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
--------------------------------------------------------------------------------------------------------------------------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.
1. INTRODUCTION
A2-VD oil prospect, located in Cuu Long
basin (Figure 1), southern offshore Vietnam is a
main target area for oil and gas exploration in
Viet Nam with the major reservoir is fractured
granite basement (PV, 1998). The Cuu Long
basin that was formed during Cenozoic Era
under the influence of India-Eurasian collision
generating the South China Sea spreading, is the
most prospective hydrocarbon basin in offshore
Vietnam (Phuong, 1997), especially the A2-VD
oil prospect in Block 15-2 is of particular
interest.
The sedimentary stratigraphy of this basin is
divided into several sequences: basement (PreTertiary), sequence E (Lower Oligocene to
Eocene), D (Upper Oligocene), C (Early
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
(Modified from PV, 1998; JVPC, 2001)
study area as presented in Figure 2 (JVPC, 2000
and 2001).
2. THREE-DIMENSIONAL (3-D) SEISMIC
DATA INTERPRETATION OF A2-VD
PROSPECT
In this research, we conducted seismic
interpretation of a volume cube for 3-D seismic
data in the area 12.5 x 6 km2 with 345 inlines
and 320 crosslines. The major seismic sequences
in each section were determined by correlation
with stratigraphy derived from the wells in the
study area (JVPC, 2000 and 2001). The
interpretation was carried out using the basic
concepts for seismic stratigraphy interpretation
(Badley, 1985; Vail et al., 1977). Figure 3 shows
the seismic data interpretation in selected
sections.
Figure 2 Stratigraphy and wells correlation of
Block 15-2 (A2) (after JVPC, 2000)
Figure 3 Seismic data interpretation in selected sections
process of NN, we applied the most common
learning law, back-propagation, as a training law
to reduce the errors (Lippman, 1987). However,
back-propagation includes several kinds of
paradigms such as on-line back-propagation,
batch back-propagation, delta-bar-delta, resilient
propagation (RPROP) and quick propagation
(Werbos, 1994). The most successful paradigm
used in this study are batch back-propagation.
By using batch back-propagation paradigm,
figure 5 shows the RMS errors as a function of
training and testing data set patterns of NN, that
all of them are lower than 0.1.
3. POROSITY DISTRIBUTIONS USING
NEURAL NETWORK
The architecture of NN we used as shown in
Figure 4 with one input layer composed of six
nodes. These six nodes represent the response of
neutron, density, sonic, resistivity (LLS, LLD
and MSFL).
Processing elements
(PE)
NPHI
Connection
weights
Density
Porosity or
Permeability
Sonic
The data used for the network design are
taken from various wells in A2-VD oil prospect.
We used derived NN to predict porosity from
logs data of all wells in A2-VD oil prospect.
Comparison of NN predictions and log
predictions with core data are displayed in
Figure 6 as a selection of well A2-VD-1X. It
shows the results in the cored reservoir intervals,
in that NN method is more efficient than
conventional log method. Porosity values versus
depth of all wells in study area were used to
reveal the distribution maps of them. Figure 7
shows the porosity distribution in the upper 100
meters of the basement.
Output layer
LLS
LLD
Hidden layer
MSFL
Input layer
Figure 4 Architecture of neural network used in
this study
A single hidden layer has five nodes and the
output layer has only one node represents
porosity. With data of this study area, more
hidden layers or more neurons of each layer is
ineffective and make more complex calculation.
For training NN, we used training data set which
is a data set of 6 inputs parameters from well log
data and 1 output parameter is porosity that was
selected from core samples. During training
0.11
The porosity distributions was correlated
with seismic data interpretation for faults and
fracture zones identification (Figures 7, 8 and 9)
because the zones of good porosity are related to
faults. Hence, 3-D structural models are able to
constructed reliably.
(a)
(b)
RMS Error Vs. Pattern
for all Nodes
RMS Error Vs. Pattern
for all Nodes
0.11
0.04
0.07
0.04
0.02
0.02
0.00
Error
Training Data
Error
0.07
Testing Data
0.09
0.09
0.00
1
9
17
25
33
Pattern #
41
49
57
65
70
1
5
9
13
Pattern #
17
21
25
27
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
p o ro s it y (% )
0.39
0.36
0.33
0.3
0.27
0.24
0.21
0.18
0.15
0.12
0.09
0.06
0.03
0
2165
CORE porosity
NN porosity
LOG porosity
2170
2175
2180
2185
2190
2195
2200
2205
2210
depth (m)
Figure 6 Comparison of porosity predicted by
NN and conventional log method to that of
core samples in a selected well (A2-VD-1X)
Figure 8. Structure of the top basement
corrected with porosity distribution in A2VD prospect
4.
