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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 --------------------------------------------------------------------------------------------------------------------------- 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. 145 Hội nghị khoa học và công nghệ lần thứ 9, Trường Đại học Bách khoa Tp. HCM, 11/10/2005 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 study area as presented in Figure 2 (JVPC, 2000 and 2001). 2. THREE-DIMENSIONAL (3-D) SEISMIC DATA INTERPRETATION OF A2-VD PROSPECT basin that was formed during Cenozoic Era In this research, we conducted seismic 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 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 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 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 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. 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) Figure 2 Stratigraphy and wells correlation of Block 15-2 (A2) (after JVPC, 2000) Figure 3 Seismic data interpretation in selected sections 146 Hội nghị khoa học và công nghệ lần thứ 9, Trường Đại học Bách khoa Tp. HCM, 11/10/2005 3. POROSITY DISTRIBUTIONS USING NEURAL NETWORK process of NN, we applied the most common 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 and MSFL). batch back-propagation, delta-bar-delta, resilient propagation (RPROP) and quick propagation (Werbos, 1994). The most successful paradigm NPHI Density Sonic LLS Processing elements (PE) Connection weights Porosity or Permeability Output layer 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 LLD Hidden layer predictions with core predictions and log data are displayed in 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 (a) 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. (b) RMS Error Vs. Pattern 0.11 for all Nodes RMS Error Vs. Pattern 0.11 for all Nodes 0.09 0.09 0.07 0.07 0.04 0.04 0.02 0.02 0.00 1 9 17 25 33 41 49 57 65 70 Pattern # 0.00 1 5 9 13 17 21 25 27 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 147 Hội nghị khoa học và công nghệ lần thứ 9, Trường Đại học Bách khoa Tp. HCM, 11/10/2005 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 2170 2175 2180 2185 2190 2195 2200 2205 2210 CORE porosity NN porosity LOG porosity 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 A2-VD prospect 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 4. CONSTRUCTION 3-D STRUCTURAL MODELS OF A2-VD PROSPECT the top of the basement. The faults strongly segmented the basement with the location is 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 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. 148 Hội nghị khoa học và công nghệ lần thứ 9, Trường Đại học Bách khoa Tp. HCM, 11/10/2005 Figure 10. 3-D view of faults and the top basement in A2-VD prospect 5. CONCLUSIONS By using neural network, reliability porosity Figure 11. 3-D relationship between the basement high and the E sequence in A2-VD prospect REFERENCES values can be predicted directly from well log 1. Badley, M. E.,. Practical seismic data. And then, porosity distribution maps were interpretation. International Human combined with seismic data interpretation to Resources Development Corporation, predict faults and fractures zones. Hence, 3-D Boston, USA (1985). structural models were constructed reliably. 2. Japan Vietnam Petroleum Company The 3-D structure models and structural (JVPC). Report for the Block 15-2 prospect, 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. 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. 149 ... - tailieumienphi.vn
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