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Zastosowanie sztucznej inteligencji w inżynierii złożowej i modelowaniu procesu eksploatacji złóż węglowodorowych

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Warianty tytułu
EN
Application of artificial intelligence in reservoir engineering and modeling of hydrocarbon exploitation processes
Języki publikacji
PL
Abstrakty
PL
Rozwojowi technik komputerowych w ostatnich pięćdziesięciu latach towarzyszy gwałtowny rozwój nowej dziedziny wiedzy jaką jest sztuczna inteligencja. Inżynieria złożowa korzysta z technik sztucznej inteligencji (SI) od lat siedemdziesiątych ubiegłego wieku. Artykuł opisuje zastosowania SI w geologii, geofizyce, inżynierii złożowej i eksploatacji złóż węglowodorowych. Omówiono możliwości stosowania SI do tworzenia tzw. „zastępczych” modeli złożowych i możliwości wykorzystywania ich w szacowaniu zasobów wydobywalnych metodami probabilistycznymi, wykorzystującymi zarówno podstawowe dane geologiczne (reservoir quality) ale również metody wydobycia i stymulacji (completion quality) dla złóż niekonwencjonalnych gazu ziemnego w złożach mułowcowo-łupkowych. Istotą takiego modelowania jest możliwość stosowania oszacowań zasobów technicznie wydobywalnych w strefie oddziaływania odwiertów i odpowiedniego planowania rozwiercania i udostępniania odwiertów. Przedstawione techniki mogą być adaptowane do złóż metanu w pokładach węgla i złóż piaskowcowych o niskich parametrach (tzw. złóż „tight”), po uwzględnieniu specyfiki tych złóż i specyfiki ich udostępnienia otworami pionowymi, kierunkowymi, poziomymi i otworami typu „u-shape”.
EN
The development of computer techniques in the last fifty years is accompanied by the rapid growth of artificial intelligence in technology. Reservoir engineering has been using artificial intelligence (AI) techniques since the 1970s. The article describes the application of artificial intelligence in geology, geophysics, reservoir engineering, and hydrocarbon reservoir exploitation processes. The possibilities of using artificial intelligence to create the so-called "surrogate" reservoir models and the possibility of using them in estimating recoverable resources by probabilistic methods for unconventional natural gas shale reservoirs is presented. Using these techniques it is the possibility of estimates of technically recoverable resources in the stimulated reservoir volume and proper planning of drillings. The presented methods can be adapted to coal bed methane reservoirs and the tight gas reservoirs, taking into account the specificity of these rocks.
Twórcy
  • Wydział Wiertnictwa, Nafty i Gazu AGH w Krakowie
autor
  • Wydział Wiertnictwa, Nafty i Gazu AGH w Krakowie
  • Wydział Wiertnictwa, Nafty i Gazu AGH w Krakowie
autor
  • Wydział Wiertnictwa, Nafty i Gazu AGH w Krakowie
Bibliografia
  • 1. Ahmed, T., Link, C., Porter, K., Wideman, C., Himmer, P., & Braun, J.: Application of Neural Network Parameter Prediction in Reservoir Characterization and Simulation - A Case History: The Rabbit Hills Field. Latin American and Caribbean Petroleum Engineering Conference. Rio de Janeiro, Brazil, 1997.
  • 2. Al-Fattah S. M., Startzman R. A.: Predicting Natural Gas Production Using Artificial Neural Network, SPE 68593, 2001.
  • 3. Ali, J. K.: Neural Networks: A New Tool for the Petroleum Industry? European Petroleum Computer Conference. Aberdeen, United Kingdom: Society of Petroleum Engineers, 1994.
  • 4. Al-Kaabi, A. U., & Lee, W. J.: An Artificial Neural Network Approach To Identify the Well Test Interpretation Model: Applications. SPE Annual Technical Conference and Exhibition. New Orleans, Louisiana: Society of Petroleum Engineers, 1990.
  • 5. Aminzadeh, F., & de Groot, P.: A Neural Networks Based Seismic Object Detection Technique. SEG Annual Meeting. Houston, Texas, 2005.
