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

Therefore, the correction between porosity distribution maps and results of seismic data interpretation can used to predict faults and fractured zones with higher reliability.. We used d

XÂY D NG MÔ HÌNH C U TRÚC 3 CHI U CHO C U T O D U KHÍ

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

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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

(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 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 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

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

Pattern #

RMS Error Vs Pattern for all Nodes

(b)

0.02 0.04 0.07 0.09

0.00 0.11

0.00

Pattern #

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

Porosi y or Perme i y y

Hidden layer Output layer

Connection weights

Processing elements (PE)

Input layer

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)

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

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

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 (JVPC) Report for the Block 15-2 prospect, southern offshore Vietnam (2001), pp

103-104

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

531-538

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