG values. After setting up 3 curves representing the smallest, average and largest cases for the porosity distribution and NTG depth graph, based on these curves, there will be a function showing the values porosity and NTG values in depth, from this function we will convert to porosity and NTG functions to build 3D porosity and NTG models using specialized software. [14] Porosity (%) NTG (%) Minimum Minimum Average Average Maximum Maximum Figure 2.5: Relationship of porosity and NTG according to reservoir depth [14] HUYNH THANH TOAN 33 Bachelor Thesis Advantages This method is simple to calculate the parameters, the calculation time is shortened, and it is easy to apply to any reservoir. Generate preliminary results as a basis for other methods to match. Disadvantages Starting from the assumption that the porosity and NTG in all cases decrease with depth, leading to significant errors when building models by this method. In addition, this method does not consider the horizontal porosity distribution. 2.2.3. CFM (Continuous Fracture Modeling) method This method belongs to the continuous fractured model. [11] Purpose of using the CFM method: - Describe the geophysical data of the wells (densities of fractured, permeability, etc.) on the 3D model. - Calculate the characteristics of the oil in the reservoir (output, recovery coefficient...) to determine the optimal well trajectory. The process of building a model based on the CFM method includes the following basic steps: - Step 1: Classify all known parameters. Using neural network combined with fuzzy logic (fuzzy logic) for classification. - Step 2: Create a stochastic model using an artificial neural network, find out the relationship among the parameters. - Step 3: Build a model and analyze the uncertainty factors. Advantages Modeling results when using CFM methods depend on seismic data, structural models are built based on seismic interpretation documents and seismic properties. The petrographic, porosity, and other properties of the rock were calculated based on high- HUYNH THANH TOAN 34 Bachelor Thesis resolution properties and corrected for data obtained from wells and core samples. The results will therefore be more reliable than previous methods. Disadvantages The continuum method uses simple mathematical equations to reflect the complexity of the fractured system. With such characteristics, the results of the continuous model are often simplified and cannot reflect all the properties of the fractured system, especially the discontinuity properties such as position, orientation. grow, size. As a result, the models are only good for applications with high homogeneity, but for the study of simulating natural fractured seams, they are limited. Another limitation of the CFM method is the use of continuous approximation when building geological models, as well as when calculating properties for individual faults within the grid cells. Because the collection of data sources is very limited, local, and often highly variable, the simulated fracture network results are not representative of the entire reservoir. In the natural fractured reservoir model, it is necessary to integrate all available data sources to build a more complete model. [11] 2.2.4. DFN (Discrete Fracture Network) method Based on the map of the roof of the layer, the map of the thickness of the layers and the seismic sections, the architectural features are elucidated. This method belongs to the discontinuous fractured model. Many reservoirs are formed in the basement rock with very good fracture permeability. Therefore, in order to describe the characteristics of the reservoir, it is necessary to build a fracture model. DFN offers a different approach than other models, more practical and accurate. The results of the DFN method give a clear and quantitative view of the fractured development direction, geometrical properties such as size, length of discontinuous fractures. The major faults described on the DFN model are determined through seismic or well data in the model, which is very important. For small fractures, statistical distributions are used to determine the location and orientation of these fractures. The data that can be used for DFN are 2D/3D seismic, well geophysical data, core sample analysis results. The results HUYNH THANH TOAN 35 Bachelor Thesis of the DFN contain random components, random simulations can be performed on the distributions of each fractured system for any parameter such as distance, size, shape of the fractures, perform sensitivity analysis so that the parameters can be controlled. [9] The DFN model simulates the individual properties of the fractures. They can be deterministic or random models describing the architecture of fractures, constructing meshes from analysis of the lengths, heights, distances, orientations and openings of the fractures. Figure 2.6: Fractured model by DFN method [9] Discontinuous fractures can be connected by a variety of methods, the usual method for describing fractures is the two-porosity, two-permeability continuous model. In the model of the stone frame are blocks or slabs, and fractures are the continuous gaps in space and coincide with the surface of the stone frame. The properties of the stone frame are considered as symmetrical and continuous throughout the stone frame. Numerical models can hardly provide many features of the reservoir and therefore do not accurately reflect the flow path. The two-porosity model does not describe the discrete characterization, lying potential, or flow control of the crack. Inaccurate forecast results make it very difficult to control forecast parameters. [2] HUYNH THANH TOAN 36 Bachelor Thesis The size of the fractures is expressed through a probability distribution, and the fracture is seen as a disc-shaped plane with an area of one face equal to the surface area parameter of the fractures. Advantages - Integrate many different data sources. - Describes the connectivity between the channels very well. - Detailed and visual description of the characteristics of cracking. - The porosity and permeability of each individual crack is calculated directly, thus reducing the error due to averaging on the grid. - Easily describe many different fracture systems (with different properties and distributions). - Can directly change the crack properties (shape, orientation, position...) to calibrate the model accordingly. Disadvantages - The marginal grid cells will not reflect the parameters of cracking, especially connectivity. - When the mesh is thinned, the crack system with small size will be lost, leading to the result of the original model being changed much. - To build a model with a high level of detail takes a lot of computer memory and computation time. 2.2.5. The method of artificial neural network (ANN) An artificial neural network is an information processing model, studied and modeled after the nervous system of an organism, like the brain to process information. It consists of many elements (processors or neurons) connected to each other through associations (association weights). An ANN has the principle of operating like a biological nervous system, learning by experience, storing the learned experiences in HUYNH THANH TOAN 37 Bachelor Thesis the form of associative weights and using them in appropriate situations. [13] This is a method of solving problems in the way of "training the network" based on a limited set of data to predict areas where information is not available. By ANN method, it is necessary to build the relationship of porosity, permeability with porosity and permeability values calculated from well geophysics. It is necessary to set up the input data set for the network as geographical attributes. Seismic and output data are the predicted porosity and permeability values under the supervision of log line values at well values. The coefficient R is the correlation coefficient between the desired parameter (log value measured at the well) and the calculated parameter of the network used to evaluate the accuracy of the ANN network. The higher the R coefficient, the more reliable the network training results. After having trained an ANN from a combination of seismic properties that have a good relationship with the porosity and permeability log at the wells, this ANN is used to extrapolate the distribution of porosity and permeability from the well out across the reservoir. [13] 2.2.6. Halo method This is the current popular method in the construction of geological models of the basement. The Halo method is based on the fault system of the study area. The porosity is distributed around the fault system. Accordingly, the porosity at the fault surface is the largest because of the strongest destruction, the further away from the fault, the smaller the porosity. Distribution of porosity, permeability depends on 2 main factors: • Distance of monitoring point to fault systems. • Depth of monitoring point to basement surface. HUYNH THANH TOAN 38 Bachelor Thesis Figure 2.7: Fractured model by Halo method Figure 3.7 simulates the distribution of porosity vertically (depth) and horizontally (distance to fault). The results of tectonic interpretation, seismic interpretation and combined with well data are input data for this model. The seismic interpretation determines the foundation surface and the system of faults, on that basis, constructs a structural model. The next step distributes porosity and permeability in the horizontal and vertical directions, respectively. The results of porosity and permeability distribution depend a lot on the distance from the wells to the faults and on the permeability and porosity of the wells in the survey area. According to the Halo method, the porosity at the fault site is the largest because there is the greatest stress causing the strongest failure and these values tend to decrease when going away from the fault and with depth. In terms of construction principles, the NPV and Halo models are the same. However, the results of the Halo model are more convincing because of the investigation on each specific fault. Each fault in the reservoir will have its own characteristics, so the Halo model will show the dependence of porosity on geological HUYNH THANH TOAN 39 Bachelor Thesis conditions better than the NPV model. For NPV, the reservoir