Applications of Artificial Intelligence in Oil and Gas Development

  • Hong Li
  • Haiyang YuEmail author
  • Nai Cao
  • He Tian
  • Shiqing Cheng
Original Paper


Artificial intelligence has been back on the stage of research works in all the walks in recently years, the sharply increase of AI-based work have shown its potential to be a future direction for almost all disciplines. In oil and gas industry, AI technology is also doubtlessly a new shining star that draws attention from researchers devoted themselves into it. In order to dig up more about the applications of artificial intelligence in oilfield development for a hint of the future trend of this exciting technology in oil and gas industry, literature investigation of a large amount of AI-based work reported has been conducted in this work. Based on the investigation, the application of AI in important issues in oilfield development including oilfield production dynamic prediction, developing plan optimization, residual oil identification, fracture identification, and enhanced oil recovery are specifically investigated and summarized, the backs and cons of existing AI algorithms has been compared. Based on the analysis and discussion, current situation of the application of AI in oilfield development is concluded, and suggestions and potential directions of future work AI application in oil and gas developing are provided.



Adaptive network-based fuzzy inference system


Artificial neural network


Adaptive particle swarm optimization


Big data analytics


Back propagation


Convolutional neural network


Data mining


Fuzzy clustering method


Genetic algorithm


Graph neural network


Hybrid intelligent system


Internet of things technology


Improved particle swami optimization


Least squares support vector machine


Mean absolute percent error


Machine learning


Multi-layer perceptron neural network


Mean squared error


Nonlinear auto regressive model with eXogenous


Principal component analysis


Polynomial neural network


Particle swarm optimization


Quantum particle swarm optimization


Root mean squared error


Standard deviation


Self-organizing maps


Surrogate regulation model


Support vector machine


Weight on bit



The authors are grateful for financial support from the National Natural Science Foundation of China (51874317) and the National Science and Technology Major Projects of China (Grant Nos. 2016ZX05037003).

Compliance with Ethical Standards

Conflict of interest

All authors declare no conflict of interest.