CONSTRUCTION 3-D STRUCTURAL
MODELS OF A2-VD PROSPECT
In this study, we focused to construct 3-D
model of the top basement and E sequence,
because that are main targets of oil and gas
production in this prospect (JVPC, 2001).
A 3-D structural model was prepared using a
PC-based program. The basement is modeled as
a Pre-Tertiary formation with a maximum depth
of 3500 ms and minimum depth (highest point)
of 2100 ms.
Figure 10 shows the 3-D structural model for
Figure 7 Porosity distribution combined
with seismic data to predict major faults
and fractured zones in the upper 100
meters of the basement
Figure 9. Structure of the top D horizon
correctedwith porosity distribution in A2-VD
prospect
the top of the basement. The faults strongly
segmented the basement with the location is
nearly as the same as the location of high
porosity distribution from NN. Re-activation of
the faults in the Eocene and Lower Oligocene
results in basement uplift, completely truncating
the E sequence (Figure 11). Fault activities were
interpreted meticulously from the seismic
sections. This uplift shifts the top of the E
sequence from 3000 ms to 2200 ms, and the
truncation eliminates the E sequence from the
basement high. Fault locations from these
structural maps are quite coincident with the
porosity
locations
obtained
by
NN.
Figure 10. 3-D view of faults and the top
basement in A2-VD prospect
5. CONCLUSIONS
By using neural network, reliability porosity
values can be predicted directly from well log
data. And then, porosity distribution maps were
combined with seismic data interpretation to
predict faults and fractures zones. Hence, 3-D
structural models were constructed reliably.
The 3-D structure models and structural
maps prepared based on 3-D seismic data and
well log data for the A2-VD prospect have
revealed the detail subsurface structure of this
area. This research provides useful data for oil
field development in offshore Vietnam, and will
be supplemented in the near future with more
detailed research on the fault distributions in this
area and also illustrated the influence of IndiaEurasian to the tectonics of Vietnam. These
studies thus form the basis for hydrocarbon
potential assessment in this area, and provide
fundamental data for planning of oil prospects.
Acknowledgements
Gratitude is extended to Japan Vietnam
Petroleum Company (JVPC) and PetroVietnam
for providing the data for this research.
Figure 11. 3-D relationship between the basement
high and the E sequence in A2-VD prospect
REFERENCES
1. Badley, M. E.,. Practical seismic
interpretation.
International
Human
Resources
Development
Corporation,
Boston, USA (1985).
2. Japan Vietnam Petroleum Company
(JVPC). Report for the Block 15-2 prospect,
southern offshore Vietnam (2000), pp. 4142.
3. Japan Vietnam Petroleum Company
(JVPC). Report for the Block 15-2 prospect,
southern offshore Vietnam (2001), pp. 103104.
4. Lippman, R. An introduction to computing
with neural nets, IEEE Transactions on
Acoustics. Speech and Signal Processing,
Vol. 4 (1987), pp. 4-22.
5. Long, H.T., Huyen, B.T.T., El-Qady, G.,
Ushijima, K. Porosity & permeability
estimation in A2-VD oil prospect, southern
offshore Vietnam using artificial neural
networks. Proceedings of Second Annual
Petroleum Conference and Exhibition,
Egypt (2005), pp. 16.
6. PetroVietnam. Report of Cuu Long basin,
southern offshore Vietnam (1998), pp. 7-8.
7. Phuong, L.T. Lithofacies and depositional
environments of the Oligocene sediments of
the Cuulong basin and their relationship to
hydrocarbon potential. Proceedings of an
International Conference on Petroleum
Systems of Southeast Asia & Australia,
Jakarta, May 21-23, IPA (1997), pp. 531538.
8. Vail, P. R., Mitchum, R. M., Jr. and
Thompson, S., III. Seismic stratigraphy and
global changes of sea level, Part 2, The
depositional sequence as a basic unit for
stratigraphic
analysis:
in
Seismic
Stratigraphy--Applications to Hydrocarbon
Exploration, Payton, C. E. (Ed.). AAPG
Memoirs, Vol. 26, (1977), pp. 53-62.
9. Werbos, P.J. The Roots of BackPropagation. John Wiley & Sons, Inc
(1994), pp. 115-127.