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:
…………………………………………………………………………………………
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…………………………………………………………………………………………
6. The main disadvantages of thesis:
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7. Recommend: Accept to defend:
Add more to defend:
Cannot defend:
8. Questions that student must answer:
a)…………………………………………………………………………………………
……………………………………………………………………………………...........
b)…………………………………………………………………………………………
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c)…………………………………………………………………………………………
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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
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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
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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
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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
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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
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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
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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
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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.
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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]
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 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.
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
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
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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
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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
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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,
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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.
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
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.
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
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
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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
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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
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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
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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)
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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
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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).
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- 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.
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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]
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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.
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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]
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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
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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.
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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:
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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]
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 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-
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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
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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]
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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
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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.
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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
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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.
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-
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
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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
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 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))
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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).
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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).
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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
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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
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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.
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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.
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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
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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.
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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.
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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
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-
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
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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.
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-
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]
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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
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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.
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 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]
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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
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*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.
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 

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]
i1 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
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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
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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
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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.
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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.
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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).
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Block C
Block B
Block A
Block D
Block E
Figure 3.1: Basement Structural Blocks of X field
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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
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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.
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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
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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
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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.
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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
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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
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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
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POROSITY – DEPTH RELATIONSHIP (X – 2X)
X – 2X
Max
Most Likely
Min
Figure 3.6: Basement Porosity vs. Depth of X-2X well
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POROSITY – DEPTH RELATIONSHIP (X – 2XST)
X – 2XST
Max
Most Likely
Min
Figure 3.7: Basement Porosity vs. Depth of X-2XST well
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POROSITY – DEPTH RELATIONSHIP (X – 3X)
X – 3X
Max
Most Likely
Min
Figure 3.8: Basement Porosity vs. Depth of X-3X well
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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
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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
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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.
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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
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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):
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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
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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.
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NTG – DEPTH RELATIONSHIP (X – 1X)
X – 1X
Figure 3.12: Basement NTG vs. Depth of X-1X
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NTG – DEPTH RELATIONSHIP (X – 2X)
X – 2X
Figure 3.13: Basement NTG vs. Depth of X-2X
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NTG – DEPTH RELATIONSHIP (X – 2XST)
N 2 G _X – 2XST
Figure 3.14: Basement NTG vs. Depth of X-2XST
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NTG – DEPTH RELATIONSHIP (X – 3X)
N T G _X – 3X
Figure 3.15: Basement NTG vs. Depth of X-3X
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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
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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
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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.
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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.
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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
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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
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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
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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
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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
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Figure 3.23: Vertical Porosity Distribution Function in HALO Fault Model
Figure 3.24: Lateral Porosity Distribution Function in HALO Fault Model
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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
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m
m
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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
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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.
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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
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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
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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
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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:
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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.
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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.
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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.
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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.
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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.
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APPENDICES:
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