XÂY DỰNG MÔ HÌNH CẤU TRÚC 3 CHIỀU CHO CẤU TẠO DẦU KHÍ
Sự minh giải tài liệu chấn 3 chiều cho cơ hội để đưa ra các bản đồ cấu trúc dưới sâu mặt đất. Ngoài ra, sự kết hợp minh giải tài liệu địa chấn với tài liệu địa vật lý giếng khoan sẽ cung cấp thêm những thông tin đáng tin cậy đệ thông hiệu tốt các cấu trúc sâu, đặc biệt là xác định các đứt gãy và đới nứt nẻ.
Hội nghị khoa học và công nghệ lần thứ 9, Trường Đại học Bách khoa Tp. HCM, 11/10/2005
XÂY DỰNG MÔ HÌNH CẤU TRÚC 3 CHIỀU CHO CẤU TẠO DẦU KHÍ
DỰA VÀO TÀI LIỆU ĐỊA CHẤN VÀ ĐỊA VẬT LÝ GIẾNG KHOAN
CONSTRUCTING A 3-D STRUCTURAL MODEL OF AN OIL & GAS
PROSPECT BASED ON SEISMIC AND WELL LOG DATA
Hồ Trọng Long*, Bùi Thị Thanh Huyền**, Keisuke Ushijima1***
* Khoa Kỹ thuật Địa chất và Dầu khí, Đại học Bách Khoa Tp.Hồ Chí Minh, Việt Nam
** Department of Civil and Earth Resources Engineering, Kyoto University, Japan
*** Exploration Geophysics Laboratory, Graduate School of Engineering, Kyushu University, Japan
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TÓM TẮT
Sự minh giải tài liệu địa chấn 3 chiều cho cơ hội để đưa ra các bản đồ cấu trúc dưới sâu mặt đất.
Ngoài ra, sự kết hợp minh giải tài liệu địa chấn với tài liệu địa vật lý giếng khoan sẽ cung cấp thêm
những thông tin đáng tin cậy để thông hiểu tốt các cấu trúc sâu, đặc biệt là xác định các đứt gãy và các
đới nứt nẻ. Trong nghiên cứu này, chúng tôi đã sử dụng một kỹ thuật tính toán dựa vào máy tính gọi là
“Mạng Nơron” để tính độ rỗng của vỉa với độ chính xác cao. Các giá trị độ rỗng có thể thành lập được
các bản đồ phân bố độ rỗng cho một cấu tạo dầu khí. Chúng tôi nhận thấy rằng, các đới có độ rỗng cao
gắn liền với các đứt gãy và các đới nứt nẻ. Vì vậy, sự hiệu chỉnh giữa các bản đồ phân bố độ rỗng và
kết quả minh giải tài liệu địa chấn có thể xác định các đứt gãy và các đới nứt nẻ với độ tin cậy cao
hơn. Từ đó, mô hình cấu trúc 3 chiều sẽ được thành lập, thể hiện các hình dạng cấu trúc và kiến tạo
cho việc đánh giá tiềm năng hydrocarbon. Chúng tôi đã sử dụng tài liệu của cấu tạo dầu khí A2-VD ở
thềm lục địa phía Nam Việt Nam cho bài báo này. Các kết quả thu được đã cung cấp những thông tin
rất có giá trị cho việc nhận diện vị trí các giếng khoan và khai thác, cũng như cho sự phát triển của cấu
tạo này trong tương lai.
ABSTRACT
Interpretation of three-dimensional (3-D) seismic data gives an opportunity to generate deep
subsurface structure maps. Furthermore, combination of seismic with well-logging data interpretation
will provide more reliable information for good understanding of deep structures, especially faults and
fractured zones prediction. In this study, we used a computing technique based on computer program
named “Neural Network”, to predict porosity of reservoirs with high accuracy. Porosity values can
build porosity contribution maps for an oil & gas prospect. We found that, the zones with high
porosity relate to the faults and fractured zones. Therefore, the correction between porosity
distribution maps and results of seismic data interpretation can used to predict faults and fractured
zones with higher reliability. Hence, 3-D structural model will be constructed, revealed structural and
tectonic configurations for hydrocarbon potential assessment. We used data of A2-VD oil & gas
prospect, southern offshore Vietnam, for this paper. Achieved results provided very valuable
information for the identification of drilling and production well location, as well as development of
the prospect in the future.
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1. INTRODUCTION study area as presented in Figure 2 (JVPC, 2000
and 2001).
A2-VD oil prospect, located in Cuu Long
basin (Figure 1), southern offshore Vietnam is a
2. THREE-DIMENSIONAL (3-D) SEISMIC
main target area for oil and gas exploration in
DATA INTERPRETATION OF A2-VD
Viet Nam with the major reservoir is fractured
PROSPECT
granite basement (PV, 1998). The Cuu Long
basin that was formed during Cenozoic Era In this research, we conducted seismic
under the influence of India-Eurasian collision interpretation of a volume cube for 3-D seismic
generating the South China Sea spreading, is the data in the area 12.5 x 6 km2 with 345 inlines
most prospective hydrocarbon basin in offshore and 320 crosslines. The major seismic sequences
Vietnam (Phuong, 1997), especially the A2-VD in each section were determined by correlation
oil prospect in Block 15-2 is of particular with stratigraphy derived from the wells in the
interest. study area (JVPC, 2000 and 2001). The
interpretation was carried out using the basic
The sedimentary stratigraphy of this basin is
concepts for seismic stratigraphy interpretation
divided into several sequences: basement (Pre-
(Badley, 1985; Vail et al., 1977). Figure 3 shows
Tertiary), sequence E (Lower Oligocene to
the seismic data interpretation in selected
Eocene), D (Upper Oligocene), C (Early
sections.