  • 6. Arps, J. J. Analysis of Decline Curves, 1945
  • 7. Athichanagorn, S., & Horne, R. N.: Automatic Parameter Estimation From Well Test Data Using Artificial Neural Network. SPE Annual Technical Conference and Exhibition. Dallas, Texas: Society of Petroleum Engineers, 1995.
  • 8. Baldwin, J. L., Otte, D. N., & Whealtley, C. L.: Computer Emulation of Human Mental Processes: Application of Neural Network Simulators to Problems in Well Log Interpretation. SPE Annual Technical Conference and Exhibition. San Antonio, Texas, 1989.
  • 9. Barbacki J., Ł. Klimkowski, S. Nagy, J. Siemek, 107, The use of artificial neural networks for the prediction of gas production and estimated recoverable resources from unconventional gas reservoirs, Przemysł Chemiczny 96/5 1009-1016
  • 10. Bellman R., An Introduction to Artificial Intelligence, 1978, Boyd & Fraser Pub. Co.
  • 11. Bieker, H. P., Slupphaug, O., & Johansen, T. A. (2007). Real-Time Production Optimization of Oil and Gas Production Systems: A Technology Survey. Society of Petroleum Engineers. doi:10.2118/99446-PA
  • 12. Boulis, A. S., A New Series of Rate Decline Relations Based on the Diagnosis of Rate-Time Data, TAMU 2009
  • 13. Bravo C., Saputelli L., Ricas F., Perez A. G., Nikolaou M., Zangl G., de Guzman N., Mohaghegh S., Nunez G., State of the art of Artificial Intelligence and Predicitive Analytics in the E&P industry: a technology survey, SPE 150314, 2012
  • 14. Briones, M. F., Rojas, G., Moreno, J., & Martinez, E. R.: Application of Neural Networks in the Prediction of Reservoir Hydrocarbon Mixture Composition From Production Data. SPE Annual Technical Conference and Exhibition. New Orleans, Louisiana: Society of Petroleum Engineers, 1994.
  • 15. Bybee, K. (2011). Prediction of Uncertainty in Exploration of Unconventional Gas Reservoirs. Society of Petroleum Engineers. doi:10.2118/0711-0099-JPT
  • 16. Chan, P. B. et al., Using the SPE/WPC/AAPG/SPEE/SEG PRMS To Evaluate Unconventional Resources, Society of Petroleum Engineers 2009.
  • 17. Cheng-Dang, Z., Wu, S.-L., Mo, Z. C., Zhu, D. H., & Xu, Z. V.: Direct Identification of Hydrocarbon From Well Logs: A Neural Network Interpretation Approach. Annual Technical Meeting. Calgary, Alberta: Petroleum Society of Canada, 1994.
  • 18. Clark, A. J., L. W. Lake, et al., Production Forecasting with Logistic Growth Models, Society of Petroleum Engineers, 2011
  • 19. Clarkson, C. R., et al., Production Data Analysis of Fractured and Horizontal CBM Wells, Society of Petroleum Engineers, 2009
  • 20. Dahaghi, A. K., & Mohaghegh, S. D. (2009). Intelligent Top-Down Reservoir Modeling of New Albany Shale. Society of Petroleum Engineers. doi:10.2118/125859-MS
  • 21. Dahaghi, A. K., Mohaghegh, S. D., & Khazaeni, Y. (2010). New Insight Into Integrated Reservoir Management Using Top-Down, Intelligent Reservoir Modeling Technique: Application to a Giant and Complex Oil Field in the Middle East. Society of Petroleum Engineers. doi:10.2118/132621-MS
  • 22. David, H. J.: Seismic Attribute Calibration Using Neural Networks. SEG Annual Meeting. Washington, DC: Society of Exploration Geophysicists, 1993.