is considered to be homogeneous, this is only relative to the sedimentary bed because the sediment has better homogeneity than the foundation bed. In case the well has enough data to calculate, the porosity will be calculated, synthesize and bring to the same absolute depth and create porosity functions that tend to decrease with depth. In fact, whether it is a sedimentary or a basement reservoir, there is heterogeneity in the reservoir, not all cases of clearance decrease with depth, especially in areas with complex geological conditions, so when applied using both NPV and Halo models will give results with low reliability. However, with such errors, it is still applied to build geological models because the calculation process is simple and easy to apply, creating initial results for reference as well as having a direct view. It is more important about geological conditions, in-situ oil reserves of the reservoir and also to compare with other methods when the data source on geology as well as exploitation is not much. Starting from the assumptions and implementation of the method, the model should be built according to the Halo method with a low level of reliability: - According to the Halo method, the porosity/permeation decreases gradually from the basement surface downwards and from the fault systems exiting. However, in practice it is not possible to accurately describe these transformation laws, but only to establish them qualitatively. - The construction of horizontal and vertical porosity and permeability distribution equations depends heavily on the subjective judgment of the builder about parameters such as: maximum distance to fault, value of largest recorded voids, the number of faults explicable from seismic. The parameters of the observed maximum porosity and the maximum distance to the fault were changed to suit the reserves by NPV method. - Data integration and binding of input parameters such as seismic data, FMI analysis results... have not been clearly shown. In addition, the model results do not reflect the porosity anomalies because the input data to build the model have been averaged. HUYNH THANH TOAN 40 Bachelor Thesis - According to both models, the parameters for calculating OIIP are uncertain quantities, typically the averaged porosity quantity during construction. The NTG value in the NPV method is calculated based on the cutoff value, so this does not fully reflect the capacity of the foundation seam. The results of the method have many errors in the calculation process. 2.2.7. Advanced Halo method This method is an improvement from the traditional Halo method and is a continuous cracking model. Applying the same principle as the Halo method, i.e. assessing the change of porosity in depth from the foundation surface and horizontally away from the fault surface, however, the Advanced Halo model uses data Available data such as well geophysical data, petrographic data, seismic properties, geological, tectonic data, etc., to classify fault systems in the foundation into many types with different reservoir characteristics in order to be able to evaluate the degree of heterogeneity of the fracture porosity and the connection between them along the fault surface. Well The depth from TOB TOB confirm by Well fault plane and fractured network Figure 2.8: Effect of fault on porosity distribution HUYNH THANH TOAN 41 Bachelor Thesis The first task of the modified Halo method is to identify a fault system and classify it into different fault types according to specified criteria. Based on the classified system, calculate the porosity and permeability as follows: Relationship between porosity and distance to fault 𝑑 ɸℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 = 10−0.23𝐷 (2.3) Where: 𝜙𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 : porosity calculated in the horizontal direction. D: Maximum distance to fault. d: distance of the observed object to the fault. Horizontal porosity distribution function Porosity Horizontal porosity distribution function Porosity Porosity Distance to fault Distance to fault Distance to fault Figure 2.9: Horizontal porosity distribution HUYNH THANH TOAN 42 Bachelor Thesis Relationship between porosity and depth from the top of the basement The fault classification is marked with relevant fracture zones in the well. The porosity of that fracture zone is derived for each fault type. Run “bestfit” on the porosity data and get the min, mean and max values for each region. All those values are graphed relative to the depth (actual depth from the top of the basement). From the graph deduce the function of porosity by depth. Figure 2.10: Diagram of porosity distribution with depth The porosity function for depth is a natural logarithmic function, which has the following form: 𝜙𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 = 𝜙𝑀𝑎𝑥𝑖𝑚𝑢𝑚 . (1 − 𝑎 . ln(𝑇𝑂𝐵)) HUYNH THANH TOAN (2.4) 43 Bachelor Thesis Where: 𝜙𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 : Porosity of the rock in the vertical direction. 𝜙𝑀𝑎𝑥𝑖𝑚𝑢𝑚 : The largest recorded voidness. TOB: depth of top basement. a: coefficient. The formula for calculating porosity according to the Advanced Halo method: 𝜙𝑀𝑜𝑑𝑒𝑙 = 𝜙𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙 . 𝜙ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 . 𝜙𝑚𝑎𝑥𝑖𝑚𝑢𝑚 (2.5) Where 𝜙: porosity distributed according to Halo model. 𝜙𝑉𝑒𝑟𝑡𝑖𝑐𝑎𝑙 : porosity calculated from the well, distributed vertically. 𝜙ℎ𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 : porosity distribution according to the horizontal distance to the fault. 𝜙𝑀𝑎𝑥𝑖𝑚𝑢𝑚 : The largest porosity was recorded in the study area. The relationship of permeability and porosity The relationship of permeability and porosity is built according to the formula: 𝐾 = (𝐾𝑚𝑎𝑥𝑖𝑚𝑢𝑚 . 𝜙 𝜙𝑚𝑎𝑥𝑖𝑚𝑢𝑚 3 ) (2.6) Where: K: permeability distribution according to Halo model. KMaximum: The maximum permeability observed. Using the porosity and permeability transformation equations established in the above steps to build an attribute model with Petrel software, then evaluate the foundation model reserves, the results of the static model's reserves checked with the seam test data, and run the dynamic model. If the results are incorrect in a certain area, repeat the fault classification step in that area (faults not cut by the well). HUYNH THANH TOAN 44 Bachelor Thesis 2.3. The geological properties support for the NPV and Halo method 2.3.1. The factors of fractures and faults a) Causes of faults and fractures The process of destruction caused by tectonics: The tectonic activities have led to the formation of fault systems, fractures and crushed zones. In the Cuu Long basin, macerated rocks with porosity as large as 10% were found on core samples at relatively large depths compared to the foundation surface. The process of tectonic activity in Cuu Long basin can be summarized as follows: - The process of stretching and stretching creates forward faults, earthen trenches (subsidence); - The process of compression and rotation from the East to West wings creates an inverse fault (lifting and rotating), deepening and expanding the old fault system, and at the same time, many new accompanying faults develop in many dimensions; - The processes of horizontal sliding motion along the structure axis. The above movement processes have caused the foundation rock to be fractured, cracked, smashed and crushed with different intensities. Regarding the mechanism of creating cracks due to tectonic activities, when compressed, cracks perpendicular to the compression direction will be closed, and cracks in the same direction with the compression direction will be open. When stretched, cracks perpendicular to the direction of tension will be wider than cracks in the same direction. When sliding horizontally, the fault edge zone is strongly crumpled and destroyed, opening fractured or splitting (Figure 2.12). HUYNH THANH TOAN 45 Bachelor Thesis Figure 2.11: Mechanism for creating cracks under the action of compression, separation and horizontal sliding Normal fault Reverse fault Strike-slip fault Marked Basement K.N cut 1 K.N cut 2 K.N separated Figure 2.12: Mechanism of formation of fractured with faults In the Cuu Long basin, tectonic phases are active in many periods. The fractured created in the first phase are usually filled by the hydrothermal process, and there the HUYNH THANH TOAN 46 Bachelor Thesis hydrothermal exchange processes take place. As a result, the fractures are filled with secondary minerals whose main components are calcite, zeolite, and quartz. Later phases of tectonic activity can continue to widen existing fractures or create new ones. The old fractures are the most vulnerable because they contain brittle minerals that will be easily broken and crushed. As a result, large open spaces in the old fissure were formed and preserved in the absence of hydrothermal solution or filled with fine-grained sediments. In the intrusive magma, the original fractures are either filled with secondary minerals, or have a very small opening, so that the rock has almost no significant fractured voids. However, under the effect of tensile stress occurring in the later tectonic active phase, the fractures will reactivate to create effective voids. If this phase of tectonic activity occurs before or during the oil and gas movement, it will have significant oil and gas content. The petrographic and mineral composition of magmatic rocks The main mineral composition of the granitoid basement rock includes quartz and feldspar (plagioclase and potassium feldspar) in addition to colored minerals such as pyroxene, hornblende, biotite, muscovite and auxiliary minerals, ores. The percentage of minerals varies depending on the type of rock. Most of the magmatic rock-forming minerals react under the effect of hydrothermal solution after magma, even quartz is corroded and dissolved. This process forms caverns ranging in size from a few tens of micrometers to tens of millimeters. The hydrothermal solution in the process of moving along the fractures and faults can form minerals such as zeolite and calcite... which fill the void space. The petrographic composition also has a great influence on the density, nature and scale of development of fractured systems. Most of the effective fracture systems are of