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Engineering >> 2022, Volume 18, Issue 11 doi: 10.1016/j.eng.2022.06.009

Intelligent Petroleum Engineering

Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

Received: 2021-12-09 Revised: 2022-05-01 Accepted: 2022-06-17 Available online: 2022-07-19

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Abstract

Data-driven approaches and AI algorithms are promising enough to be relied on even more than physics-based methods; their main feed is data which is the fundamental element of each phenomenon. These algorithms learn from data and unveil unseen patterns out of it. The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology. As the oil and gas industry is in the transition phase to oilfield digitization, there has been an increased drive to integrate data-driven modeling and machine learning algorithms in different petroleum engineering challenges. ML has been widely used in different areas of the industry. Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry; however, lack of two main features is noticeable. Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable. Attention must be given to data itself and the way it is classified and stored. Although there are sheer volumes of data coming from different disciplines, they reside in departmental silos and are not accessible by consumers. In order to derive as much insight as possible out of data, the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.
 

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References

[ 1 ] Noshi CI, Schubert JJ. The role of machine learning in drilling operations; a review. In: Proceedings of the SPE/AAPG Eastern Regional Meeting; 2018 Oct 7–11; Pittsburgh, PA, USA. Richardson: OnePetro; 2018. link1

[ 2 ] Solomatine DP, Ostfeld A. Data-driven modelling: some past experiences and new approaches. J Hydroinform 2008;10(1):3–22. link1

[ 3 ] Dubois D, Hájek P, Prade H. Knowledge-driven versus data-driven logics. J Logic Lang Inform 2000;9(1):65–89. link1

[ 4 ] Schwarzacher W. The semi-Markov process as a general sedimentation model. In: Merriam DF, editor. Mathematical models of sedimentary processes: an international symposium. Boston: Springer; 1972. p. 247–68. link1

[ 5 ] Matalas NC. Mathematical assessment of synthetic hydrology. Water Resour Res 1967;3(4):937–45. link1

[ 6 ] Agterberg FP. Markov schemes for multivariate well data. In: Proceedings of the International, Symposium on the Application of Computers and Operations Research in the Mineral Industry. Pennsylvania: Pennsylvania State University; 1966. link1

[ 7 ] Preston FW, Henderson J. Fourier series characterization of cyclic sediments for stratigraphic correlation. In: Merriam DF, editor. Proceedings of the Symposium on Cyclic Sedimentation. Kansas: Geological Survey; 1964. p. 415–25. link1

[ 8 ] Newendorp PD. Decision analysis for petroleum exploration. Tulsa: PennWell Books; 1976. link1

[ 9 ] Reddy RKT, Bonham-Carter GF. A decision-tree approach to mineral potential mapping in Snow Lake Area. Manitoba Can J Rem Sens 1991;17(2):191–200. link1

[10] Zhao X, Mendel JM. Minimum-variance deconvolution using artificial neural networks. In: SEG technical program expanded abstracts 1988. Houston: SEG Library; 1988. link1

[11] McCormack MD. Neural computing in geophysics. Lead Edge 1991;10(1):11–5. link1

[12] Waldeland AU, Solberg AHSS. Salt classification using deep learning. In: Proceedings of the 79th EAGE conference and exhibition 2017; 2017 Jun 12– 15; Paris, France; 2017. link1

[13] Araya-Polo M, Dahlke T, Frogner C, Zhang C, Poggio T, Hohl D. Automated fault detection without seismic processing. Lead Edge 2017;36(3):208–14. link1

[14] Guitton A. 3D convolutional neural networks for fault interpretation. In: Proceedings of the 80th EAGE conference and exhibition 2018; 2018 Jun 11–14; Copenhagen, Denmark; 2018. link1

[15] Purves S, Alaei B, Larsen E. Bootstrapping machine-learning based seismic fault interpretation. In: Proceedings of the AAPG Annual Convention and Exhibition; 2018 May 20–23; Salt Lake City, UT, USA; 2018. link1

[16] Wu H, Zhang B. Semi-automated seismic horizon interpretation using encoder-decoder convolutional neural network. In: SEG technical program expanded abstracts 2019. Houston: SEG Library; 2019. link1

[17] Chevitarese DS, Szwarcman D, Gama e Silva RM, Vital Brazil E. Deep learning applied to seismic facies classification: a methodology for training. In: Proceedings of the European Association of Geoscientists & Engineers, Saint Petersburg 2018; 2018 Apr 9–12; Saint Petersburg; 2018. p. 1–5.

