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 

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

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.

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

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


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

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.

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. 41-
42.
3. Japan Vietnam Petroleum Company

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 Back-
Propagation. John Wiley & Sons, Inc
(1994), pp. 115-127.