tectonic origin, forming together with fault systems, tectonic failure zones and areas highly influenced by tectonic stress fields. Although the foundation rock is always affected by faults and fractures, it is possible to see that open, well-connected fractures are more common in acid magmatic rocks (granite, granodiorite) than in neutral-basic HUYNH THANH TOAN 47 Bachelor Thesis rocks. (diorite, gabbro). This has to do with the brittle (high quartz content) characteristic of granite compared with the more ductile diorite (more feldspar components). Cooling and shrinking of the magma rock volume: Due to the intrusive magma in contact with the surrounding rock. Accompanying that process is volume shrinkage of magma forming primary fractures. When cooled, cracks and rifts appear more Granite Cracks, rifts, appear more in the contact zones between rocks and at the intersection of faults Granodiorite Quart Diorite Quart Figure 2.13: Model of fractures, cooling fractures and volume shrinkage of magmatic rock Thermodynamic activity The activities of the hydrothermal process are closely related to the tectonic activities and lead to changes in the composition as well as the hollow space structure in the basement rock of the Cuu Long Basin. In addition to being able to create secondary minerals such as zeolite, calcite, which fills or closes the fractures, hydrothermal solutions also dissolve the eroded driftwood to create cavities, especially at the intersections of big and small fractures. However, it is also suggested that hydrothermal activity not only does not widen the existing fractures, but on the contrary, they are also partially or completely filled. HUYNH THANH TOAN 48 Bachelor Thesis The direction of migration of the hydrothermal solution Caverns due to hydrothermal activity Figure 2.14: Thermodynamic activity model The process of weathering Occurs when the intrusive magma that has been formed, crystallized deep below under high pressure conditions, is raised to the surface, leading to a reduction in load due to erosion of the upper layer of rock and soil, pressure reduction at low pressure. Surface pressure conditions and weathering processes are important causes leading to fractures, weathering, causing rock to be destroyed, eroded, washed away, forming secondary porosity in the basement rock. According to drilling documents in some mines such as Bach Ho, Rang Dong, Hai Su Den... only about 20m from the foundation surface. In general, the weathering process does not significantly affect the secondary changes of the basement rock, but mainly reduces the mechanical strength of the rock and forms a permeable zone on the surface of the granitoid mass, which is not permeable. HUYNH THANH TOAN 49 Bachelor Thesis Sand Rainwater Clay Weathering zone Rock basement Groundwater goes up Weathered domain Figure 2.15: Weathering model b) The geological features of the fault Faults are fracture planes along which there is observable displacement of the wings caused by shear displacement parallel to the surface of the fault. Fault system is a system of faults located close to each other and related to each other. Figure 2.16: The geological features of the fault HUYNH THANH TOAN 50 Bachelor Thesis Line direction Slope direction line slope angle Slope direction Projected onto the horizontal plane East West Figure 2.17: Orientation of the fault elements on the horizontal plane - H-direction (Strike slip) is a horizontal line on the rock surface (indicating the direction of extension of the rock layer). - Slope direction A (Dip slip) is the factor indicating the inclined direction plugged into the ground of the rock layer. - The slope direction line A' is the line lying on the layer surface perpendicular to the direction line. - Dip angle is the angle between the rock surface and the horizontal plane. - The vertical displacement distance (Throw) is the vertical component of the slope angle (the projection of the slope direction onto the vertical plane). - The horizontal displacement distance (Heave) is the horizontal component of the slope angle (the projection of the slope direction onto the horizontal plane). For the inclined rock layer, measuring the lying potential of the rock layer is measuring the slope direction azimuth (β) and slope angle (α). Since the line azimuth is perpendicular to the slope direction, the line azimuth (with two values) will be equal to the slope azimuth plus/minus 90 degrees. HUYNH THANH TOAN 51 Bachelor Thesis The slope azimuth is the angle formed by the north and the clockwise direction of the slope. Main types of faults: Faults are classified based on the direction of movement of rocks on either side of the fault - Normal fault is a type of dip-slip fault where the hanging wall moves downwards from the footwall. The average dipping angle of a normal fault ranges from 45 to 90 degrees. Force direction Normal fault Figure 2.18: Normal fault model - The reverse fault is the result of compressive stress, one in which the block of rock on one side of a fault moves up and over the other side of rock. HUYNH THANH TOAN 52 Bachelor Thesis Reverse fault Figure 2.19: Reverse fault model - Strike - slip fault Strike-slip faults are vertical (or nearly vertical) fractures where the blocks have mostly moved horizontally. If the block opposite an observer looking across the fault moves to the right, the slip style is termed right lateral; if the block moves to the left, the motion is termed left lateral. Left lateral Right lateral Figure 2.20: Strike- slip fault model HUYNH THANH TOAN 53 Bachelor Thesis - Listric fractures Similar to the forward fault, but the fault surface is an upward basin, the slope decreases with depth, it can rotate and slide along the fault plane (Figure 2.21), or be pulled away from the main fault (Figure 2.22), is sliding away instead of moving along the slope of the fault plane under the action of stretching force. Figure 2.21: The suspension wing is swings and slides along the fault plane (slump) Figure 2.22: The suspension wing is pulled away from the cylinder under the action of the tension force HUYNH THANH TOAN 54 Bachelor Thesis 2.3.2. Fault classification basis a) Based on well data and classification criteria Gamma ray method Natural gamma (GR) method measures and records the natural gamma radiation intensity of the formation. Almost all types of rock formations emit gamma rays and the intensity depends on the levels of radioactive isotopes of potassium, thorium and uranium contained in the formation. The granodiorite rock groups usually have a lower GR value than the granite and monzogranite groups because they contain less potassium feldspar and are rich in colored minerals, clay minerals, and neutral buckwheat rocks with lower GR intensity. [12] Sonic log method Acoustic method in well geophysics is a method of measuring the propagation time of elastic waves, at sound frequencies in a soil and rock medium. Sound waves are emitted from the T-generator, propagate in the drilling fluid, in the rock, and return to the drilling fluid to reach the reciprocating earthquakes R. The propagation time depends on the petrographic composition and porosity of the foundation rock. Acoustic methods are mainly used to determine fracture porosity, total porosity, distinguish between open and closed fractures, and aid in seismic interpretation. [12] Types of sound waves can be mentioned as: - Compression wave or longitudinal wave P is a type of wave that causes the material points to move around the equilibrium position along the direction of wave propagation. P waves can travel through all solid, liquid and gaseous media. - Transverse wave (S-wave) causes the material points to vibrate in a direction perpendicular to the direction of wave propagation. S waves exist only in solid media and not in liquids and gases. HUYNH THANH TOAN 55 Bachelor Thesis - Pipe wave (Stonely Wave) propagates along the well wall in the condition that the well has solution (water), on the well wall there is a type of wave generated on the contact surface between the drilling fluid and the rock in the well wall. This wave propagation causes the well wall to deform. The speed of a Stonely wave is usually very low, even slower than a P wave traveling in solution. The measured values of P wave and S wave are often used in combination with other well geophysical parameters such as GR, Density... to determine the characteristics of the fractured basement rock. Density method The density method is a method of measuring the apparent mass density of the formation based on the scattering of gamma rays when interacting with the environment. Density method is mainly used to determine porosity in geomorphology, in addition, it is also used to determine the petrographic composition by measuring the photoelectric absorption index Pe of the rock layers. Density and petrographic methods are used to calculate porosity and identify rock layers in a slice according to their petrographic composition. This method proved effective in determining the presence of open fracture zones. Open fracture zones with good permeability, often filled with drilling fluids, often have low GR values and reduced density values. [12] The neutron method The neutron method is a porosity method that studies porous formations and determines their porosity on the basis of differences in hydrogen content in the formations. Thus, when the reservoir rock is a clean rock saturated with water and oil and gas in the pores, the neutron method will reflect the fluid-saturated pore portion. Gas zones can also be detected by comparing the results of this method with other porosity methods. In the basement rock, the open fracture zones are filled with fluids or contain secondary minerals with high hydrogen content, so the NPHI value is high. [12] HUYNH THANH TOAN 56 Bachelor Thesis Therefore, in the study of fracture, the neutron method combined with other log lines such as density, resistivity, natural radioactivity... can help locate the fracture zones. FMI method FMI (Fullbore Formation Microimager) is a method of measuring well images by providing micro-resistance images of formations in water-based drilling fluids. When measuring the resistance or conductivity along the well wall, at the cavernous fractures of the layers, there is a change in