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研究生： 張勛和
Gary Schin Haar Deu
論文名稱： 人工智慧方法輔助天然氣儲集層歷史調諧： 以台灣 C 氣田為例
Artificial Intelligence Assisted History Matching of Natural Gas Reservoir: A Case Study of C-Gas Field, Taiwan
指導教授： 謝秉志
Hsieh, Bieng-Zih
學位類別： 碩士
Master
系所名稱： 工學院 - 資源工程學系
Department of Resources Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 中文
論文頁數： 101
中文關鍵詞： 人工智慧 、數值模擬 、歷史調諧 、台灣氣田
外文關鍵詞： Artificial Intelligence, Numerical Simulation, History Matching, Taiwan Gas Field
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    • 油氣資源開發時，瞭解油氣資源分佈至關重要。透過前期的探勘活動，雖然可以取得礦區部分的地質與流體資訊，但實際上礦區是一個立體的結構，若要探勘礦區上每個點，花費會過於高昂且不實際。因此可以透過數值模擬法建立簡化的數值網格模型來有效進行實際礦區的分析計算。然而，即使利用現地量測蒐集的參數建立數值模型後，還是有許多參數充滿不確定性。為了提高模型的可信度，可以透過歷史調諧來調整模型參數，使模型的生產反應可以與過去的歷史資料相擬合。
    進行歷史調諧時，通常需要經過大量的測試分析後才能從大量的參數組合找出最佳的調諧模型，過程非常耗時。但在人工智慧的輔助下，可以大量減少所需的時間。本研究透過 CMOST 人工智慧分析軟體，透過輻狀基底函數神經網路（Radial Basis Function Neural Network, RBFNN）協助挑選參數數值，並利用響應曲面法（Response surface methodology，RSM）建立代理模型。透過代理模型，可以更快速瞭解不同的參數變化對目標函數結果的影響。此外在調諧過程中，透過 CMG DECE 引擎及利用禁忌搜尋法（Tabu Search）模仿人類的記憶行為進行決策，可以在可能的參數解中用最短的時間找到最佳解。
    本研究在台灣 C 氣田的人工智慧輔助歷史調諧的分析中，利用人工智慧軟體有效從不同參數組合運算結果進行學習，避開較差的參數組合，並用較少的時間達成最佳的調諧結果。此外，透過所建立的代理模型，能提早在完成調諧前，了解參數組合對於目標結果的影響。

    Numerical grid model is commonly used for oil and gas field simulation to analyze and calculate for substituting actual field. However, even establishing a numerical model with parameters collected from in situ field measurements, loads of uncertain factors still remain, which will increase the uncertainty and reduce the reliability of numerical model. In order to improve the reliability of model, the parameters can be fitted through history matching process to match with previous historical data. The more parameters are fitted, the higher reliability of model is. The optimal fitted model can not only be used to observe field condition in the past, but predict future performance of the reservoir.

    Generally, traditional history matching process is a time-consuming work to find the optimized matching model from a variety of parameter combinations after numerous testing and analyses. In this study, an artificial intelligence analysis tool, CMOST, is used to improve history matching process of C-Gas Field in Taiwan with less time and labor. It is able to learn from calculated results of various parameter combinations to avoid generating worse matching cases, which takes less time to make optimal matching result in the history matching analysis. Moreover, with the proxy model established during the matching process, the influence of various input parameters on output results can be instantly observed before finishing all of matching process.