[18] Dramsch JS, Lüthje M. Deep-learning seismic facies on state-of-the-art CNN architectures. In: SEG technical program expanded abstracts 2018. Houston: SEG Library; 2018. link1

[19] Mosser L, Dubrule O, Blunt MJ. Reconstruction of three-dimensional porous media using generative adversarial neural networks. Phys Rev E 2017;96 (4):043309. link1

[20] Laloy E, Hérault R, Jacques D, Linde N. Training-image based geostatistical inversion using a spatial generative adversarial neural network. Water Resour Res 2018;54(1):381–406. link1

[21] Mousavi SM, Horton SP, Langston CA, Samei B. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression. Geophys J Int 2016;207(1):29–46. link1

[22] He M, Li Q, Li X. Injection-induced seismic risk management using machine learning methodology—a perspective study. Front Earth Sci 2020;8:227. link1

[23] Mahani AB, Schultz R, Kao H, Walker D, Johnson J, Salas C. Fluid injection and seismic activity in the northern Montney play, British Columbia, Canada, with special reference to the 17 August 2015 M w 4.6 induced earthquake. Bull Seismol Soc Am 2017;107(2):542–52. link1

[24] Ellsworth WL. Injection-induced earthquakes. Science 2013;341(6142):1225942. link1

[25] Atkinson GM, Eaton DW, Ghofrani H, Walker D, Cheadle B, Schultz R, et al. Hydraulic fracturing and seismicity in the western Canada sedimentary basin. Seismol Res Lett 2016;87(3):631–47. link1

[26] Gharbi RB, Elsharkawy AM. Universal neural network based model for estimating the PVT properties of crude oil systems. In: Proceedings of the SPE Asia Pacific Oil and Gas Conference and Exhibition; 1997 Apr 14–16; Kuala Lumpur, Malaysia. Richardson: OnePetro; 1997. link1

[27] Osman EA, Abdel-Wahhab OA, Al-Marhoun MA. Prediction of oil PVT properties using neural networks. In: Proceedings of the SPE Middle East Oil Show; 2001 Mar 17–20; Manama, Bahrain. Richardson: OnePetro; 2001. link1

[28] Wang K, Luo J, Wei Y, Wu K, Li J, Chen Z. Practical application of machine learning on fast phase equilibrium calculations in compositional reservoir simulations. J Comput Phys 2020;401:109013. link1

[29] Helmy T, Fatai A. Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs. Int J Comput Intell Appl 2010;9(4):313–37. link1

[30] Patel AK, Chatterjee S. Computer vision-based limestone rock-type classification using probabilistic neural network. Geosci Front 2016;7(1):53–60. link1

[31] An P. The effect of random noise in lateral reservoir characterization using feed-forward neural networks. In: SEG technical program expanded abstracts 1994. Houston: SEG Library; 1994. link1

[32] An P, Moon WM, Kalantzis F. Reservoir characterization using seismic waveform and feedforword neural networks. Geophysics 2001;66(5):1450–6. link1

[33] Jamialahmadi M, Javadpour FG. Relationship of permeability, porosity and depth using an artificial neural network. J Petrol Sci Eng 2000;26(1–4): 235–9. link1

[34] Wang B, Sharma J, Chen J, Persaud P. Ensemble machine learning assisted reservoir characterization using field production data—an offshore field case study. Energies 2021;14(4):1052. link1

[35] Liu X, Ge Q, Chen X, Li J, Chen Y. Extreme learning machine for multivariate reservoir characterization. J Petrol Sci Eng 2021;205:108869. link1