resistance compared to the adjacent rock layers that are not cracked. When the borehole cuts cavernous fractures, their traces will be left on the well wall in a circular shape (if the plane is perpendicular to the well axis) or elliptical (if the fracture surface is not perpendicular to the well axis). When opening the cylindrical expansion of the 3D well into 2D, straight or sinusoidal lines will be obtained, depending on the angle formed by the well axis and the cavernous fracture or layered surfaces. Crack in the well Cracking on the performance surface Layered face Layered cracked face Figure 2.23: Wells image Based on resistance measurement by a set of oriented point microelectrodes. Acquire an array of microresistors measured from 192 electrodes on eight pads located HUYNH THANH TOAN 57 Bachelor Thesis on four orthogonal arms of the caliper. The position and spacing of the 8 pads covered 80% of a well with a diameter of 8 inches and a resolution of 5mm. [13] Figure 2.24: FMI measuring tool After processing with a specialized software, we will observe fractures, cavities with low resistance compared to the surrounding un-fractures rock. The final product is the image of the well wall shown in colors: - The white color represents the irregular changing area, impermeable soil and rock; - Yellow shows high resistance areas; - Brown and black colors represent areas of lower resistance (usually caverns, large cracks). At the same time, the calculation results also show the density, opening, and distribution direction of fractured in the rock. The advantage of the FMI method is that it can give a very sharp image of the well wall. The disadvantage of FMI is not to distinguish between open fractures and fractures filled with secondary minerals or low-resistance veins. HUYNH THANH TOAN 58 Bachelor Thesis PLT (Production logging test) method The term PLT refers to a system that includes a long array of sensors along with modern measurement and interpretation techniques. Operators use PLT to estimate the rate of fluid movement in and out of the well, and the properties of the fluid under well conditions. Well completion engineers can estimate open-pit, exploit and plan for future failure prevention or repair based on PLT interpretations. The reservoir engineer and drilling engineer can use these log lines to manage and optimize condition in the reservoir and well bore. PTL was used in the 30s of the twentieth century, with the original purpose of measuring well temperature, over the decades, many other measurement techniques have been added such as pressure, flow rate, density fluid and holdup (the volume of the tube occupied by the fluid). Normal fluid speed is measured with a rotating flowmeter, a propeller that rotates as the fluid moves through it. Under ideal conditions, the number of revolutions per second (RPS) of the impeller is proportional to the flow rate. Friction in the impeller support and effects from fluid viscosity result in a non-linear velocity response, requiring correction of the measurement. The correction is performed by increasing or decreasing the log measurement rate. Before calculating the absolute speed of the fluid, adjust the rotator speed to the relative speed of the tool. Because of the friction in the pipe wall, the absolute speed of the fluid is not the same as the average speed of the fluid moving through the tube. After correcting for those factors, the engineer uses computer modeling techniques to convert the rotor speed to the average fluid speed. [13] HUYNH THANH TOAN 59 Bachelor Thesis Figure 2.25: PLT measuring tool PLT's measuring tools include rotary flowmeter, fluid holdup, air bubble sensor, pressure gauge, temperature sensor, gammaray gauge, sleeve locator, line gauge lens, centering, and relative azimuth sensors. Figure 2.26: Log lines of PLT HUYNH THANH TOAN 60 Bachelor Thesis *Classification criteria Table 2.1: Criteria for classifying faults cut by wells Fault type Mud log Oil Show Logs 1 2 3 4 Lost all drilling fluid Lost drilling fluid YES Very good porosity Granite biotite Primary contact Granite biotite horblende Secondary contact YES 1 NO Petrology Border line PLT Other information 2 Maybe, Undefined Lost drilling fluid YES Good porosity 3 NO NO YES Porosity Mafic rock Along the circuit 4 NO NO NO/ Undefined No porosity Felsic rock Along the circuit b) Seismic data and classification criteria Variable function property (Variance) The transform property (in contrast to the homogeneity property), which represents the change in the shape of the seismic pulse in space, helps to determine the change in the horizontal acoustic impedance. Similar wave pulses have a low coefficient of variation, and areas with discontinuous geological properties have a high coefficient of variation. HUYNH THANH TOAN 61 Bachelor Thesis 2 t L 2 L j t 2 j t w j t i 1 xij x j L 2 L j t 2 j t L w j t i 1 xij2 L 2 [9] (2.7) where: σ: Variation (0≤ σ ≤ 1). xij: The value of the sample at position i (horizontal axis) and j (vertical-time axis). wj-t: Coefficient. L: The size of the window. Application: The transform property is commonly used in the identification of faults in the basement surface. However, seismic data inside the foundation often have a low signal-to-noise ratio, so the use of this attribute to distinguish faults and associated fractures is inaccurate and unreliable. RMS Amplitude property This property is calculated as the mean root of the sum of the squares of the amplitudes. RMS Amplitude = 1 N [9] i1 ai2 n (2.8) where: ai: Amplitude of test sample i N: Number of test pieces in the calculation window Application: Provides information about the energy of seismic and seismic wave pulses. This property is very sensitive and often highlights large amplitude values. Therefore, this attribute is often used to identify hydrocarbons or geological features such as lava vessels (dike, intrusive bodies). This attribute can localize the locations of HUYNH THANH TOAN 62 Bachelor Thesis fracture zones and indicate the characteristics of these zones. However, this property does not allow to distinguish the fracture zones from those with greater acoustical stiffness than granite and is not able to limit the multiple reflections in the basement. [9] Ant - Tracking (Edge enhancement technique by swarm intelligent) property Ant - Tracking is a seismic property that is introduced to clarify fault zones and fractures from the original signal source. The Ant - Tracking property can automatically detect signals that interrupt the continuity of geological strata. In addition, based on information on tectonics as well as regional geology, understanding as well as predictions about the characteristics of the fault/fissure system can provide information about the slope direction, slope angle of the cleft zone cracked as input data. From there, Ant - Tracking will show the fracture zone more clearly. [9] RAI property This is the property that allows the summation of amplitude values with uniform sampling, calculated by integrating seismic routes. The comparison results between the RAI property and the seismic amplitude, show that the RAI has a higher vertical resolution than the seismic amplitude. At the same time, energy and continuity are also higher. This seismic attribute is therefore used as a primary seismic document to generate other seismic properties. [9] Curvature (Degree of deviation from a plane by concavity or convexity) property This property is often used to assist in the identification of fault zones and seismic fractures. After having the results of running these seismic attributes and longitudinally cutting the seismic properties along the wellbore into a virtual log (pseudo - log) carrying the values of the seismic attributes. These seismic property logs will be compared with the results of FMI analysis, i.e. compared with the borehole longitudinal fracture density curve. This comparison helps to find the properties with HUYNH THANH TOAN 63 Bachelor Thesis the best coefficients associated with the fracture density curve at the well. [15] Instantaneous Frequency The time derivative of the wave phase w = d(phase)/dt and the time derivative of the instantaneous frequency is called 'Phase Acceleration' and is calculated from the rate of change with time of the instantaneous phase. Which instantaneous frequency is used to evaluate the damping of seismic waves. Oil and gas reservoirs often cause a drop in high frequency components. This seismic property can also help to determine the period of geological intervals and can be useful for cross-correlation across faults. In addition, this parameter can also be used to define the gas - water or gas - oil boundary. The instantaneous frequency tends to be unstable in the presence of noise and is sometimes difficult to interpret. [15] Dip Deviation (Deviation in orientation of field) Like Variance, the Dip Deviation property is also an edge detection attribute, to determine the change in slope angle value on the seismic signal. *Classification criteria Faults are divided into 3 types based on seismic property anomalies. - Type 1: Anomalies are observed very clearly and well. - Type 2: Well observed anomaly. - Type 3: Translucent anomaly. c) Based on the geological characteristics of the tectonic phase The tectonic phases that have a strong influence on the fractured basement reservoir of X field are the compression phase after the E-set and the epoch-set sedimentation process in set BI.1 HUYNH THANH TOAN 64 Bachelor Thesis Figure 2.27: The tectonic phase from the Jurassic to the present of the Cuu Long basin [15] The opening of the associated fractures or fault zones depends on the fault length, vertical displacement, fault type, and the number of tectonic phases that the fault undergoes. The interpreted faults are grouped based on their geometric properties (line, azimuth, and slope) vertical displacement in different phases and fault type. HUYNH THANH TOAN 65 Bachelor Thesis For classification based on tectonic fields, focus on testing whether the fault is open (opened), or sheared, or opened with filled circuit. Steps to be taken to characterize and classify faults for a 3D static model: Step 1: Check and validate the prior interpretation and study the fault/fracture properties in detail. Step 2: Measure/characterize fault geometry, vertical