  • 23. Denney, D. (2010). Insight Into Integrated Reservoir Management Using a Top-Down Intelligent-Reservoir-Modeling Technique: Application to a Giant and Complex Oil Field. Society of Petroleum Engineers. doi:10.2118/1210-0063-JPT
  • 24. Denney, D. (2012). Improve Unconventional-Reservoir Completion and Stimulation Effectiveness. Society of Petroleum Engineers. doi:10.2118/1012-0115-JPT
  • 25. Dong, Z., S. A. Holditch, D. A. McVay, et al., Probabilistic assessment of world recoverable shale-gas resources, SPE Economics & Management 2015, 7(2) SPE-167768-PA
  • 26. Dong, Z., S. A. Holditch, D. A. McVay, Resource evaluation for shale gas reservoirs, SPE Economics & Management 2013, 5(1):5-16 SPE-152066-PA
  • 27. Dong, Z., S. A. Holditch, W. B. Ayers, et al., Probabilistic estimate of global coalbed methane recoverable resource, SPE Economics & Management 2014, 7(2) SPE-169006-PA
  • 28. Dong, Z., S. A. Holditch, W. B. Ayers, Probabilistic evaluation of global recoverable tight gas resources, SPE Economics & Management 2013, 7(3) SPE-169006-PA
  • 29. Duong, A. N. An Unconventional Rate Decline Approach for Tight and Fracture-Dominated Gas Wells, Society of Petroleum Engineers, 2010
  • 30. Ebadi M., M. Ali Ahmadi, S. Gerami & R. Askarinezhad. Article: Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study. International Journal of Computer Applications 39(8):23-28, February 2012
  • 31. Ershaghi, I., Li, X., Hassibi, M., & Shikari, Y.: A Robust Neural Network Model for Pattern Recognition of Pressure Transient Test Data. SPE Annual Technical Conference and Exhibition. Houston, Texas: Society of Petroleum Engineers, 1993.
  • 32. Gharbi, R., & Elsharkawy, A.: Neural Network Model for Estimating The PVT Properties of Middle East Crude Oils. Middle East Oil Show and Conference. Bahrain, 1997.
  • 33. Gibbs T.H.: Horizontal Well Placement Optimization In Gas Reservoirs Using Genetic Algorithms, Texas A&M University, 2010.
  • 34. Goldberg D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Professional, 1989.
  • 35. Gomez, Y., Khazaeni, Y., Mohaghegh, S. D., & Gaskari, R. (2009). Top Down Intelligent Reservoir Modeling. Society of Petroleum Engineers. doi:10.2118/124204-MS
  • 36. Goyagoler B., Horne R.N.: Uncertainty Assessment of Well-Placement Optimization, 76th SPE Annual Technical Conference and Exhibition, SPE 87663, 2001.
  • 37. Haghighat, S. A., Mohaghegh, S. D., Gholami, V., & Moreno, D. (2014). Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling. Society of Petroleum Engineers. doi:10.2118/169573-MS
  • 38. Horne R., Guyaguler B.: Optimization of Well Placement, Journal of Energy Resources Technology, Vol. 122, pp. 4-70, 1999.
  • 39. IEA, World Energy Outlook 2011: Golden Age of Natural Gas?, Report of IEA 2011
  • 40. Jong-Se, L., & Jungwhan, K.: Reservoir Porosity and Permeability Estimation from Well Logs using Fuzzy Logic and Neural Networks. SPE Asia Pacific Oil and Gas Conference and Exhibition. Perth, Australia, 2004.
  • 41. Kalam, M., Al-Alawi, S., & Al-Mukheini, M.: Assessment of Formation Damage Using Artificial Neural Networks. SPE Formation Damage Control Symposium. Lafayette, Louisiana, 1996.
  • 42. Kaydani H., A.N. AliMohebbi, M. Eftekhari, Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm, Journal of Petroleum Science and Engineering, 123 July 2014.
  • 43. Kelkar, M. G., Bonney, K. E., & Bonney, P. A. (2010). Prediction of Uncertainty in Exploration of Unconventional Gas Reservoirs. Society of Petroleum Engineers. doi:10.2118/133299-MS
  • 44. Key, S., Nielsen, H., Signer, C., Sønneland, L., Waagbø, K., & H. Veire, H.: Fault And Fracture Classification Using Artifical Neural Networks - Case Study From the Ekofisk Field. SEG Annual Meeting. Dallas, Texas, 1997.
