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Cited by 560Oil and Gas Production TechniquesArtificial Intelligence ApplicationsImpact of AI and Big Data on Business and Society

Application of machine learning and artificial intelligence in oil and gas industry

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Sircar, Anirbid, Yadav, Kriti, Rayavarapu, Kamakshi, Bist, Namrata, Oza, Hemangi·2021·Petroleum Research
DOI 10.1016/j.ptlrs.2021.05.009
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Abstract

Oil and gas industries are facing several challenges and issues in data processing and handling. Large amount of data bank is generated with various techniques and processes. The proper technical analysis of this database is to be carried out to improve performance of oil and gas industries. This paper provides a comprehensive state-of-art review in the field of machine learning and artificial intelligence to solve oil and gas industry problems. It also narrates the various types of machine learning and artificial intelligence techniques which can be used for data processing and interpretation in different sectors of upstream oil and gas industries. The achievements and developments promise the benefits of machine learning and artificial intelligence techniques towards large data storage capabilities and high efficiency of numerical calculations. In this paper a summary of various researchers work on machine learning and artificial intelligence applications and limitations is showcased for upstream and sectors of oil and gas industry. The existence of this extensive intelligent system could really eliminate the risk factor and cost of maintenance. The development and progress using this emerging technologies have become smart and makes the judgement procedure easy and straightforward. The study is useful to access intelligence of different machine learning methods to declare its application for distinct task in oil and gas sector.

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  • AI technologies are currently extensively used in oil and gas exploration and drilling, significantly improving efficiency and increasing yield.

    “AI technologies are currently extensively used in oil and gas exploration and drilling, significantly improving efficiency and increasing yield [110, 123].”

    From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [110]

    Section 3.1 (Exploration; page unavailable) describes growing AI use, higher exploration-well success rates, and improved drilling quality with lower costs. Section 5 (Enablers and challenges; page unavailable) also describes increasingly widespread machine-learning use in drilling. These passages support adoption and efficiency gains, but they do not establish that AI has increased oil or gas yield. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 55