displacement in different phases and fault type. Step 3: Refine fault characteristics with FMI data Step 4: Faults cut by the well are classified into 4 types as mentioned. The properties after passing step 3 will be used as "correction points" for similar faults that are not sheared by the well. HUYNH THANH TOAN 66 Bachelor Thesis CHAPTER 3. BUILDING GEOLOGICAL MODEL FOR FRACTURED BASEMENT RESERVOIR BY NPV AND HALO METHOD IN FIELD X, BLOCK Y, CUU LONG BASIN 3.1. Purpose and performance The main purpose of the 3D geological model is to build the distribution of reservoir parameter for calculating the OIIP and for further simulation study. From common understanding, porosity and permeability of basement reservoir is strongly dependent on the density of fractures, distribution, fracture apertures, factors of tectonic systems, hydrothermal processes and weathering. So in order to logically model the basement reservoir, it is important to divide the basement reservoir into several structural Blocks. Structural Blocks are defined in the basement as zones containing similar structural. The boundaries of structural Blocks are typically large tectonic features that have structural relief and evidence of multiple phases of fault movement.Each Block typically contains multiple sets of faults/fractures, trends and depth to top Basement. Segment is defined in the structural Block based on intensity of faults/fractures, fault types and their dominant trends. Segment’s boundaries are arbitrary. In the case of the X field, Blocks are bounded by a set of NW-SE faults dipping to SW. Each Block-bounding faultis a potential site for cataclastic gouge formation. It can provide a potential barrier in the reservoir. Sealing capacity along a fault may vary with depth or along strike. Four Segments are defined in the X field, Block A. They have arbitrary boundaries and not actual fault/fracture set boundaries. X field was divided into 5 structural Blocks C, B, A, D and E (Figure 3.1). The Block A was subdivided into 4 segments. Segments were defined from seismic fracture characteristics (intensity, type, and trend). They are Segments A: Green, Red, Yellow and Blue (Figure 3.2). HUYNH THANH TOAN 67 Bachelor Thesis Block C Block B Block A Block D Block E Figure 3.1: Basement Structural Blocks of X field HUYNH THANH TOAN 68 Bachelor Thesis Figure 3.2: Segments in the Structural Block A of X field Figure 3.3: Segments in the Structural Block A of the X field at slice 4200m with in terpreted Faults Fractured Basement Reservoirs provide a unique challenge with respect to reservoir modelling. These fractured reservoirs contain porosity and permeability systems that are dependent on tectonic activities and are thus segregated into HUYNH THANH TOAN 69 Bachelor Thesis structural Block. Hence, two types of modelling approaches are used to describe the reservoir. These are the Net Pore Volume Model (NPV) and the Halo Fault Model. The Net Pore Volume Model is used to generate OIIP volumes in a structural Block based model using porosity and net/gross to describe the rock volume in a probabilistic method. The Halo Fault Model is used to characterize the reservoir with a fracture enhanced halo around lineaments described by seismic in the reservoir. A fractured halo fault is applied to the lineaments and porosity and permeability volume is produced. This is used to simulate fluid flow modelling. Both models are matched against well test and historical data and or dynamic data to ensure quality and matched against conventional volumetricapproach to ensure the total net volume. This thesis will deal with two types of these models. 3.2. Workflow for 3D Geological Modelling The workflow of 3D Basement Reservoir modelling for X field is shown in figure 3.4. HUYNH THANH TOAN 70 Bachelor Thesis Figure 3.4: 3D Reservoir Modelling Workflow 3.2.1. Structural Modelling Considering the scale of the geological objects to be realized and the runtime for reservoir simulation due to computing speed, the grid configuration of X Basement Geological model (3D Static Model) was optimized for the number of cells with regardsto preservation of as much as possible the reservoir heterogeneities. The 3D Grid of theX Basement Model was rotated at 45 degrees according to the NE – SW direction asthe main orientation of X structural development. Fault Modelling & Pillar Gridding As mentioned above, the X structure is divided into 2 Blocks and 4 segments in Block A. These Blocks and Segments boundaries are defined by faults and bounded. In this stage of study, there are just 19 fault sticks that were used in the HUYNH THANH TOAN 71 Bachelor Thesis process of fault modelling and of structural Block/Segment subdivision. The other fault sticks (in segments) are used in property modelling for segment porosity distribution. Pillar Gridding is a process of building 3D grid of static model. The horizontal grid size of X 3D geological model is 50m x 50m. This size is an average value and has been used effectively and widely in 3D reservoir modelling of neighboring basementreservoirs such as the Su Tu complex, Ruby, etc. The average value of the vertical grid size is 20 m. Make Horizons The Make Horizons process is the first step in defining the vertical layering of the 3D grid in Petrel. Both the top surface of X basement and the model base depth surface of 5000m are used in Make Horizons. The reason to set the base of model deeper than POWC is to have enough space for aquifer support in reservoir simulation. The Top and Base horizons that were generated in the Make Horizon process were correlated precisely with well markers. These horizons together with the fault model were then used to construct the 3D structural framework. Layering Layering is the final step in defining the 3D grid of the X model. The thickness of thebasement section was divided into 70 layers using the top down method. The average thickness of each layer is 20m. This value was considered to optimise the total numberof cells but thin enough to preserve the vertical heterogeneities. It is smaller as compared to other basement models (usually 50m). 3.2.2. Property Modelling Porosity modelling This is a process of filling the cells of the grid with the porosity distribution (called property population). The fractured granite reservoir is understood to contain significant heterogeneities. The variations of fracture intensity require that the HUYNH THANH TOAN 72 Bachelor Thesis reservoir volume be characterized as a composite of well and seismic information following the Conceptual Basement Fracture Model. This conceptual model of X should reflect the fracture distribution in the weathered zone of the upper part of the basement and faultassociated fracture system in the both upper and lower parts as well as below the Structural Spill Point at 4620m TVDSS (Blocks A, B). The final porosity model, as mentioned above, is the result of the combination of two porosity models. They are the Net Pore Volume model and the Halo Faults model. The Net Pore Volume model is built mainly using well information. Volumetricof this model are used to compare with such values from the Hallo Fault Model. The Halo Faults model is built using results from the tectonic/seismic interpretation in combination with well data and its pore volume in principle should be in line with that ofthe Net Pore Volume model. Property distributions in the Halo Fault model are more detailed and realistic in representing fracture basement characteristics and be used forupscaling and reservoir simulation. Model Validation Theoretically, there are two QC methods for model validation and ranking these include static and dynamic. These tools are not only applied for QC model but also for qualifying the upscaling process. Data and model validations were carried out at everymajor modelling step. First of all, as soon as the data has been imported into Petrel, they should be under strict quality control. Typical ways of data QC are to display them in parallel with checking statistics, histogram, etc. In addition, the general intersection was also used to view the data in cross section. This is useful for QC’ing the structural framework and the property model. Another important step for model QC includes checking for crossing pillars to make sure no negative cell volumes have been generated. Depth synthetic data was used to compare against rawdata in order to ensure no depth mismatch. HUYNH THANH TOAN 73 Bachelor Thesis 3.2.3. Net Pore Volume Model (NPV) Where multi-well penetrations are available the data can be summarized and compiled into a consistent set of depth dependent functions. The NPV model uses gross rock volume (GRV) that is generated from geologic/geophysical data and porosity estimated from resistivity-based method plus NTG which is generated from a combination of mud log, lithological and petro-physical information. Property distribution of a structural Blocks/ Segments are populated by a composite set of functions from individual wells (or just awell) relating to Maximum, Most likely, and Minimum levels respectively. Significant changes may occur between basement structural Blocks and Segments (Segments within Block A), that are likely the result of different local tectonic. A segment or block without well data will have a porosity characterization assigned to it from a neighboring known Segment/Block (where well data is available). Net/Gross distribution is also built using the same approach in a similar fashion but relies more on structural genesis that may not be from the same analogue Segment/Block. In summary, net pore volume of areas where no well data is available are a function of analogue segments which have similar structural genesis and internal seismic characteristics. 