[36] Chen Z. Reservoir simulation: mathematical techniques in oil recovery. In: CBMS-NSF regional conference series in applied mathematics. Philadelphia: Siam; 2007. link1

[37] Chen Z, Huan G, Ma Y. Computational methods for multiphase flows in porous media. Philadelphia: Siam; 2006. link1

[38] Amirian E, Chen Z. Cognitive data-driven proxy modeling for performance forecasting of waterflooding process. Glob J Technol Optim 2017;08(01):1–9. link1

[39] Amirian E, Dejam M, Chen Z. Performance forecasting for polymer flooding in heavy oil reservoirs. Fuel 2018;216:83–100. link1

[40] Huang Z, Chen Z. Comparison of different machine learning algorithms for predicting the SAGD production performance. J Petrol Sci Eng 2021;202:108559. link1

[41] Dang C, Nghiem L, Fedutenko E, Gorucu SE, Yang C, Mirzabozorg A, et al. AI based mechanistic modeling and probabilistic forecasting of hybrid low salinity chemical flooding. Fuel 2020;261:116445. link1

[42] Ng CSW, Ghahfarokhi AJ, Amar MN. Well production forecast in Volve field: application of rigorous machine learning techniques and metaheuristic algorithm. J Petrol Sci Eng 2022;208(Pt B):109468. link1

[43] Hui G, Chen S, He Y, Wang H, Gu F. Machine learning-based production forecast for shale gas in unconventional reservoirs via integration of geological and operational factors. J Nat Gas Sci Eng 2021;94:104045. link1

[44] Tadjer A, Hong A, Bratvold RB. Machine learning based decline curve analysis for short-term oil production forecast. Energy Explor Exploit 2021;39 (5):1747–69. link1

[45] Ahmad MA, Chen Z. Machine learning models to predict bottom hole pressure in multi-phase flow in vertical oil production wells. Can J Chem Eng 2019;97 (11):2928–40. link1

[46] Lee K, Lim J, Yoon D, Jung H. Prediction of shale-gas production at Duvernay Formation using deep-learning algorithm. SPE J 2019;24(06): 2423–37. link1

[47] Popa A, Connel S. Optimizing horizontal well placement through stratigraphic performance prediction using artificial intelligence. In: Proceedings of the SPE Annual Technical Conference and Exhibition; 2019 Sep 30–Oct 2; Calgary, AB, Canada. Richardson: OnePetro; 2019. link1

[48] Mohaghegh SD. Mapping the natural fracture network in Utica shale using artificial intelligence (AI). In: Proceedings of the SPE/AAPG/SEG Unconventional Resources Technology Conference; 2017 Jul 24–26; Austin, TX, USA. Houston: SEG Library; 2017. link1

[49] He Q, Zhong Z, Alabboodi M, Wang G. Artificial intelligence assisted hydraulic fracturing design in shale gas reservoir. In: Proceedings of the SPE Eastern Regional Meeting; 2019 Oct 15–17; Charleston, WV, USA. Richardson: OnePetro; 2019. link1

[50] Sun Z, Wang L, Zhou J, Wang C. A new method for determining the hydraulic aperture of rough rock fractures using the support vector regression. Eng Geol 2020;271:105618. link1

[51] Yang S, McBride P, Kherroubi J, He A, Le Nir I, Quesada D, et al. An efficient workflow for geological characterization in unconventional reservoirs from a new through-the-bit logging electrical micro-imaging tool. In: Proceedings of the 2018 AAPG International Conference and Exhibition; 2018 Nov 4–11; Cape Town, South Africa; 2018. link1

[52] Wang LK, Sun AY. Well spacing optimization for Permian basin based on integrated hydraulic fracturing, reservoir simulation and machine learning study. In: Proceedings of the SPE/AAPG/SEG Unconventional Resources Technology Conference; 2020 Jul 20–22; online. Richardson: OnePetro; 2020. link1

[53] Bangi MSF, Kwon JSI. Deep reinforcement learning control of hydraulic fracturing. Comput Chem Eng 2021;154:107489. link1