  1. Abou-Sayed, A., 2012. Data mining applications in the oil and gas industry. J. Petrol. Technol. 64 (10), 88e95.
  2. Adelman, M.A., Silva, H.D., Koehn, M.F., 1991. User cost in oil production. Resour. Energy 13 (3), 217e240.
  3. Al-Fattah, S.M., Startzman, R.A., 2001. Predicting natural gas production using artificial neural network. In: SPE Hydrocarbon Economics and Evaluation Symposium, 2e3 April. Society of Petroleum Engineers, Dallas, pp. 1e10.
  4. Ali, J.K., 1994. January. Neural networks: a new tool for the petroleum industry?. In: European Petroleum Computer Conference. Society of Petroleum Engineers.
  5. Aliouane, L., Ouadfeul, S.A., 2014. Sweet spots discrimination in shale gas reservoirs using seismic and well-logs data. A case study from the worth basin in the barnett shale. Energy Procedia 59, 22e27. https://10.1016/j.egypro.2014.10.344. · DOI
  6. Al-Mudhafer, W.J.M., Shaheed, M., 2011. January. Adopting simple & advanced ge- netic algorithms as optimization tools for increasing oil recovery & NPV in an Iraq oil field. In: SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers.
  7. Anderson, R.N., 2017. December. 'Petroleum Analytics Learning Machine'for opti- mizing the Internet of Things of today's digital oil field-to-refinery petroleum system. In: Proceedingsin 2017 IEEE International Conference on Big Data (Big Data), pp. 4542e4545.
  8. Ani, M., Oluyemi, G., Petrovski, A., Rezaei-Gomari, S., 2016. Reservoir uncertainty analysis: the trends fromProbability to algorithms and machine learning. In: Proceedings of the SPE Intelligent Energy InternationalConference and Exhi- bition. Aberdeen, UK, 6e8 September 2016.
  9. Anifowose, F.A., Labadin, J., Abdulraheem, A., 2017b. Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead. Journal of Petroleum Exploration and Production Technology 7 (1), 251e263.
  10. Ashena, R., Thonhauser, G., 2015. Application of artificial neural networks in geo- science and petroleum industry. Artificial Intelligent Approaches in Petroleum Geosciences. Springer, pp. 127e166.
  11. Aydin, G., 2014. Production modeling in the oil and natural gas industry: an application of trend analysis. Petrol. Sci. Technol. 32 (5), 555e564.
  12. Brekke, J.S., 2020. Machine Learning Effects on the Norwegian Oil and Gas Industry (Doctoral Dissertation).
  13. Cao, Q., Banerjee, R., Gupta, S., Li, J., Zhou, W., Jeyachandra, B., 2016, June. Data driven production forecasting using machine learning. In: SPE Argentina Exploration and Production of Unconventional Resources Symposium. Society of Petroleum Engineers.
  14. Castiñeira, D., Toronyi, R., andSaleri, N., 2018. Machine Learning and Natural Lan- guage Processing for Automated Analysis of Drilling and Completion Data. So- ciety of Petroleum Engineers. https://10.2118/192280-MS. · DOI
  15. Chaki, S., Routray, A., Mohanty, W.K., 2015. A novel pre-processing scheme to improve the prediction of sand fraction from seismic attributes using neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (4), 1808e1820.
  16. Diersen, S., Lee, E.J., Spears, D., Chen, P., Wang, L., 2011. Classification of seismic windows using artificial neural networks. Procedia Computer Science 4, 1572e1581. https://doi.org/10.2118/191601-18RPTC-MS. · DOI
  17. Dong, X., GuoJ, H€ o€ ok, M., Pi, G., 2015. Sustainability assessment of the natural gas industry in China using principal component analysis. Sustainability 7 (5), 6102e6118.
  18. Dunlop, J., IsangulovR, Aldred, W.D., Sanchez, H.A., Flores, J.L.S., Herdoiza, J.A., Luppens, C., 2011. Increased Rate of Penetration through Automation. Society of Petroleum Engineers. https://10.2118/139897-MS. · DOI
  19. Evans, S.J., 2019. How digital enegineering and cross-induatsry knowledge tranfer is reducing project execution risks in oil and gas. In: Proceedings in Offshore Technology Conference Held in Houston, Texas, USA, 6 e 9 May 2019, OTC- 29458-MS.
  20. Feng, G.Y., Han, J.X., 2015. The oilfield production prediction model based on principal component analysis and least squares support vector machine. ComputKnowlTechnol 11 (31), 144e147.