3.2.3.1. Porosity versus Depth Estimation and Justification: a. Methodology Description In the X field, basement porosity of each well is calculated from resistivity logs. In order to use the results of porosity interpretation for further 3D reservoir modelling, the relationships of porosity vs. depth are required to be established for each well, Segment and Block. Due to the heterogeneity of basement rock, resulting from fracture distribution, the involvement ofsecondary materials, intrusives, etc., the variation of porosity with depth is not always decreasing. In order to capture this variation and to reduce uncertainties, curves of porosity versus depth were built with careful adjustments. Due to the large range of porosity variation, theoretical HUYNH THANH TOAN 74 Bachelor Thesis curves should be generated as much as possible.However, at this stage of analysis, just three key curves which correspond to Minimum, Most Likely and Maximum cases are established. These relationships are likely exponential functions as indicated in the Formula below: 𝐷𝑒𝑝𝑡ℎ = 𝑎 ∗ 𝑒 𝑏∗𝑃𝑜𝑟𝑜 (3.1) Where: Depth: distance from top of basement Poro: porosity a, b: constant factors Workflow of establishing Porosity vs. Depth is conducted by the following steps: To establish curves of Porosity versus Depth with careful review of porosity variations of each well. To establish curves of Porosity versus Depth for a group of wells which are penetrating the same Segment (Segment application) To establish curves of Porosity versus Depth for regional basement including Ruby, Bach Ho, Su Tu Den, Su Tu Vang and Rang Dong areas. This is used forcomparison. b. Porosity - Depth Relationship for Well Results of porosity interpretation of 5 wells X-1X, X-2X, X-2XST and X-3X and analogue data such as Y-6PST, and other regional data are used to plot the porosity against depth on the scatter plot that has the same scale on both the y and x axis. The X-3X porosity is only used for reference due to the lack of wireline data available in this well (only LWD data acquired). Following careful review, optimized representative curves of Minimum, most likely and Maximum Porosity were generated. Some abnormal values of porosity have been observed. These values may relate to granitic dikes or fault intersections. To account for them HUYNH THANH TOAN 75 Bachelor Thesis correctly, it may need further specific study and examination of additional data such as FMI/ DSI, core and etc. At this stage of the analysis, it is assumed that when the depth from top of basement increases, the porosity will decrease. Three curves (Minimum, Most likely and Maximum curves) of each well were generated and are presented in Figures 3.5 to 3.9. The relevant functions are summarized in Table 3.2. POROSITY – DEPTH RELATIONSHIP (X – 1X) X – 1X Max Most Likely Min Figure 3.5: Basement Porosity vs. Depth of X-1X well HUYNH THANH TOAN 76 Bachelor Thesis POROSITY – DEPTH RELATIONSHIP (X – 2X) X – 2X Max Most Likely Min Figure 3.6: Basement Porosity vs. Depth of X-2X well HUYNH THANH TOAN 77 Bachelor Thesis POROSITY – DEPTH RELATIONSHIP (X – 2XST) X – 2XST Max Most Likely Min Figure 3.7: Basement Porosity vs. Depth of X-2XST well HUYNH THANH TOAN 78 Bachelor Thesis POROSITY – DEPTH RELATIONSHIP (X – 3X) X – 3X Max Most Likely Min Figure 3.8: Basement Porosity vs. Depth of X-3X well HUYNH THANH TOAN 79 Bachelor Thesis POROSITY – DEPTH RELATIONSHIP (X – 1X & X – 2X & SD - 6PST) X – 1X X – 2X Figure 3.9: Basement Porosity vs. Depth of X-1X, X-3X & SD-6PST wells Table 3.2.: Functions of Basement Porosity Vs Depth for all Wells in X field Well Case Functions of Porosity Vs Depth from Top Basement Min Depth = 5436.6e-263.16Poro Most Likely Depth = 93198e-198.02Poro Max Depth = 517341e-158.73Poro X-1X HUYNH THANH TOAN 80 Bachelor Thesis Min Depth = 156454e-400Poro Most Likely Depth = 357010e-250Poro Max Depth= 342440e-166.67Poro Min Depth = 3E+07e-1428.6Poro Most Likely Depth = 2E+06e-833.33Poro Max Depth = 268337e-500Poro Min Depth = 4797.8e-250Poro Most Likely Depth = 4234e Max Depth = 4027.5e-100Poro X-2X X-2XST -142.86Poro X-3X Porosity-Depth Relationship and application for Basement Reservoir Basement Porosity with Depth curves generated for each and for combined Wells are applied to each basement block in the X field and are summarized in the Table below: Table 3.3: Porosity – Depth relationship applied for X Segments and Blocks Block/ Well Segment A_Blue Case Functions of Porosity vs. Depth from Top Basement Min Depth = 156454e-400Poro Most Likely Depth = 357010e-250Poro Max Depth = 342440e-166.67Poro X-2X HUYNH THANH TOAN 81 Bachelor Thesis Block B Min Depth = 3E+07e-1428.6Poro Most Likely Depth = 2E+06e-833.33Poro Max Depth = 268337e-500Poro Min Depth = 109196e-666.67Poro Most Likely Depth = 1629024e-200Poro Max Depth = 164830e-147.06Poro X-2XST A_Red A_Green X-1X +SD- A_Yellow 6PST c. Porosity-Depth Relationship for Regional Basement Results of porosity interpretation for X field basement are plotted together with porosity of other Basement Fields such as of Bach Ho, Su Tu Den, Su Tu Vang, Rang Dong and Ruby for comparison (Figure 3.10). As shown by this figure, the porosity of X Basement reservoir is greater. This is reasonable based on the recent drilling and testing information of X-3X, the longest basement penetration in the Field. HUYNH THANH TOAN 82 Bachelor Thesis X – 1X X – 2X X – 2XST X – 3X Figure 3.10: Porosity vs. Depth in the Basement of Cuu Long Basin 3.2.3.2. NTG versus Depth Estimation and Justification a. Methodology Description Based on current understanding, the Basement porosity is mainly generated by fractures, and in turn fractures tend to develop more in shallower and crestal parts of Basement structures and are less developed in deeper parts. Because of this, the Net to Gross should have the same tendency as fracture distribution (or fracture porosity).The calculated Net to Gross of Rang Dong, Ruby, Su Tu Den and Bach HUYNH THANH TOAN 83 Bachelor Thesis Ho Fields demonstrates this observation. An assumption is made prior to NTG estimation that when fractures are plugged by clays, diagenetic minerals or not connected to each other, they are noneffective. In addition, Total Gas logs (TG) that respond to porosity are a function of connected fracture systems those are hydrocarbon bearing. However, Total Gas is affected significantly by some factors that make the interpretation of total gas data more complicated. These factors are as follows: Non-connected fracture system bearing hydrocarbons. These isolated features may be broken out and opened by drilling penetration. As a result, fractures become connected and may cause the total gas increase. Gravity of drilling mud. The high gravity will flush the hydrocarbons into the reservoir, thus causing the total gas to decrease. Mud loss during drilling. In addition, total gas may be also being affected by some other factors such assurface equipment, borehole geometry and mud chemistry. Due to the above factors, the Background Gas and Peak Gas should be estimated and generated for each well at relevant depth intervals to cover variations as shown in the Figure 3.11. Then, NTG can be estimated using the Background Gas and Peak Gas.First of all, the cut-off line of TG is estimated and generated using (A) formula. Any interval having TG greater than cut-off value and corresponding with good porosity or hydrocarbon tested intervals are considered as Net Thickness. These values are estimated for every 40m of total Basement section of all the wells in X field. As soon as NTG has been estimated, similar to porosity, three curves of Net to Gross versus Depth are generated with adjustments. They represent the Minimum, Most likely and Maximum cases. The function that demonstrates the relationship of Netto Gross and Depth from Top Basement is a logarithm function as shown in the formula (B): HUYNH THANH TOAN 84 Bachelor Thesis 𝑇𝐺𝐶𝑢𝑡−𝑜𝑓𝑓 − 𝑇𝐺𝐵𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑 𝑇𝐺𝑃𝑒𝑎𝑘 − 𝑇𝐺𝐵𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑 = 1 3 𝐷𝑒𝑝𝑡ℎ = 𝑐 ∗ 𝐿𝑛 (𝑁𝑇𝐺 ) + 𝑑 (A) (B) Where: TGBackground: Background gas TGPeak: Peak gas TGCut – off: Cut – off gas Depth: Depth from top of basement NTG: Net to Gross c, d: Constant factor HUYNH THANH TOAN 85 Bachelor Thesis Figure 3.11: Establishing the Background, Peak and Cut-off Gas values b. Net to Gross – Depth Relationship of each well As mentioned earlier, Net to Gross values of X Basement were estimated for every 40m of basement section for all wells in X Field. Depending on each well, either deep or shallow penetration, variations of Net to Gross with Depth are demonstrated by three curves that represent the Minimum, Most likely and Maximum cases. Results areillustrated in Figures from 3.12 to 3.16 and Table 3.4. HUYNH THANH TOAN 86 Bachelor Thesis NTG – DEPTH RELATIONSHIP (X – 1X) X – 1X Figure 3.12: Basement NTG vs. Depth of X-1X HUYNH THANH TOAN 87 Bachelor Thesis NTG – DEPTH RELATIONSHIP (X – 2X) X – 2X Figure 3.13: Basement NTG vs. Depth of X-2X HUYNH THANH TOAN 88 Bachelor Thesis NTG – DEPTH RELATIONSHIP (X – 2XST) N 2 G _X – 2XST Figure 3.14: Basement NTG vs. Depth of X-2XST HUYNH THANH TOAN 89 Bachelor Thesis NTG – DEPTH RELATIONSHIP (X – 3X) N T G _X – 3X Figure 3.15: Basement NTG vs. Depth of X-3X HUYNH THANH TOAN 90 Bachelor Thesis NTG – DEPTH RELATIONSHIP (X – 1X + X – 3X) N 2 G _X – 3X Figure 3.16: Basement NTG vs. Depth of X-1X and X-3X HUYNH THANH TOAN 91 Bachelor Thesis Table 3.4: Summary of NTG - Depth relationship of all wells in X Well Case Functions of NTG vs Depth from Top Basement Min Depth = -908Ln(NTG) + 3465.4 Most Likely Depth = -989Ln(NTG) + 3980.2 Max Depth = -1070Ln(NTG) + 4495 Min Depth = -1020Ln(NTG) + 3600 Most Likely Depth = -1005Ln(NTG) + 3900 Max Depth = -1035Ln(NTG) + 4200 Min Depth = -1080Ln(NTG) + 3000 Most Likely Depth = -1080Ln(NTG) + 3500 Max Depth = -1150Ln(NTG) + 4000 Min Depth = -905Ln(NTG) + 3415 Most Likely Depth = -837.5Ln(NTG) + 3307.5 Max Depth = -770Ln(NTG) + 3200 X-1X X-2X X-2XST X-3X c. Net to Gross-Depth Relationship and Application for Basement Reservoir Basically, the relationship of Net to Gross versus Depth for each Block/Segment is constructed from NTG of each well. Similar to porosity, Net to Gross distribution curves demonstrate a trend that decreases with Depth. For Blocks/Segments without well data, the relationship of Net to Gross applied to them are dependent on their similarity of fracture characteristics (density of fracture, HC HUYNH THANH TOAN 92 Bachelor Thesis potential, etc) to the nearby Block/ Segment. Table 3.5: NTG – Depth relationship for X Basement Segments and Blocks Segment / Block A_Blue Well Case The functions of NTG vs. Depth Min Depth = -1020Ln(NTG) + 3600 Most Likely Depth = -1005Ln(NTG) + 3900 Max Depth = -1035Ln(NTG) + 4200 Min Depth = -1080Ln(NTG) + 3000 Most Likely Depth = -1080Ln(NTG) + 3500 Max Depth = -1150Ln(NTG) + 