    中文摘要 I Extended Abstract II 誌謝 XIII 表目錄 XVII 圖目錄 XVIII 符號說明 XX 第壹章 緒論 1 1.1 前言 1 1.2 研究動機 2 1.3 研究目的 3 第貳章 文獻回顧 4 2.1 數值模擬 4 2.2 歷史調諧 6 2.2.1 歷史調諧案例 11 2.3 人工智慧輔助歷史調諧 11 2.3.1 響應曲面法（Response Surface Methodology，RSM） 11 2.3.2 代理模型 13 2.3.3 人工神經網路 14 2.3.4 人工智慧輔助儲集層模擬案例 17 2.4 禁忌搜尋法（Tabu Search） 18 2.5 小結 19 第參章 研究方法 20 3.1 CMG建模工具與模擬器 20 3.2 CMOST人工智慧軟體 21 3.3 敏感度分析 24 3.3.1 代理模型回歸方式 24 3.3.2 代理模型停止運算條件 25 3.4 歷史調諧 29 3.4.1 歷史調諧誤差（History Match Error） 29 3.4.2 CMG DECE 引擎 31 3.4.3 禁忌搜尋法（Tabu Search） 31 3.5 輻狀基底函數神經網路（Radial Basis Function Neural Network, RBFNN） 35 第肆章 研究流程與模型設計 38 4.1 研究流程 38 4.2 台灣 C 氣田 39 4.2.1 C氣田簡介 39 4.2.2 資料整理 42 4.3 數值模型 42 4.3.1 網格設計 44 4.3.2 地層參數 46 4.3.3 流體PVT 設定 48 4.3.4 相對滲透率設計 51 4.3.5 初始狀態假設 52 4.3.6 生產井串設計 54 第伍章 結果與討論 57 5.1 初始模型運算結果 57 5.2 敏感度分析 60 5.2.1 敏感度分析參數選擇 60 5.2.2 敏感度分析目標函數 63 5.2.3 敏感度分析引擎控制 64 5.2.4 敏感度分析結果 65 5.3 歷史調諧 75 5.3.1 歷史調諧參數 75 5.3.2 歷史調諧目標函數 76 5.3.3 歷史調諧引擎控制 77 5.3.4 歷史調諧結果 78 5.4 最佳調諧模型 82 5.5 共串井之不同產層供氣能力評估 89 第陸章 結論與建議 91 6.1 結論 91 6.2 建議 92 參考文獻 93

    1. Abido, M. A.（2002）. Optimal power flow using tabu search algorithm. Electric power components and systems, 30（5）, 469-483.
    2. Al-Hussainy, R., & Humphreys, N.（1996）. Reservoir management: principles and practices. Journal of Petroleum Technology, 48（12）, 1-129.
    3. Amini, S.（2015）. Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence.
    4. Amini, S., & Mohaghegh, S.（2019）. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Fluids, 4（3）, 126.
    5. April, J., Glover, F., Kelly, J., Laguna, M., Erdogan, M., Mudford, B., & Stegemeier, D.（2003, January）. Advanced optimization methodology in the oil and gas industry: the theory of scatter search techniques with simple examples. In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers.
    6. Aziz, K.（1989）. Ten golden rules for simulation engineers. J. Petrol. Technol, 41（11）, 1157.
    7. Baldwin, J. L., Otte, D. N., & Whealtley, C. L.（1989, January）. Computer emulation of human mental processes: Application of neural network simulators to problems in well log interpretation. In SPE annual technical conference and exhibition. Society of Petroleum Engineers.
    8. Baker, R. O., Chugh, S., Mcburney, C., & McKishnie, R.（2006）. History matching standards; quality control and risk analysis for simulation. In Canadian International Petroleum Conference. Petroleum Society of Canada.
    9. Basu, J. K., Bhattacharyya, D., & Kim, T. H.（2010）. Use of artificial neural network in pattern recognition. International journal of software engineering and its applications, 4（2）.
    10. Becerra, G. G., Modenesi, A., & Lisboa, E. A.（2012）. Uncertainty history matching and forecasting, a field case application. In SPE Latin America and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers.
    11. Bittencourt, A. C., & Horne, R. N.（1997）. Reservoir development and design optimization. In SPE annual technical conference and exhibition. Society of Petroleum Engineers.
    12. Box, G. E., & Wilson, K. B.（1992）. On the experimental attainment of optimum conditions. In Breakthroughs in statistics（pp. 270-310）. Springer, New York, NY.
    13. Carlson, M. R.（2003）. Practical reservoir simulation: using, assessing, and developing results. PennWell Books.
    14. Carreras, P. E., Turner, S. E., & Wilkinson, G. T.（2006）. Tahiti: development strategy assessment using design of experiments and response surface methods. In SPE Western Regional/AAPG Pacific Section/GSA Cordilleran Section Joint Meeting. Society of Petroleum Engineers.
    15. CMG, Computer Modelling Group.（2015）. Computer Modelling Group Manual. Calgary, Alberta Canada.
    16. CMG, Computer Modelling Group.（2016）. Computer Modelling Group Manual. Calgary, Alberta Canada.
    17. CMG, Computer Modelling Group.（2016）. CMOST User’s Guide, Version 2016. Calgary, Alberta Canada.
    18. Coats, K. H., Dempsey, J. R., & Henderson, J. H.（1970）. A new technique for determining reservoir description from field performance data. Society of Petroleum Engineers Journal, 10（01）, 66-74.
    19. Craig, F. F.（1971）. The reservoir engineering aspects of waterflooding（Vol. 3, pp. 12-44）. New York: HL Doherty Memorial Fund of AIME.