  21. 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 ConferencedNorth America. Society of Petroleum Engineers, Midland, pp. 1e19, 13e14 September.
  22. Ghahfarokhi, P.K., Carr, T., Bhattacharya, S., Elliott, J., Shahkarami, A., Martin, K., 2018. A fiber-optic assisted multilayer perceptron reservoir production modeling: a machine learning approach inprediction of gas production from the Marcellus shale. In: SPE/AAPG/SEG Unconventional Resources Technology Conference,23e25 July, Houston, Texas, USA, pp. 1e10.
  23. Hassanvand, M., Moradi, S., Fattahi, M., Zargar, G., Kamari, M., 2018. Estimation of rock uniaxial compressive strength for an Iranian carbonate oil reservoir: modeling vs. artificial neural network application. Petroleum Research 3 (4), 336e345.
  24. Hawedi, H.S., Haron, H., Nordin, A., Ahmed, A.A., 2011. Current challenges and future perspective: the influence of organizational intelligence on Libyan oil and gas industry. IJCSNS 11 (1), 145.
  25. Heghedus, C., Shchipanov, A., Rong, C., 2019. Advancing deep learning to improve upstream petroleum monitoring. IEEE Access (7), 106248e106259.
  26. HolditchS, A., 2013. Unconventional oil and gas resource development e let's do it right. Journal of Unconventional Oil and Gas Resources 1 (2), 2e8.
  27. Ilkhchi, A.K., Rezaee, M., MoallemiS, A., 2006. A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field. J. Geophys. Eng. 3 (4), 356e369.
  28. Khan, M.R., Tariq, Z., Abdulraheem, A., 2018a. August. Machine learning derived correlation to determine water saturation in complex lithologies. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition. Society of Petroleum Engineers.
  29. Khan, M.R., Tariq, Z., Abdulraheem, A., 2018b. Machine learning derived correlation to determine water saturation in complex lithologies. In: SPE Kingdom of Saudi Arabia Annual Technicalsymposium and Exhibition. Society of Petroleum En- gineers, Dammam, pp. 1e10, 23e26 April.
  30. Kumar, A., 2019. A Machine Learning Application for Field Planning. Offshore Technology Conference. https://10.4043/29224-MS. · DOI
  31. Lu, H., Huang, K., Azimi, M., Guo, L., 2019. Blockchain technology in the oil and gas industry: a review of applications, opportunities, challenges, and risks. IEEE Access 7, 41426e41444.
  32. Makinde, I., Lee, W.J., 2019. Principal components methodologyeA novel approach to forecasting production from liquid-rich shale (LRS) reservoirs. Petroleum 5 (3), 227e242.
  33. Managi, S., Opaluch, J.J., Jin, D., Grigalunas, T.A., 2005. Technological change and petroleum exploration in the Gulf of Mexico. Energy Pol. 33 (5), 619e632.
  34. Nande, S., 2018. Application of Machine Learning for Closure Pressure Determina- tion. Society of Petroleum Engineers. https://10.2118/194042-STU. · DOI
  35. Noshi, C.I., Schubert, J.J., 2018. The role of machine learning in drilling operations; a review. In: SPE/AAPG Eastern Regional Meeting. Society of Petroleum Engineers.
  36. Onwuchekwa, C., 2018. Application of Machine Learning Ideas to Reservoir Fluid Properties Estimation. Society of Petroleum Engineers. https://10.2118/193461- MS. · DOI
  37. Osman, E.S.A., 2001. Artificial neural networks models for identifying flow regimes and predicting liquid holdup in horizontal multiphase flow. In: SPE Middle East Oil Show, 17e20 March. Society of Petroleum Engineers, Manama, pp. 1e8.
  38. PandeyR, K., KakatiH, MandalA, 2017. Thermodynamic modeling of equili- briumconditions of CH4/CO2/N2 clathrate hydrate in presence of aqueous solution of sodium chloride inhibitor. Petrol. Sci. Technol. 35 (10), 947e954.
  39. Peng, Z., Yang, H., Pan, H., Ji, Y., 2016. August. Identification of low resistivity oil and gas reservoirs with multiple linear regression model. In: 2016 12th Interna- tional Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, pp. 529e533.
  40. Priyanka, E.B., Thangavel, S., Gao, X.Z., 2021. Review analysis on cloud computing based smart grid technology in the oil pipeline sensor network system. Pe- troleum Research 6 (1), 77e90.