Miocene), B1 (Middle Miocene), and younger
sequences (B2 and A). The stratigraphy
correlates with wells VD-1X, VD-2X in the
Figure 1 Location of the A2-VD prospect Figure 2 Stratigraphy and wells correlation of
(Modified from PV, 1998; JVPC, 2001) Block 15-2 (A2) (after JVPC, 2000)
Figure 3 Seismic data interpretation in selected sections
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3. POROSITY DISTRIBUTIONS USING process of NN, we applied the most common
NEURAL NETWORK learning law, back-propagation, as a training law
The architecture of NN we used as shown in to reduce the errors (Lippman, 1987). However,
Figure 4 with one input layer composed of six back-propagation includes several kinds of
nodes. These six nodes represent the response of paradigms such as on-line back-propagation,
neutron, density, sonic, resistivity (LLS, LLD batch back-propagation, delta-bar-delta, resilient
and MSFL). propagation (RPROP) and quick propagation
(Werbos, 1994). The most successful paradigm
Processing elements used in this study are batch back-propagation.
NPHI (PE)
Connection
By using batch back-propagation paradigm,
weights figure 5 shows the RMS errors as a function of
Density training and testing data set patterns of NN, that
all of them are lower than 0.1.
Porosity or
Sonic
Permeability The data used for the network design are
taken from various wells in A2-VD oil prospect.
LLS
Output layer We used derived NN to predict porosity from
logs data of all wells in A2-VD oil prospect.
Comparison of NN predictions and log
LLD Hidden layer
predictions with core data are displayed in
Figure 6 as a selection of well A2-VD-1X. It
MSFL shows the results in the cored reservoir intervals,
Input layer
in that NN method is more efficient than
Figure 4 Architecture of neural network used in
this study conventional log method. Porosity values versus
depth of all wells in study area were used to
A single hidden layer has five nodes and the reveal the distribution maps of them. Figure 7
output layer has only one node represents shows the porosity distribution in the upper 100
porosity. With data of this study area, more meters of the basement.
hidden layers or more neurons of each layer is The porosity distributions was correlated
ineffective and make more complex calculation. with seismic data interpretation for faults and
For training NN, we used training data set which fracture zones identification (Figures 7, 8 and 9)
is a data set of 6 inputs parameters from well log because the zones of good porosity are related to
data and 1 output parameter is porosity that was faults. Hence, 3-D structural models are able to
selected from core samples. During training constructed reliably.
(a) (b)
RMS Error Vs. Pattern RMS Error Vs. Pattern
for all Nodes for all Nodes
0.11 0.11
0.09 0.09
Training Data
Testing Data
0.07 0.07
Error
Error
0.04 0.04
0.02 0.02
0.00 0.00
1 9 17 25 33 41 49 57 65 70 1 5 9 13 17 21 25 27
Pattern # Pattern #
Figure 5 RMS errors as a function of training and testing data set patterns of porosity NN for
(a) the training data set; (b) the testing data set
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0.39
0.36
0.33
0.3
0.27
p o ro s it y (% )
0.24 CORE porosity
0.21
0.18
NN porosity
0.15 LOG porosity
0.12
0.09
0.06
0.03
0
2165 2170 2175 2180 2185 2190 2195 2200 2205 2210
depth (m)
Figure 6 Comparison of porosity predicted by Figure 7 Porosity distribution combined
NN and conventional log method to that of with seismic data to predict major faults
core samples in a selected well (A2-VD-1X) and fractured zones in the upper 100
meters of the basement
Figure 8. Structure of the top basement Figure 9. Structure of the top D horizon
corrected with porosity distribution in A2- correctedwith porosity distribution in A2-VD
VD prospect prospect
4. CONSTRUCTION 3-D STRUCTURAL the top of the basement. The faults strongly
MODELS OF A2-VD PROSPECT segmented the basement with the location is
nearly as the same as the location of high
In this study, we focused to construct 3-D porosity distribution from NN. Re-activation of
model of the top basement and E sequence, the faults in the Eocene and Lower Oligocene
because that are main targets of oil and gas results in basement uplift, completely truncating
production in this prospect (JVPC, 2001). the E sequence (Figure 11). Fault activities were
A 3-D structural model was prepared using a interpreted meticulously from the seismic
PC-based program. The basement is modeled as sections. This uplift shifts the top of the E
a Pre-Tertiary formation with a maximum depth sequence from 3000 ms to 2200 ms, and the
of 3500 ms and minimum depth (highest point) truncation eliminates the E sequence from the
of 2100 ms. basement high. Fault locations from these
structural maps are quite coincident with the
Figure 10 shows the 3-D structural model for
porosity locations obtained by NN.
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Figure 10. 3-D view of faults and the top Figure 11. 3-D relationship between the basement
basement in A2-VD prospect high and the E sequence in A2-VD prospect
5. CONCLUSIONS
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