    20. Crichlow, H. B.（1977）. Modern reservoir engineering: a simulation approach. Prentice hall.
    21. Crombecq, K., Laermans, E., & Dhaene, T.（2011）. Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling. European Journal of Operational Research, 214（3）, 683-696.
    22. Dake, L. P.（1978）. Fundamentals of reservoir engineering. Amsterdam ; New York : Elsevier Scientific Pub. Co. ; New York : Distributors for the U.S. and Canada Elsevier North-Holland
    23. Duda, H., Hart, P., & David, G.（2001）. Stork, pattern classification. ed: John Wiley & Sons, 25, 1150-1157.
    24. Elvind, D., Asmund, H., & Rolf, V.（1992）. Maximum information at minimum cost: a North Sea field development study with an experimental design. Journal of Petroleum Technology, 44（12）, 1-350.
    25. Ertekin, T., Abou-Kassem, J. H., & King, G. R.（2001）. Basic applied reservoir simulation（Vol. 7）. Richardson, TX: Society of Petroleum Engineers.
    26. Esmaili, S.（2013）. Production History Matching and Forecasting of Shale Assets Using Pattern Recognition.
    27. Fanchi, J. R.（2005）. Principles of applied reservoir simulation. Elsevier.
    28. Glover, F.（1986）. Future paths for integer programming and links to artificial intelligence. Computers operations research, 13（5）, 533-549.
    29. Glover, F., & Taillard, E.（1993）. A user's guide to tabu search. Annals of operations research, 41（1）, 1-28.
    30. Guan, L., Du, Y., Johnson, S. G., & Choudhary, M.（2004, January）. Advances of Interwell Tracer Modelling in Petroleum Industry. In Canadian International Petroleum Conference. Petroleum Society of Canada.
    31. Jacquard, P.（1965）. Permeability distribution from field pressure data. Society of Petroleum Engineers Journal, 5（04）, 281-294.
    32. Jahns, H. O.（1966）. A rapid method for obtaining a two-dimensional reservoir description from well pressure response data. Society of Petroleum Engineers Journal, 6（04）, 315-327.
    33. Kam, P., Nadeem, M., Novlesky, A., Kumar, A., & Omatsone, E. N.（2015）. Reservoir characterization and history matching of the Horn River shale: An integrated geoscience and reservoir-simulation approach. Journal of Canadian Petroleum Technology, 54（06）, 475-488.
    34. Kriesel, D.（2007）. A brief introduction to neural networks. 2007. URL http://www. dkriesel. com.
    35. Kruger, W. D.（1961）. Determining areal permeability distribution by calculations. Journal of Petroleum Technology, 13（07）, 691-696.
    36. Kumar, K., & Thakur, G. S. M.（2012）. Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4（6）, 57.
    37. Larocque, M., Banton, O., & Razack, M.（2000）. Transient‐state history matching of a karst aquifer ground water flow model. Groundwater, 38（6）, 939-946.
    38. Lim, J. S., & Kim, J.（2004, January）. Reservoir porosity and permeability estimation from well logs using fuzzy logic and neural networks. In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers.
    39. Marsland, S.（2015）. Machine learning: an algorithmic perspective. CRC press.
    40. Mattax, C. C., & Dalton, R. L.（1990）. Reservoir Simulation（includes associated papers 21606 and 21620）. Journal of petroleum technology, 42（06）, 692-695.
    41. Mohaghegh, S.（2000）. Virtual-intelligence applications in petroleum engineering: part 3—fuzzy logic. Journal of petroleum technology, 52（11）, 82-87.
    42. Mohaghegh, S. D., Hafez, H. H., Gaskari, R., Haajizadeh, M., & Kenawy, M.（2006, January）. Uncertainty analysis of a giant oil field in the middle east using surrogate reservoir model. In Abu Dhabi International Petroleum Exhibition and Conference. Society of Petroleum Engineers.
    43. Mohaghegh, S. D.（2011）. Reservoir simulation and modeling based on artificial intelligence and data mining（AI&DM）. Journal of Natural Gas Science and Engineering, 3（6）, 697-705.
    44. Montgomery, D. C.（2017）. Design and analysis of experiments. John wiley & sons.
    45. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M.（2016）. Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons.
    46. Narayanan, K., White, C. D., Lake, L. W., & Willis, B. J.（1999, January）. Response surface methods for upscaling heterogeneous geologic models. In Proceedings of the 1999 15th Symposium on Reservoir Simulation.
    47. Oloso, M. A., Khoukhi, A., Abdulraheem, A., & Elshafei, M.（2009, October）. Prediction of crude oil viscosity and gas/oil ratio curves using recent advances to neural networks. In SPE/EAGE Reservoir Characterization & Simulation Conference（pp. cp-170）. European Association of Geoscientists & Engineers.
