Tài liệu XÂY DỰNG MÔ HÌNH CẤU TRÚC 3 CHIỀU CHO CẤU TẠO DẦU KHÍ docx

Hi ngh khoa hc và công ngh ln th 9, Trng i hc Bách khoa Tp. HCM, 11/10/2005

145
XÂY DNG MÔ HÌNH CU TRÚC 3 CHIU CHO CU TO DU KHÍ
DA VÀO TÀI LIU A CHN VÀ A VT LÝ GING KHOAN

CONSTRUCTING A 3-D STRUCTURAL MODEL OF AN OIL & GAS
PROSPECT BASED ON SEISMIC AND WELL LOG DATA


H Trng Long*, Bùi Th Thanh Huyn**, Keisuke Ushijima1***

* Khoa K thut a cht và Du khí, i hc Bách Khoa Tp.H Chí Minh, Vit Nam
** Department of Civil and Earth Resources Engineering, Kyoto University, Japan
*** Exploration Geophysics Laboratory, Graduate School of Engineering, Kyushu University, Japan


TÓM TT

S minh gii tài liu đa chn 3 chiu cho c hi đ đa ra các bn đ cu trúc di sâu mt đt.
Ngoài ra, s kt hp minh gii tài liu đa chn vi tài liu đa vt lý ging khoan s cung c
p thêm
nhng thông tin đáng tin cy đ thông hiu tt các cu trúc sâu, đc bit là xác đnh các đt gãy và các
đi nt n. Trong nghiên cu này, chúng tôi đã s dng mt k thut tính toán da vào máy tính gi là
“Mng Nron” đ tính đ rng ca va vi đ chính xác cao. Các giá tr đ rng có th thành lp đc
các bn đ phân b đ rng cho mt cu to du khí. Chúng tôi nhn thy rng, các đi có đ rng cao
gn lin vi các đt gãy và các đi nt n. Vì vy, s hiu chnh gia các bn đ phân b đ rng và
kt qu minh gii tài liu đa chn có th xác đnh các đt gãy và các đi nt n vi đ tin cy cao
hn. T đó, mô hình cu trúc 3 chiu s đc thành lp, th hin các hình dng cu trúc và ki
n to
cho vic đánh giá tim nng hydrocarbon. Chúng tôi đã s dng tài liu ca cu to du khí A2-VD 

thm lc đa phía Nam Vit Nam cho bài báo này. Các kt qu thu đc đã cung cp nhng thông tin
rt có giá tr cho vic nhn din v trí các ging khoan và khai thác, cng nh cho s phát trin ca cu
to này trong tng 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.
Hi ngh khoa hc và công ngh ln th 9, Trng i hc Bách khoa Tp. HCM, 11/10/2005

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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 (Pre-
Tertiary), 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
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 km
2
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 1 Location of the A2-VD prospect
(Modified from PV, 1998; JVPC, 2001)
Figure 2 Stratigraphy and wells correlation of
Block 15-2 (A2) (after JVPC, 2000)


Figure 3 Seismic data interpretation in selected sections
Hi ngh khoa hc và công ngh ln th 9, Trng i hc Bách khoa Tp. HCM, 11/10/2005

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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).

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
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.
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.

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)
0.02
0.04
0.07
0.09
0.00
0.11
0.00
1 9 17 25 33 41 49 57 65 70
Pattern #
Error
Training Data
RMS Error Vs. Pattern
for all Nodes
(b)
0.02
0.04
0.07
0.09
0.00
0.11
0.00
1 5 9 13 17 21 25 27
Pattern #

Error
Testing Data
RMS Error Vs. Pattern
for all Nodes
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
Density
NPHI
Sonic
LLS
MSFL
LLD


P
P
o
o
r
r
o
o
s
s
i
i
t
t
y
y

o
o
r
r














P
P
e
e
r
r
m
m
e
e

a
a
b
b
i
i
l
l
i
i
t
t
y
y
Hidden layer
Output layer
Connection
weights
Processing elements
(
PE
)

In
p
ut la
y
e
r

Hi ngh khoa hc và công ngh ln th 9, Trng i hc Bách khoa Tp. HCM, 11/10/2005

148
0
0.03
0.06
0.09
0.12
0.15
0.18
0.21
0.24
0.27
0.3
0.33
0.36
0.39
2165 2170 2175 2180 2185 2190 2195 2200 2205 2210
depth (m)
p
orosit
y

(
%
)
CORE porosity
NN porosity
LOG porosity

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 7 Porosity distribution combined
with seismic data to predict major faults
and fractured zones in the upper 100
meters of the basement



Figure 8. Structure of the top basement
corrected with porosity distribution in A2-
VD prospect
Figure 9. Structure of the top D horizon
correctedwith porosity distribution in A2-VD
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

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.
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Figure 10. 3-D view of faults and the top
basement in A2-VD prospect
Figure 11. 3-D relationship between the basement
high and the E sequence 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 India-
Eurasian 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.

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