  • 45. Klimkowski Ł., Jednoodwiertowy model zastępczy oparty na elementach sztucznej inteligencji dla symulacji eksploatacji gazu z łupków, rozprawa doktorska, AGH, 2016
  • 46. Klimkowski L., Nagy S., Key factors in shale gas modeling and simulation, Archives of Mining, 2014 vol. 59 no. 4, s. 987-1004
  • 47. Klimkowski L., Nagy S., J. Barbacki, 2016 Zastosowanie sztucznych sieci neuronowych do modelowania eksploatacji gazu ze złóż łupkowych, Instytut Nafty i Gazu - Państwowy Instytut Badawczy. - Kraków : INiG-PIB, 2016
  • 48. Kulga I.B.: Development Of An Artificial Neural Network For Hydraulically Fractured Horizontal Wells In Tight Gas Sands, Pennsylvania State University, 2010.
  • 49. Kwaśnicka H.: Ewolucyjne projektowanie sieci neuronowych, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, 2007.
  • 50. Maity, D., & Aminzadeh, F. (2012). Reservoir Characterization of an Unconventional Reservoir by Integrating Microseismic, Seismic, and Well Log Data. Society of Petroleum Engineers. doi:10.2118/154339-MS
  • 51. Maity, D., Aminzadeh, F., & Karrenbach, M. (2012). Novel Hybrid ANN Autopicker for Hydrofrac Data: A Comparative Study. Society of Exploration Geophysicists.
  • 52. Masoud, N.: Neural Network Knowledge-Based Modeling of Rock Properties Based on Well Log Databases. SPE Western Regional Meeting. Bakersfield, California, 1998.
  • 53. McCulloch W.S., W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, Vol. 5, 1943, p. 115-133
  • 54. McCulloch, W.S. and Pitts, W.: “A Logical Calculus of Ideas Immanent in Nervous Activity”, Bull. Mathematical Biophysics (1943) 5, 115.
  • 55. Mohaghegh S.D., Modavi A., Hafez H.H., et. al.: Development of Surrogate reservoir models (SRM) for fast track analysis of complex reservoirs, SPE 99667, 2006.
  • 56. Mohaghegh S.D., Reservoir simulation and modeling based on Artificial Inteligence and Data Mining (AI&DM), JNGSE, Elsevier B.V., 2011
  • 57. Mohaghegh S.D., Virtual Intelligence applications in petroleum engineering: Part I - Artificial Neural Network, Part II - Evolutionary Computing, Part III - Fuzzy Logic, SPE Distingueshed Authors Series, 2000
  • 58. Mohaghegh, S., Virtual-Intelligence Applications in Petroleum Engineering: Part 1 - Artificial Neural Networks, Journal of Petroleum Technology, Sept. 2000a
  • 59. Mohaghegh, S., Virtual-Intelligence Applications in Petroleum Engineering: Part 2 - Evolutionary Computing, Journal of Petroleum Technology, Oct. 2000b
  • 60. Mohaghegh, S., Virtual-Intelligence Applications in Petroleum Engineering: Part 3 - Fuzzy Logic, Journal of Petroleum Technology, Nov. 2000c
  • 61. Mohaghegh, S. D. (2016). Top-Down Modeling: A Shift in Building Full-Field Models for Mature Fields. Society of Petroleum Engineers. doi:10.2118/0716-0022-JPT
  • 62. Mohaghegh, S. D., Liu, J., Gaskari, R., Maysami, M., & Olukoko, O.: Application of Well-Base Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study. SPE Western Regional Meeting. Bakersfield, California, USA: Society of Petroleum Engineers, 2012.
  • 63. Mohaghegh., S., Arefi, R., Ameri., S., & Rose., D.: Design and Development of An Artificial Neural Network for Estimation of Formation Permeability. SPE Computer Applications, 1995.
  • 64. Montes G., Bartolome P., Udias A.L.: The Use of Gentic Alghoritms in Well Placemet Optimization, SPE 69439, 2001.