    48. Pennington, W. D., Acevedo, H., Haataja, J. I., & Minaeva, A.（2001）. Seismic time-lapse surprise at Teal South: That little neighbor reservoir is leaking!. The Leading Edge, 20（10）, 1172-1175.
    49. Pruess, K., Weres, O., Schroeder, R., Marconcini, R., & Neri, G.（1980）. History Match Simulation of Serrazzano Geothermal Reservoir.
    50. Purwar, S., Jablonowski, C. J., & Nguyen, Q. P.（2010, January）. A Method For Integrating Response Surfaces Into Optimization Models With Real Options: A Case in Gas Flooding. In SPE Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers.
    51. Rodriguez, A. A., Klie, H., Wheeler, M. F., & Banchs, R.（2007, January）. Assessing Multiple Resolution Scales in History Matching With Metamodels. In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers.
    52. Saleri, N. G., Toronyi, R. M., & Snyder, D. E.（1992）. Data and data hierarchy. Journal of Petroleum Technology, 44（12）, 1-286.
    53. Sampaio, T. P., Ferreira Filho, V. J. M., & Neto, A. D. S.（2009, January）. An application of feed forward neural network as nonlinear proxies for use during the history matching phase. In Latin American and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers.
    54. Schlumberger. "ECLIPSE Reservoir Simulator." 2014.
    55. Schiozer, D. J.（1999, January）. Use of Reservoir simulation, Parallel computing and Optimization techniques to accelerate History Matching and Reservoir management decisions. In Latin American and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers.
    56. Seide, F., Li, G., & Yu, D.（2011）. Conversational speech transcription using context-dependent deep neural networks. In Twelfth annual conference of the international speech communication association.
    57. Shahkarami, A.（2012）. Artificial intelligence assisted history matching--proof of concept.
    58. Shahkarami, A.（2014）. Assisted history matching using pattern recognition technology. West Virginia University.
    59. Shahkarami, A., Mohaghegh, S. D., & Hajizadeh, Y.（2018）. Assisted history matching using pattern recognition technology. International Journal of Oil, Gas and Coal Technology, 17（4）, 412-442.
    60. Slotte, P. A., & Smorgrav, E.（2008, January）. Response surface methodology approach for history matching and uncertainty assessment of reservoir simulation models. In Europec/EAGE Conference and Exhibition. Society of Petroleum Engineers.
    61. Solorzano, L. N., Arredondo, S. E., & Sergio, E.（1973, January）. Method for Automatic History Matching of Reservoir Simulation Models. In Fall Meeting of the Society of Petroleum Engineers of AIME. Society of Petroleum Engineers.
    62. SPE.（2001）. Guidelines for the Evaluation of Petroleum Reserves and Resources
    63. Thomas, G. W.（1982）. Hydrocarbon Reservoir Simulation, Int. Human Res. Dev. Corp., Boston.
    64. Watts, J.（1997）. Reservoir Simulation: Past, Present, and Future. SPE Computer Applications , 171-176.
    65. Wiggins, M. L., & Startzman, R. A.（1990, January）. An approach to reservoir management. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
    66. Yang, C., Nghiem, L. X., Card, C., & Bremeier, M.（2007, January）. Reservoir model uncertainty quantification through computer-assisted history matching. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
    67. 王玲絲（2012）。苗栗地區儲集層孔隙率與滲透率特性評估。國立中央大學應用地質研究所碩士論文。
    68. 台灣油礦探勘總處（1986）。出磺坑構造地質剖面勘查指南。地質7卷，2期，83-90。
    69. 何信昌，1994，台灣地質圖說明書─苗栗：經濟部中央地質調查所，第十二號，69 頁周佳儀（2008）。出磺坑背斜構造發育之三角剪切模型。國立中央大學應用地質研究所碩士論文。
    70. 楊世閎（2016）。台灣西北部出磺坑背斜石油系統研究。國立中央大學應用地質研究所碩士論文。
    71. 魏聲焜，吳光興，賴承義，徐進興（1993）。 出磺坑構造北端之新鑽探目標。鑛冶: 中國鑛冶工程學會會刊, 37（2）, 90-99。
    72. 藍敏男（2004）。出磺坑構造地質與水系發育之相關性研究。國立中央大學，應用地質研究所碩士論文。
    73. 謝秉志（2020）。出磺坑氣田主要產層砂體特性分析與油氣潛能評估委託研究（台灣中油公司股份有限公司探採研究所委託研究）。台南，國立成功大學資源工程學系。

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