  1. 1.
    Wang XL (2017) Application of artificial intelligence in oil and gas industry. Mod Inf Technol 3(1):117–119Google Scholar
  2. 2.
    Gilman H, Nordtvedt J E (2014) Intelligent energy: the past, the present, and the future. In: SPE intelligent energy conference & exhibition, 1–3 April. Society of Petroleum Engineers, Utrecht, pp 185–190Google Scholar
  3. 3.
    Yu WJ (2016) On the development trend and necessity of intelligent oilfield. Sci Wealth 10:500Google Scholar
  4. 4.
    Shi YJ (2016) Analysis of the research status of intelligent oilfield in China. Straits Technol Ind 12:81–83Google Scholar
  5. 5.
    Ma SX (2018) Technology focus: formation evaluation. SPE 70(08):50. CrossRefGoogle Scholar
  6. 6.
    Liu W, Yan N (2018) Application and influence of artificial intelligence in petroleum engineering area. Pet Technol Forum 4:32–40Google Scholar
  7. 7.
    Li DW, Shi GR (2018) Optimization of common data mining algorithms for petroleum exploration and development. Acta Pet Sin 39(2):240–246MathSciNetGoogle Scholar
  8. 8.
    Giuliani M, Cadei L, Montini M et al (2018) Hybrid artificial intelligence techniques for automatic simulation models matching with field data. In: Abu Dhabi international petroleum exhibition & conference, 12–15 November. Society of Petroleum Engineers, Abu Dhabi, pp 1–11Google Scholar
  9. 9.
    Hojageldiyev D (2018) Artificial intelligence in HSE. In: Abu Dhabi international petroleum exhibition & conference, 12–15 November. Society of Petroleum Engineers, Abu Dhabi, pp 1–9Google Scholar
  10. 10.
    Liu BJ (2015) Construction conception of intelligent oilfield. J Shengli Oilfield Party Sch 06:99–101Google Scholar
  11. 11.
    Liu XD, Yang TT, Yan HY (2013) Forecast of oilfield indexes based on elman neural network and genetic algorithm. Comput Mod 2:150–152Google Scholar
  12. 12.
    Lien MO, Jansen JD, Brouwer DR (2006) Multiscale smart well management. In: Intelligent energy conference and exhibition, 11–13 April. Society of Petroleum Engineers, Amsterdam, pp 1–11Google Scholar
  13. 13.
    Liu N (2014) Digital oilfield construction, smooth evolution to intelligent oilfield. Silicon Val 4:191Google Scholar
  14. 14.
    Yousef AA, Liu JS, Blanchard GW et al (2012) Smart waterflooding: industry. In: SPE annual technical conference and exhibition, 8–10 October. Society of Petroleum Engineers, San Antonio, pp 1–25Google Scholar
  15. 15.
    Wang HF, Wang SJ, Zhu SB (2018) Conception and exploration of the smart oil and gas field construction in “internet +” era. Oil Gas Field Surf Eng 8:1–8Google Scholar
  16. 16.
    Anderson S, Barvik S, Rabitoy C (2019) Innovative digital inspection methods. In: Offshore technology conference, 6–9 May, Houston, TX, pp 1–9Google Scholar
  17. 17.
    Denney D (2006) To support digital oil fields. SPE 58(10):71–72Google Scholar
  18. 18.
    Nadhan D, Mayani MG, Rommetveit R (2018) Drilling with digital twins. In: IADC/SPE Asia pacific drilling technology conference and exhibition, 27–29 August. Society of Petroleum Engineers, Bangkok, pp 1–18Google Scholar
  19. 19.
    Rodriguez JA, Nasr H, Scott M et al (2013) New generation of petroleum workflow automation: philosophy and practice. In: SPE digital energy conference, 5–7 March. Society of Petroleum Engineers, The Woodlands, pp 1–13Google Scholar
  20. 20.
    Tan AA, Potts JK (1995) Digital log management system. SPE 7(04):88–90Google Scholar
  21. 21.
    William WW, Balch RS, Stubbs BA (2002) How artificial intelligence methods can forecast oil production. In: SPE/DOE improved oil recovery symposium, 13–17 April. Society of Petroleum Engineers, Tulsa, pp 1–16Google Scholar
  22. 22.
    Gatta SR (1999) Decision tree analysis and risk modeling to appraise investments on major oil field projects.In: SPE middle east oil show and conference. Information Engineering Research Institute, USA, pp 187–202Google Scholar
  23. 23.
    Agwu OE, Akpabio JU, Alabi SB et al (2018) Artificial intelligence techniques and their applications in drilling fluid engineering: a review. J Pet Sci Eng 167:300–315CrossRefGoogle Scholar
  24. 24.
    Tang HK, Li XY (2015) BP neural network is applied to the production dynamic analysis of an oil field. Oil Gas Field Surf Eng 4:20–21Google Scholar
  25. 25.
    Al-Thuwaini J, Zangl G, Phelps RE (2006) Innovative approach to assist history matching using artificial intelligence. In: Intelligent energy conference and exhibition, 11–13 April. Society of Petroleum Engineers, Amsterdam, pp 1–7Google Scholar
  26. 26.
    Mohaghegh SD, Grujic OS, Zargari S, Dahaghi AK (2011) Modeling, history matching, forecasting and analysis of shale reservoirs performance using artificial intelligence. In: SPE digital energy conference and exhibition, 19–21 April. Society of Petroleum Engineers, The Woodlands, pp 1–14Google Scholar