  41. Rana, S., Ertekin, T., King, G.R., 2018. An efficient probabilistic assisted history matching tool using GaussianProcesses proxy models: application to coalbed methane reservoir. In: Proceedings of the SPE AnnualTechnical Conference and Exhibition, Dallas, TX, USA, 24e26 September 2018.
  42. Saghir, F., Gilabert, H., andBoujonnier, M., 2018. Edge Analytics and Future of Up- stream Automation. Society of Petroleum Engineers. https://10.2118/192019- MS. · DOI
  43. Salem, K.G., AbdulazizAAM, Abdel Sattar, A., Dahab, A.S.D., 2018. Prediction of hy- draulic properties in carbonate reservoirs using artificial neural network. In: Abu Dhabi International Petroleumexhibition & Conference, 12e15 November. Society of Petroleum, Abu Dhabi, pp. 1e18.
  44. Subrahmanya, N., Xu, P., El-Bakry, A., Reynolds, C., 2014. Advanced Machine Learning Methods for Production Data Pattern Recognition. Society of Petro- leum Engineers. https://10.2118/167839-MS. · DOI
  45. Tabesh, M., Soltani, J., Farmani, R., Savic, D., 2009. Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. J. Hydroinf. 11 (1), 1e17.
  46. Tarrahi, M., Afra, S., Surovets, I., 2015. October. A novel automated and probabilistic EOR screening method to integrate theoretical screening criteria and real field EOR practices using machine learning algorithms. In: SPE Russian Petroleum Technology Conference. Society of Petroleum Engineers.
  47. Teixeira, A., Secchi, A., 2019. Machine learning models to support reservoir pro- duction optimization. IFAC 52 (1), 498e501.
  48. Temizal, C., Canbaz, C.H., Palabiyik, Y., Putra, D., Asena, A., Ranjith, R., Jongkittinarukorn, K., 2019. A comprehensive review of smart/intelligent oil- field technologies and applications in the oil and gas industry. In: Proceedings in Society of Petroleum Engineers. SPE-195095-MS.
  49. Yan, H.Y., Fu, J.Y., Dong, J.H., 2014. Forecast model of oilfield indexes based on IPSO GNN. Sci. Technol. Eng. 14 (15), 197e202.
  50. Yeten, B., Durlofsky, L.J., Aziz, K., 2003. Optimization of nonconventional well type, location, and trajectory. SPE J. 8 (3), 200e210.
  51. Zarei, F., Daliri, A., Alizadeh, N., 2008. January. The use of neuro-fuzzy proxy in well placement optimization. In: Intelligent Energy Conference and Exhibition. So- ciety of Petroleum Engineers.
  52. Zhang, D., Chen, Y., Meng, J., 2018. Synthetic well logs generation via recurrent neural networks. Petrol. Explor. Dev. 45 (4), 629e639. https://doi.org/10.1016/ S1876-3804(18)30068-5. · DOI
  53. Zhang, J., Yin, X., Zhang, G., Gu, Y., Fan, X., 2020. Prediction method of physical parameters based on linearized rock physics inversion. Petrol. Explor. Dev. 47 (1), 59e67. https://doi.org/10.1016/S1876-3804(20)60005-2. · DOI
  54. Zheng, L., Wei, P., Zhang, Z., Nie, S., Lou, X., Cui, K., Fu, Y., 2017. Joint exploration and development: a self-salvation road to sustainable development of unconven- tional oil and gas resources. Nat. Gas. Ind. B 4 (6), 477e490.
  55. Zliobaite, I., Pechenizkiy, M., Gama, J., 2016. An overview of concept drift applica- tions. In: Japkowicz, N., Stefanowski, J. (Eds.), Big Data Analysis: New Algo- rithms for a New Society, Studies in Big Data 16. Springer, Cham.

Cited by

Sorted by citations

Authors
Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H.
Affiliations
Pandit Deendayal Energy University (IN)
Corresponding author
Sircar, Anirbid
ORCID
Venue
Petroleum Research
Publisher
Elsevier BV
Published
2021-12
Volume
6
Issue
4
Pages
379-391
ISSN
2096-2495 (ISSN-L)
DOI
10.1016/j.ptlrs.2021.05.009
OpenAlex
W3172365935
Cited by
560
References
1
Citations by year
2026: 88 · 2025: 147 · 2024: 149 · 2023: 111 · 2022: 55 · 2021: 10
FWCI
43.10
Type
journal article
Language
en
Update notices
erratum (2023-06-01)
Open access
Licence
Locations
  • Petroleum Research (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Oil and Gas Production Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
machine learning, artificial intelligence, oil and gas industry, upstream oil and gas, data processing, data interpretation, maintenance cost reduction, risk mitigation, intelligent systems

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