  • 65. Nagy et. al. (red), Zagadnienia eksploatacji niekonwencjonalnych złóż gazu ziemnego w skałach mułowcowo-łupkowych; autorzy: J. Barbacki, K. Dąbrowski, Ł. Kaczmarek, Ł. Klimkowski, Sz. Kuczyński, P. Łętkowski, M. Maksimczuk, S. Nagy, K. Polański, J. Siemek, J. Skibiński, R. Smulski, W. Szott, T. Wejrzanowski, T. Włodek, Wyd. AGH, 2019
  • 66. Nagy, S., Cz. Rybicki, Cz. Ropa, M. Szafran, S. Szafran, J. Blicharski, R. Smulski, Eksploatacja złóż gazu i przygotowanie gazu do transportu, Vademecum gazownika: pr. zbiorowa. T. 1, Podstawy gazownictwa ziemnego, red. T. 1 S. Nagy, 2014
  • 67. Nikravesh M., Aminzadeh F., Past, present and future intelligent reservoir characterization, JPSE, Elsevier, 2001
  • 68. Nikravesh, M., Kovscek, A., Johnston, R., & Patzek, T.: Prediction of Formation Damage During Fluid Injection into Fractured, Low Permeability Reservoirs via Neural Networks. SPE Formation Damage Control Symposium. Lafayette, Louisiana: Society of Petroleum Engineers, 1996.
  • 69. Old S., PRISE: Petroleum Resource Investigation Summary and Evaluation, TAMU 2008
  • 70. Oloso, M. A., Khoukhi, A., Abdulraheem, A., & Elshafei, M.: Prediction of Crude Oil Viscosity and Gas/Oil Ratio Curves Using Recent Advances to Neural Networks. SPE/EAGE Reservoir Characterization and Simulation Conference. Abu Dhabi, UAE, 2009.
  • 71. Osman, E. A., Abdel-Wahhab, O. A., & Al-Marhoun, M. A.: Prediction of Oil PVT Properties Using Neural Networks. SPE Middle East Oil Show. Bahrain, 2001.
  • 72. Palacio, J. C., T. A. Blasingame, Decline-Curve Analysis with Type Curves - Analysis of Gas Well Production Data, Society of Petroleum Engineers, 1993
  • 73. Patzek T.W., et al., Gas production in the Barnett Shale obeys a simple scaling theory, PNAS 110(49): 19731-19736, 2013
  • 74. PRMS 2018, https://www.spe.org/en/industry/petroleum-resources-management-system-2018/ dostęp 5 lipca 2019 r.
  • 75. Rajput V., Basel E.D.K., Ertekin T.: A Production Performance Prediction and Field Development Tool for Coalbed Methane Reservoirs: A Proxy Modeling Approach, SPE 169588, 2014.
  • 76. Ramgulam, A., Ertekin, T., & Flemings, P. B. (2007). An Artificial Neural Network Utility for the Optimization of History Matching Process. Society of Petroleum Engineers. doi:10.2118/107468-MS
  • 77. Rodriguez, A. A., Klie, H., Wheeler, M. F., & Banchs, R. Assessing Multiple Resolution Scales in History Matching With Metamodels. SPE Reservoir Simulation Symposium. Houston, Texas, U.S.A., 2007.
  • 78. Rosenblatt, F.: “The Perceptron: Probabilistic Model for Information Storage and Organization in the Brain”, Psychol. Rev. (1958) 65, 386.
  • 79. Roth, G., & Tarantoia, A.: Inversion of Seismic Waveforms Using Neural Networks. SEG Annual Meeting. New Orleans, Louisiana: Society of Exploration Geophysicists, 1992.
  • 80. Russell, S. & P. Norvig, Artificial Intelligence: A Modern Approach, 2nd edition, Prentice Hall, 2003
  • 81. Sadiq, T., & I.S. Nashawi: Using Neural Networks for Prediction of Formation Fracture Gradie. SPE/CIM International Conference on Horizontal Well Technology. Calgary, Alberta, Canada, 2000.