  27. 27.
    Costa LAN, Maschio C, Schiozer DJ (2014) Application of artificial neural networks in a history matching process. SPE J 123:30–45Google Scholar
  28. 28.
    Shahkarami A, Mohaghegh SD, Gholami V, Haghighat SA (2014) Artificial intelligence (AI) assisted history matching. In: SPE western North American and Rocky Mountain joint meeting, 17–18 April. Society of Petroleum Engineers, Denver, pp 1–26Google Scholar
  29. 29.
    Denney D (2011) Modeling history matching, forecasting, and analysis of shale-reservoir performance with artificial intelligence. SPE 63(09):60–63Google Scholar
  30. 30.
    Zhu QJ, Gu YB, Lu SL et al (2002) Application of artificial neural network model in oil resource prediction. East China Geol 23(4):281–287Google Scholar
  31. 31.
    Al-Fattah SM, Startzman RA (2001) Predicting natural gas production using artificial neural network. In: SPE hydrocarbon economics and evaluation symposium, 2–3 April. Society of Petroleum Engineers, Dallas, pp 1–10Google Scholar
  32. 32.
    Osman ESA (2001) Artificial neural networks models for identifying flow regimes and predicting liquid holdup in horizontal multiphase flow. In: SPE middle east oil show, 17–20 March. Society of Petroleum Engineers, Manama, pp 1–8Google Scholar
  33. 33.
    Yan HY, Fu JY, Dong JH et al (2014) Forecast model of oilfield indexes based on IPSO GNN. Sci Technol Eng 14(15):197–202Google Scholar
  34. 34.
    Xu SH, Bi CC, Zhang Y et al (2015) Oilfield development indicators prediction based on radial basis process neural network. Comput Technol Autom 3:52–54Google Scholar
  35. 35.
    Feng GY, Han JX (2015) The oilfield production prediction model based on principal component analysis and least squares support vector machine. Comput Knowl Technol 11(31):144–147Google Scholar
  36. 36.
    Gaurav A (2017) Horizontal shale well EUR determination integrating geology, machine learning, pattern recognition and multivariate statistics focused on the Permian basin. In: SPE liquids-rich basins conference—North America, 13–14 September. Society of Petroleum Engineers, Midland, pp 1–19Google Scholar
  37. 37.
    Salem KG, Abdulaziz AAM, Abdel Sattar A, Dahab ASD (2018) Prediction of hydraulic properties in carbonate reservoirs using artificial neural network. In: Abu Dhabi international petroleum exhibition & conference, 12–15 November. Society of Petroleum, Abu Dhabi, pp 1–18Google Scholar
  38. 38.
    Ghahfarokhi PK, Carr T, Bhattacharya S, Elliott J, Shahkarami A, Martin K (2018) A fiber-optic assisted multilayer perceptron reservoir production modeling: a machine learning approach in prediction of gas production from the Marcellus shale. In: SPE/AAPG/SEG unconventional resources technology conference, 23–25 July, Houston, Texas, USA pp 1–10Google Scholar
  39. 39.
    Khan MR, Tariq Z, Abdulraheem A (2018) Machine learning derived correlation to determine water saturation in complex lithologies. In: SPE Kingdom of Saudi Arabia annual technical symposium and exhibition, 23–26 April. Society of Petroleum Engineers, Dammam, pp 1–10Google Scholar
  40. 40.
    Zhang ZX, Kang XF, Zhang FF (2012) Application of grey theory for oil and gas reservoir evaluation program optimization. Adv Mater Res 616–618:1008–1012CrossRefGoogle Scholar
  41. 41.
    Kang XJ, Yuan AW, Gao J et al (2006) Neural network method for comprehensive evaluation of oilfield development planning scheme. Spec Oil Gas Res 13(2):48–50Google Scholar
  42. 42.
    Park H, Lim JS, Kang JM, Roh J, Min B (2006) A hybrid artificial intelligence method for the optimization of integrated gas production system. In: SPE Asia pacific oil & gas conference and exhibition, 11–13 September. Society of Petroleum Engineers, Adelaide, pp 1–9Google Scholar
  43. 43.
    Xiao DR, Pan H (2010) Optimization of designing of the oil field exploiting based on fuzzy mathematics and BP neural network. Microcomput Inf 26(6):209–211Google Scholar
  44. 44.
    Godarzi AA, Amiri RM, Talaei A et al (2014) Predicting oil price movements: a dynamic artificial neural network approach. Energy Policy 68(5):371–382CrossRefGoogle Scholar
  45. 45.
    Sun H, Li PC (2016) Measures optimization for oil and water well based on quantum particle swarm optimization. Comput Technol Dev 26(9):78–82Google Scholar
  46. 46.
    Feng GQ, Pan LY, Kong B et al (2018) Hierarchical optimization research based on fuzzy clustering analysis. Eval Dev Oil Gas Reserv 3:30–39Google Scholar
  47. 47.