4000 Min Depth = -1000Ln(NTG) + 3296 Most Likely Depth = -1070Ln(NTG) + 4000 Max Depth = -1105Ln(NTG) + 4500 X-2X Block B X-2XST A_Red A_Green A_Yellow X-1X + X-3X + SD-6PST d. Net to Gross – Depth Relationship for Regional Basement NTG values of basement wells in X field and some other basement fields such as Ruby, Su Tu Vang, Su Tu Den, Su Tu Chua and Bach Ho are used together to generate a regional variation of NTG vs. Depth. It is clearly seen that the NTG of X field is within the range of regional basement. The regional basement NTG vs. Depth is shown in Figure 3.17. HUYNH THANH TOAN 93 Bachelor Thesis X – 1X X – 2X X – 2XST X – 3X Figure 3.17: Basement NTG vs. Depth in the Cuu Long Basin 3.2.3.3. Results of NPV Modelling The Net Pore Volume model uses gross rock volume (GVR) generated from geologic and geophysical data. Applied against this volume is gross porosity from resistivity porosity transforms and net/gross ratios generated from a combination of mudgas, lithologic and petrophysical information. Properties within a structural segment are represented by a composite set of functions from individual wells. The reservoir is characterized with respect to gross porosity and net/gross function at maximum, mean,and minimum levels. These are represented in the reservoir as a lognormal function that decreases with depth and is consistent with the local field data as well as analogues. HUYNH THANH TOAN 94 Bachelor Thesis Table 3.6: NTG and Porosity Functions Applied to each X field basement Segments and Blocks MAX Block/ Characterization Segment Wells A_Blue Max functions of porosity and net/gross 2X A_Red Max functions of porosity and net/gross 1X, 3X, Y6P A_Yellow Max functions of porosity and net/gross 1X, 3X, Y6P A_Green Max functions of porosity and net/gross 1X, 3X, Y6P Block B Max functions of porosity and net/gross 2XST MEAN Block/ Characterization Segment Wells A_Blue Mean functions of porosity and net/gross 2X A_Red Mean functions of porosity and net/gross 1X, 3X, Y6P A_Yellow Mean functions of porosity and net/gross 1X, 3X, Y6P A_Green Mean functions of porosity and net/gross 1X, 3X, Y6P Block B Mean functions of porosity and net/gross 2XST HUYNH THANH TOAN 95 Bachelor Thesis MIN Block/ Characterization Segment Wells A_Blue Min functions of porosity and net/gross 2X A_Red Min functions of porosity and net/gross 1X, 3X, Y6P A_Yellow Min functions of porosity and net/gross 1X, 3X, Y6P A_Green Min functions of porosity and net/gross 1X, 3X, Y6P Block B Min functions of porosity and net/gross 2XST MODELS RESULT Figure 3.18: Basement Porosity Model for X Field HUYNH THANH TOAN 96 Bachelor Thesis Figure 3.19: Basement NTG Model for X Field Figure 3.20: Porosity and NTG cross sections through X-1X well (Most likely case), X Field HUYNH THANH TOAN 97 Bachelor Thesis Figure 3.21: Porosity and NTG strike sections (Most likely case), X Field 3.2.4. Halo Fault Model The Halo Fault Model is developing of the basement modelling method to describe better the reality of basement geology. In this model, porosity is distributed along fault systems as a result of fault associated fracture (or porosity). Porosity at the fault location is considered as the maximum porosity due to greatest damage (strongeststress) and this value tends to decrease with distance away from the fault and also decrease with depth. [4] As observed by many outcrops in Vung Tau and Phan Thiet, it is clear that the fault damage zone is not usually isolated but is commonly in conjunction (linked or connected) with others. This means fracture porosity and/or porosity distribution may change abruptly or slowly away from a fault depending on how dense the system is or how good the fracture connectivity is. In some cases, dynamic interpretation such as DST analysis may be able to provide control dimension. This parameter is represented by lateral porosity. The lateral porosity is a function of the distance to fault. Hence, in order to cover the uncertainties of this variation, the 3D geological model uses a rangeof the distance to fault as indicated in Table 3.7 and HUYNH THANH TOAN 98 Bachelor Thesis 3.8. These ranges are either taken from nearby basement fields such as Su Tu Den and Ruby or are estimated based on outcrop studies. [4] Along the vertical direction, porosity decreases when the distance from top of the basement increases. This is reflected by vertical porosity. The vertical porosity vs. depth represented by minimum, most likely and maximum curves are presented in figures of 3.22, 3.23. Figure 3.22: HALO Fault Model HUYNH THANH TOAN 99 Bachelor Thesis Figure 3.23: Vertical Porosity Distribution Function in HALO Fault Model Figure 3.24: Lateral Porosity Distribution Function in HALO Fault Model HUYNH THANH TOAN 100 Bachelor Thesis Fault systems of X basement were classified into two categories for improving the porosity distribution. The two categories are sealing (non-conductive) and nonsealing (conductive) fault systems. Non-conductive faults are assigned to all block boundary faults. Block boundary faults have typically experienced multi-phase activity with large movement, so they are generally associated with cataclasite and milonite zones with poor to no porosity, as seen in nearby fields. Based on the range of distance to fault, the range of vertical porosity and the fault classification, this 3D property model was built for 16 cases, and each of them has 3 sub-cases as a Min, ML and Max. All these cases are described in Tables 3 . 7 a n d 3.8. Table 3.7: Scenario #1 in the X Field: All faults are conductive m m m m m m m m Table 3.8: Scenario #2 in the X Field: Bounding faults – Non-conductive and Other faults - conductive m m m HUYNH THANH TOAN m m m m m 101 Bachelor Thesis ML of case 2 Figure 3.25: Porosity Sections in HALO Fault Model of X Field Low case of case 3 Figure 3.26: Porosity Sections in HALO Fault Model of X Field ML case of case 1 Figure 3.27: Porosity Sections in HALO Fault Model of X Field HUYNH THANH TOAN 102 Bachelor Thesis 3.3. Volumetric Calculation The input parameters used to calculate Volumes for the X field, 3D Basement Reservoir model are listed as follows: BRV : Taken from model. NTG : Taken from NTG model. Porosity: Taken from Porosity model. Bo The result of PVT analysis of fluid sample taken from X-3X Sw : : OWC: The Water saturation (assumption from nearby Fields) Oil Water Contact assumed as structural spill point at 4620m TVDSS (Block A, B) Table 3.9: Values of Applied Parameters Parameters Min Most likely Max Bo (res.bbl/STB) 2.34 2.37 2.53 Sw (fraction) 0.05 0.1 0.15 NTG From modelling From modelling From modelling Porosity From modelling From modelling From modelling BRV From modelling From modelling From modelling The Tables below illustrate the typical results of volumetric estimation by using different scenarios of input parameters. HUYNH THANH TOAN 103 Bachelor Thesis Table 3.10: OIIP Estimated from NPV Model. OIIP from NPV MODEL MinNTG-MinPoro ML.NTG-ML.Poro MaxNTG-MaxPoro OIIP [MMSTB] OIIP [MMSTB] OIIP [MMSTB] Block A 57.52 194.91 366.01 Block B 5.45 13.68 24.45 Total 62.97 208.59 390.46 Block Table 3.11: OIIP Estimated from Halo Fault model – Case #1 Maximum of Distance to Fault = 120 m Block Min Most likely Max OIIP [MMSTB] OIIP OIIP [MMSTB] [MMSTB] Block A 64.09 182.19 268.62 Block B 2.34 3.56 5.03 Total 66.43 185.75 273.65 HUYNH THANH TOAN 104 Bachelor Thesis Table 3.12: OIIP Estimated from Halo Fault model – Case #2 Maximum of Distance to Fault = 270 m Block Min OIIP [MMSTB] Most likely OIIP [MMSTB] High OIIP [MMSTB] Block A 83.58 236.20 348.49 Block B 3.90 5.90 8.33 Total 87.48 242.1 356.82 Table 3.13: OIIP Estimated from Halo Fault model – Case #3 Maximum of Distance to Fault = 400 m Block Min Most likely Max OIIP OIIP OIIP [MMSTB] [MMSTB] [MMSTB] Block A 97.30 273.71 403.98 Block B 5.50 8.31 11.72 Total 102.8 282.02 415.7 HUYNH THANH TOAN 105 Bachelor Thesis Table 3.14: OIIP of X Basement Estimated from Halo Fault Models Total Case 1 Case 2 Case 3 Maximum of Distance to Fault Maximum of Distance to Fault =120 m Maximum of Distance to Fault = 270 m Maximum of Distance to Fault = 400 m Min Most likely STOIIP [STB] STOIIP [STB] Max STOIIP [STB] 66,433,160 185,752,436 273,651,159 87,483,145 242,102,310 356,820,002 102,806,613 282,025,927 415,704,173 The OIIP of Most likely cases of Distance to Fault from 120m to 400m are ranging from 185 to 282 MMSTB. This range covers the P50 OIIP of Conventional Volumetric Method. Table 3.15: OIIP Estimated from Halo Fault Model (Variogram method). Realization volumetric estimation (OIIP [MMSTB]) Block/Segment R#1 R#2 R#3 R#4 R#5 A_Green 34.75 29.3 22.08 25.11 22.53 A_Red 45.03 60.66 47.72 53.78 61.49 A_Yellow 67.67 81.79 62.15 33.24 47.9 A_Blue 20.04 20.81 16.59 13.72 14.99 Block B 73.15 63.52 34.23 74.21 72.25 240.64 256.08 182.77 200.06 219.16 Total HUYNH THANH TOAN 106 Bachelor Thesis Note that, the Table 3.15 above is a result of other reservoir modelling method applied for X basement reservoir with intention to reduce uncertainty of porosity distribution using curves of porosity vs depth in which porosity decrease with depth. An azimuth parameter was used in respect to conductive fracture orientation existing in X basement. 