  • 82. Saeed N., et al. “Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoir”, 2009, 90(3), 452-457
  • 83. Sampaio, T., Filho, V. M., & Neto, A. d.: An Application of Feed Forward Neural Network as Nonlinear Proxies for Use During the History Matching Phase. Latin American and Caribbean Petroleum Engineering Conference. Cartagena de Indias, Colombia, 2009.
  • 84. Shahkarami, A., Mohaghegh, S. D., Gholami, V., & Haghighat, S. A. (2014). Artificial Intelligence (AI) Assisted History Matching. Society of Petroleum Engineers. doi:10.2118/169507-MS
  • 85. Siemek, J., S. Nagy, Estimation of uncertainties in gas-condensate systems reserves by Monte Carlo simulation, 2004, Acta Montanistica Slovaca, Vol. 9, Iss. 3, p. 289-293
  • 86. Singh, V., Painuly, P. K., Srivastava, A. K., Tiwary, D. N., & Chandra, M.: Neural Networks And Their Applications In Lithostratigraphic Interpretation of Seismic Data For Reservoir Characterization. World Petroleum Congress. Madrid, Spain, 2008.
  • 87. Stoisits, R. F., Crawford, K. D., MacAllister, D. J., McCormack, M. D., Lawal, A. S., & Ogbe, D. O. (1999). Production Optimization at the Kuparuk River Field Utilizing Neural Networks and Genetic Algorithms. Society of Petroleum Engineers. doi:10.2118/52177-MS
  • 88. Sultanp, M. A., & Al-Kaabi, A. U.: Application of Neural Network to the Determination of Well-Test Interpretation Model for Horizontal Wells. SPE Asia Pacific Oil and Gas Conference and Exhibition. Melbourne, Australia, 2002.
  • 89. Szafran M., S. Szafran [w:] Vademecum gazownika t. 1 (red. S. Nagy), Wydawnictwo SITPNiG, Kraków 2014, 301.
  • 90. Tadeusiewicz R., Neural Networks In Mining Sciences - General Overview, Arch. Min. Sci., Vol. 60 (2015), No. 4, p. 971-984.
  • 91. Tadeusiewicz R., Sieci neuronowe, Akademicka Oficyna Wydaw. RM, Warszawa 1993
  • 92. Thagard P., Computational Philosophy of Science, 1993, MIT press
  • 93. Thomas, L. A., & Pointe, P. R.: Conductive fracture identification using neural networks. The 35th U.S. Symposium on Roc Mechanics (USRMS). Reno, NV, 1995.
  • 94. Wang, P., Litvak, M., & Aziz, K. (2002). Optimization of Production Operations in Petroleum Fields. Society of Petroleum Engineers. doi:10.2118/77658-MS
  • 95. Wei, C., Wang, Y., Ding, Y., Li, Y., Shi, J., Liu, S., … Zhang, Q. (2017). Uncertainty Assessment in Production Forecasting and Optimization for a Giant Multi-Layered Sandstone Reservoir Using Optimized Artificial Neural Network Technology. Society of Petroleum Engineers. doi:10.2118/186069-MS
  • 96. Yang, H., & Huang, K.-Y.: Hybrid Neural Network For Seismic Pattern Recognition. SEG Annual Meeting. Houston, Texas: Society of Exploration Geophysicists, 1991.
  • 97. Zangl, G., Giovannoli, M., & Stundner, M.: Application of Artificial Intelligence in Gas Storage Management. SPE Europec/EAGE Annual Conference and Exhibition. Vienna, Austria, 2006, SPE 100133.
  • 98. Nagy S., J. Siemek, Confined Phase Envelope of Gas-Condensate Systems In Shale Rocks, Arch. Mining Sci. 2014, 59, nr 4, 1005.
  • 99. Siemek, J., S. Nagy, Z. Kolenda [w:] Wybrane problemy energetyki i ochrony środowiska (red. M. Banaś), AGH, Kraków 2015.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9ba43cd8-3208-4efa-8ce5-27c2c4dc8d5f
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