    Roueché JN, Karacan CO (2018) Zone identification and oil saturation prediction in a waterflooded field: residual oil zone, East Seminole field, Texas, USA, Permian Basin. In: SPE improved oil recovery conference, 14–18 April. Society of Petroleum Engineers, Tulsa, pp 1–14Google Scholar
  48. 48.
    Denney D (2000) Artificial neural networks identify restimulation candidates. SPE 52(02):44–45Google Scholar
  49. 49.
    Du QL, Zhu LH (2009) Remaining oil distribution and its description technology during ultra-high water cut stage in La-Sa-Xing oilfi1eld. Daqing Pet Geol Dev 5:99–105Google Scholar
  50. 50.
    Liu J, Gao HY, Wang TT et al (2013) Parameters prediction of remaining oil of aII oil region in Daqing based on BP neural networks. Inner Mong Petrochem Ind 23:47–50Google Scholar
  51. 51.
    Mao GQ, Teng QZ, Wu Y et al (2014) Shape recognition of remained oil based on Bp neural network. J Terahertz Sci Electron Inf Technol 12(6):858–864Google Scholar
  52. 52.
    Denney D (2006) Treating uncertainties in reservoir-performance prediction with neural networks. Soc Pet Eng 58(06):69–71Google Scholar
  53. 53.
    Thomas AL, La Pointe PR (1995) Conductive fracture identification using neural networks. In: The 35th U.S. symposium on rock mechanics (USRMS), 5–7 June. American Rock Mechanics Association, Reno pp 1–6Google Scholar
  54. 54.
    Zhang JC, Xing YZ, Zheng LH (2005) Using artificial intelligent technique to identify fractures. Well Log Technol 29(1):52–54Google Scholar
  55. 55.
    Huang FX, Xia ZY, Gui HB et al (2016) The application of BP neural network to DMT hill metamorphic fracture prediction. Chin J Eng Geophys 13(4):483–490Google Scholar
  56. 56.
    Anderson RN, Xie B, Wu L, Kressner AA, Frantz JH, Ockree MA, McLane MA (2016) Using machine learning to identify the highest wet gas producing mix of hydraulic fracture classes and technology improvements in the Marcellus shale. In: Unconventional resources technology conference, pp 1–13Google Scholar
  57. 57.
    Xie W (2017) Pso-LSSVM based identifying method for the fracture-vug fillings. Daqing Pet Geol Dev 2:135–142Google Scholar
  58. 58.
    Wu ZY, Mo XW, Liu JH et al (2018) Convolutional neural network algorithm for classification evaluation of fractured reservoirs. Pet Explor 57(04):135–143Google Scholar
  59. 59.
    Nande S (2018) Application of machine learning for closure pressure determination. In: SPE annual technical conference and exhibition, 24–26 September. Society of Petroleum Engineers, Dallas, pp 1–10Google Scholar
  60. 60.
    Ahmed SA, Elkatatny S, Ali AZ, Abdulraheem A, Mahmoud M (2019) Artificial neural network ANN approach to predict fracture pressure. In: SPE middle east oil and gas show and conference, 18–21 March. Society of Petroleum Engineers, Manama, pp 1–9Google Scholar
  61. 61.
    Adeyemi BJ, Sulaimon AA (2012) Predicting wax formation using artificial neural network. In: Nigeria annual international conference and exhibition, 6–8 August. Society of Petroleum Engineers, Lagos pp 1–8Google Scholar
  62. 62.
    Li TN (2015) Study on diagnosis model of oilfield abnormal well based on feedback dynamic neural network. Comput Technol Autom 2:114–116CrossRefGoogle Scholar
  63. 63.
    Liu BJ (2018) Research on diagnostic technique of indicator diagram based on CNN convolution neural network. J Xi’an Pet Univ 5:70–75Google Scholar
  64. 64.
    Wang X, He Y, Li F, Dou X, Wang Z, Xu H, Fu L (2019) A working condition diagnosis model of sucker rod pumping wells based on big data deep learning. In: International petroleum technology conference 26–28 March, Beijing, China, pp 1–10Google Scholar
  65. 65.
    Zhou CC, Li J, Zhang XG et al (2008) Predication for EOR by polymer flooding based on artificial neural network-comparison between ANN and quadratic. Polynomial Stepwise Regres Method 27(3):113–116Google Scholar
  66. 66.
    Ni HM, Liu YJ, Fan YC et al (2014) Optimization of steam flooding injection and production based on improved particle swarm optimization. Acta Pet Sin 35(1):114–117Google Scholar
  67. 67.
    Shi SZ, Yu HY, Sun ZL et al (2014) Forecast of fracturing effect based on gray correlation analysis and BP neural network. J Changjiang Univ (Self Publ Ed) 31:154–156Google Scholar
  68. 68.
    Yang ZH, Li ZP (2017) A new method for choice of water-control fracturing segments in horizontal wells based on the BP neural network system. Geol Explor 53(4):0818–0824Google Scholar

Copyright information

© CIMNE, Barcelona, Spain 2020

Authors and Affiliations

  • Hong Li
    • 1
  • Haiyang Yu
    • 1
    Email author
  • Nai Cao
    • 1
  • He Tian
    • 1
  • Shiqing Cheng
    • 1
  1. 1.State Key Laboratory of Petroleum Resources and ProspectingChina University of PetroleumBeijingChina

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