3.4. Upscaling Upscaling is a process to create a coarser grid from a fine grid model due to the limitation of Flow Simulation runtime (range of total cells of static model may be some millions to tens of millions while flow simulation can only run with range of less than some hundred thousands). Good upscaling has to maintain as much as possible the scale of geological heterogeneity of the geological model and/or property distribution and volumetric. However, differences between the two models (pre and post upscaling) always exist but an acceptable difference should be less than 10%. As mentioned earlier, Front-Sim as a dynamic tool was applied to QC the upscaling quality. In the upscaling process, different options are run until an acceptable model is created. The upscaling process is commonly comprised of two steps as described in detail in the following sections. Create Coarser Geometry When creating the simulation grid, carefully preparation and design were taken to ensure important geological features were maintained such as the geological and structural heterogeneity of the fine grid model. Based on the normal grid size for other basement reservoirs in the Cuu Long Basin, and with agreement of the total cell number with the simulation group, the initial horizontal grid size was set to 100mx100m. After that, some sensitivity cases were run in order to optimize the grid size. Finally, a horizontal grid size of 100mx100m was chosen. The main parameters for upscaling the 3D X Basement Coarse Grid are as follow: HUYNH THANH TOAN 107 Bachelor Thesis Cell size: Layers: Number of faults: Cells (I x J x K) Total number of defined cells: Rotated angle: 100m x 100m x 40m 35 layers 111 147 x 53 x 35 272685 450 Scale Up Properties The Most likely of Cases #1 are considered as the Base Case to be up-scaled for numerical simulation respectively. The OIIP of these cases are close to the P50 OIIP that is calculated from Conventional Volumetric Estimation. Reservoir properties that have been populated in the 3D fine grid were upscaled into the coarser grid using a range of upscale methods. After running some sensitivities, the layered sampling & arithmetic method which is weighted to gross volume was selected to use for upscaling of porosity. The statistical values are presented in Figure 3.28 for QC as an example. Results Pore Volumes of before and after upscaling models were compared. As a result, the difference of OIIP between pre and post upscaling is less than 0.5 percent for all three 3D property models. These differences are presented in Table 16 and indicate the success of the upscaling porosity property. Porosity images of pre and post upscalingare shown in the Figures 3.29 and 3.30. HUYNH THANH TOAN 108 Bachelor Thesis Figure 3.28: Static values and histograms of pre and after Upscaling of 3D porosity model for low case of Case 3 Figure 3.29: Porosity in Halo Fault Model before and after Upscaling. HUYNH THANH TOAN 109 Bachelor Thesis Upscale Poro Along X – 3X Fine scale Poro Along X – 3X Figure 3.30: Porosity in Halo Fault Model before and after Upscaling Section along X-3X well. Model Verification The static method also includes volumetric checks using different scenarios of parameter input for static model (both NPV and Halo models) and upscaling (before and after) (see Table 3.16). Table 3.16: Difference between pre and after upscaling OIIP (MMSTB) Min case (case #3) Most likely case (case #1) Most likely case (case #2) Fine Grid 102.81 185.75 242.10 Coarse Grid 108.99 183.89 240.89 Difference (%) 0.06 -0.01 -0.005 The dynamic tool in Petrel is Front-Sim. All reservoir engineering data such as well test, PVT, production, etc. are input into the Front-Sim model. Forecast production profiles can be generated from Front-Sim Run for comparison both pre and post upscaling models. Front-Sim is not only a good tool for model QC and upscaling qualification but is also very useful to validate most of data input as well as initial indication of well performance for Reservoir Simulation. HUYNH THANH TOAN 110 Bachelor Thesis CHAPTER 4. CONCLUSION AND RECOMMENDATIONS 4.1. Conclusions: Present and explain the formation of the basement of the Cuu Long basin, as well as the causes of the complex geological structure in the basement. Systematize the basic theory, advantages and disadvantages of the methods being used for the basement layer. Systematize the basic theory of model building methods. Detailed study on the theoretical basis and application process of the NPV and Halo method in field X. Presenting the classification of fault systems as the basis for the attribute model. Uses gross rock volume (GRV) that is generated from geologic/geophysical data and porosity estimated from resistivity-based method plus NTG. Set up the porosity variation equation for depth from the top of the basement and according to the fracture distance. From there, build the permeability equation for each type of fault. Applying the principles of NPV and Halo, with the support of Petrel software (Schlumberger Company) to build a model of porosity and permeability distribution with established data. Result of other reservoir modelling method applied for X basement reservoir with intention to reduce uncertainty of porosity distribution using curves of porosity with depth. An azimuth parameter was used in respect to conductive fracture orientation existing in X basement. Based on the normal grid size for other basement reservoirs in the Cuu Long Basin, the initial horizontal grid size was set. After that, some sensitivity cases were run in order to optimise the grid size. Finally, a horizontal grid size of 100mx100m was chosen and show the main parameters for upscaling the 3D X Basement Coarse Grid. Pore Volumes of before and after upscaling models were compared. As a result, the difference of OIIP between pre and post upscaling is less than 0.5 percent for all three 3D property models. The dynamic tool in Petrel is FrontSim for comparison both pre and post upscaling models. Front-Sim is not only a good tool for model QC and upscaling qualification. HUYNH THANH TOAN 111 Bachelor Thesis 4.2. Recommendations: In the meantime, the current working model is recommended to be used as input for next simulation step. Method: o We continue to assess the impact of uncertain factors on OIIP results o Strengthen the field to upgrade petro physics, DST… data For future plan: o It is necessary to update the model as new data are available, especially when development wells are drilled. Dynamic check should also be planned to reduce the model uncertainties. o If favorable database such as core data, depositional conceptual model, etc., becomes available, object modeling may be considered to capture more geologically heterogeneities. o As soon as flow simulation started, interactive response should be set up for steps before prediction to catch Limitations: o According to both models, the parameters for calculating OIIP are uncertain quantities, typically the averaged porosity quantity during construction. The NTG value in the NPV method is calculated based on the cutoff value, so this does not fully reflect the capacity of the foundation seam. The results of the method have many errors in the calculation process. o In fact, whether it is a sedimentary or a basement reservoir, there is heterogeneity in the reservoir, not all cases of porosity decrease with depth, especially in areas with complex geological conditions, so when applied Using both NPV and Halo models will give results with low reliability. HUYNH THANH TOAN 112 Bachelor Thesis REFERENCES [1]. Efeoghene, Enaworu. 2014, Evaluating Uncertainty in the Volumes of Fluids in Place in an Offshore Niger Delta Field, pp. 144-149. [2]. Arvin Khadem Samimi, Ghafoor Karimi. 2014, Sensitivity and Uncertainty Analysis Of Original Oil-In-Place In Carbonate Reservoir Modeling, A case study, pp. 332-338. [3]. Tinh, Vo Van. Reserve Assessment of initial oil on 23-1 and 23-2 Miocene Reservoir of Cuu Long Basin. TPHCM: s.n., 2014. [4]. Tuan, Nguyen Manh. Building 3D Geological Model in order to serve in simulaation for Gau Den Field, Block 16-1, Cuu Long Basin. TPHCM: 1, 2012. [5]. Tran Van Xuan, Thai Ba Ngoc. Appying Bestfit and Crystalball in reserve assessment for X Field by volumetric method. [6]. Schlumberger, Houston. Petrel Manual. 2014. [7]. Octavian Catuneanu, William E.Galloway. 2011, Sequence Stratigraphy: Methodology and Nomenclature, pp. 173-245. [8]. Vitor, Ebio. 2017, The application of sensitivity analysis to reservoir simulation. [9]. Corre, Thore, B. Feraudy, V. P. de and G.Vincent. 2000, Integrated Uncertainty Assessment For Project Evaluation and Risk Analysis. [10]. EMMANUEL GRINGARTEN, EARTH DECISION SCIENCES. 2012, UNCERTAINTY ASSESSMENT IN 3D RESERVOIR MODELING, pp. 38-42. [11]. Tran Van Xuan, Thai Ba Ngoc. 2019, Analysing the sensitivity of input parameters for oil reserve estimation of DQ oil Field in conjunction with Monte Carlo simulations. [12]. Samson, P., Guémené, J.-M., Robbe, O., Feraudy, V. D., Rossi, T., Larsonneur, J.-L., Bez, M., Bourdat, M., Larue, D. 3D Modeling and Reservoir Uncertainties: A Case Study. HUYNH THANH TOAN 113 Bachelor Thesis [13]. Chatterjee, S., Bhattacherjee, A., Samanta, B., Pal, S.K., 2006. Ore grade estimation of a limestone deposit in India using an Artificial Neural Network. Applied GIS 2(1), 2.1–2.20. [14]. Công ty Liên doanh điều hành Cửu Long, 2013. Advanced Halo Model for Su Tu Vang field. [15]. Đỗ Tuấn Khanh, 2008. Mô hình hóa vỉa móng nứt nẻ bằng phương pháp mạng nơ ron nhân tạo (Artificial Intelligent Network). Luận văn tốt nghiệp. Đại học Bách Khoa Tp. Hồ Chí Minh. [16]. Nguyễn Đức Đông, 2015. Ứng dụng phương pháp mạng nơron nhân tạo (ANN) để xây dựng mô hình dự báo phân bố trường rỗng-thấm của tầng đá móng nứt nẻ mỏ Tây Hồ, lô A, bồn trũng Cửu Long. Luận văn thạc sỹ, Đại học Bách Khoa Tp. Hồ Chí Minh. [17]. Nguyễn Anh Đức, 2015. Đặc Điểm Nứt Nẻ Trong Đá Móng Granitoid Mỏ Hải Sư Đen Trên Cơ Sở Phân Tích Tổng Hợp Tài Liệu Địa Vật Lý Giếng Khoan Và Thuộc Tính Địa Chấn. Luận văn tiến sĩ. Đại học mỏ địa chất Hà Nội. [18]. Nguyễn Thụy Hoài Giang, 2010. Đặc điểm môi trường trầm tích của tầng chứa cát kết oligocen muộn, mỏ Sư Tử, lô 15-1, bồn trũng Cửu Long. Luận văn tốt nghiệp. Đại học Bách Khoa Tp.Hồ Chí Minh. [19]. KS. Nguyễn Thị Thu Huyền và PGS.TSKH. Hoàng Đình Tiến, 2014. Các yếu tố ảnh hưởng đến sự hình thành và phát triển tính thấm chứa của đá móng mỏ Bạch Hổ. Tạp chí Dầu khí số 2/2014, tr.17-26. [20]. Nguyễn Quốc Quân, 2012. Mô hình địa chất cải tiến cho tầng móng nứt nẻ granit trong vùng mỏ Hải Sư Đen, bể Cửu Long. Tạp chí địa chất, loại A, số 330, 34/2012, tr.17-25 [21]. Hoàng Thị Hồng Hạnh, 2010. Bài giảng Địa chất cơ sở - Chương 7. Chuyển động kiến tạo và biến dạng của vỏ trái đất. Đại học Bách Khoa Thành phố Hồ Chí Minh. HUYNH THANH TOAN 114 Bachelor Thesis [22]. ThS. Đặng Ngọc Quý và PGS.TS. Hoàng Văn Quý, 2014. Thân dầu trong đá móng trước Đệ Tam mỏ Sư Tử Đen, Sư Tử Vàng và các yếu tố địa chất ảnh hưởng đến khả năng thu hồi dầu. Tạp chí dầu khí, số 2/2014, tr. 12-16. [23]. Hoàng Văn Long, 2011. Địa chất cấu tạo và đo vẽ bản đồ địa chất. Đại Học Mỏ - Địa Chất. [24]. Schlumberger, 2002. FMI, Borehole geology, geomechanics and 3D reservoir modeling. [25]. Schlumberger Information Solution (2010), Petrel – Tài liệu phần mềm tìm kiếm thăm dò đến khai thác và mô phỏng vỉa Petrel, Schlumberger center of Product. APPENDICES: HUYNH THANH TOAN 115