[54] Duplyakov VM, Morozov AD, Popkov DO, Shel EV, Vainshtein AL, Burnaev EV, et al. Data-driven model for hydraulic fracturing design optimization. Part II: inverse problem. J Petrol Sci Eng 2021;208(Pt A):109303. link1

[55] Chaikine IA, Gates ID. A machine learning model for predicting multi-stage horizontal well production. J Petrol Sci Eng 2021;198:108133. link1

[56] Han D, Kwon S. Application of machine learning method of data-driven deep learning model to predict well production rate in the shale gas reservoirs. Energies 2021;14(12):3629. link1

[57] Mohaghegh SD, inventor; Mohaghegh SD, assignee. System and method providing real-time assistance to drilling operation. United States patent US20150300151. 2015 Oct 22.

[58] Unrau S, Torrione P, Hibbard M, Smith R, Olesen L, Watson J. Machine learning algorithms applied to detection of well control events. In: Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition; 2017 Apr 24–27; Dammam, Saudi Arabia. Richardson: OnePetro; 2017. link1

[59] Pollock J, Stoecker-Sylvia Z, Veedu V, Panchal N, Elshahawi H. Machine learning for improved directional drilling. In: Proceedings of the Offshore Technology Conference; 2018 Apr 30–May 3; Houston, TX, USA. Richardson: OnePetro; 2018. link1

[60] Zhao J, Shen Y, Chen W, Zhang Z, Johnston S. Machine learning-based trigger detection of drilling events based on drilling data. In: Proceedings of the SPE Eastern Regional Meeting; 2017 Oct 4–6; Lexington, KY, USA. Richardson: OnePetro; 2017. link1

[61] Hegde C, Gray KE. Use of machine learning and data analytics to increase drilling efficiency for nearby wells. J Nat Gas Sci Eng 2017;40:327–35. link1

[62] Goebel T, Molina RV, Vilalta R, Gupta KD, inventors; Landmark Graphics Corp., assignee. Method and system for predicting a drill string stuck pipe event. United States patent US8752648. 2014 Sep 10.

[63] Dursun S, Tuna T, Duman K, Kellogg RW, inventors; Landmark Graphics Corp., assignee. Real-time risk prediction during drilling operations. United States patent US15/024, 575. 2015 Apr 30.

[64] Castiñeira D, Toronyi R, Saleri N. Machine learning and natural language processing for automated analysis of drilling and completion data. In: Proceedings of the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition; 2018 Apr 23–26; Dammam, Saudi Arabia. Richardson: OnePetro; 2018. link1

[65] Bhattacharya S. A brief review of popular machine learning algorithms in geosciences. In: Briefs in Petroleum Geoscience & Engineering. Cham: Springer; 2021. link1

[66] Abrahart RJ, See LM, Solomatine DP. Practical hydroinformatics: computational intelligence and technological developments in water applications. New York: Springer Science & Business Media; 2008. link1

[67] Li Z, Kovachki N, Azizzadenesheli K, Liu B, Bhattacharya K, Stuart A, Anandkumar A. Fourier neural operator for parametric partial differential equations. 2020. arXiv:2010.08895.

[68] Noshi C. A brief survey of text mining applications for the oil and gas industry. International Petroleum Technology Conference; 2019 Mar 26–28; Beijing, China. Richardson: OnePetro; 2019. link1

[69] Brestoff NE, inventor; Intraspexion LLC assignee. Using classified text and deep learning algorithms to identify risk and provide early warning. United States patent US9552548B1. 2017 Jan 24.

[70] Lv G, Zheng C, Zhang L. Text information retrieval based on concept semantic similarity. In: Proceedings of the 2009 Fifth International Conference on Semantics; 2009 Oct 12–14; Zhuhai, China. Piscataway: IEEE Publisher; 2009. link1

[71] Arumugam S, Rajan S, Gupta S. Augmented text mining for daily drilling reports using topic modeling and ontology. In: Proceedings of the SPE Western Regional Meeting; 2017 Apr 23–27; Bakersfield, CA, USA. Richardson: OnePetro; 2017. link1

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