CoChat
Sign Up
Cited by 232Hydrocarbon exploration and reservoir analysisOil and Gas Production TechniquesReservoir Engineering and Simulation Methods

A systematic review of data science and machine learning applications to the oil and gas industry

Open access
Tariq, Zeeshan, Aljawad, Murtada Saleh, Hasan, Amjed, Murtaza, Mobeen, Mohammed, Emad, El-Husseiny, Ammar, Alarifi, Sulaiman A., Mahmoud, Mohamed +1 more·2021·Journal of Petroleum Exploration and Production Technology
DOI 10.1007/s13202-021-01302-2
Open this paper in CoChat — free accountOpen PDF

Abstract

Abstract This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s13202-021-01302-2

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 [123]

    The Introduction and Drilling performance prediction sections describe AI applications in exploration and drilling, including improved drilling performance and savings in time and cost. But Drilling performance prediction also notes limited real-time implementation, while the Concluding remarks describe enhanced production as a possibility; these passages do not establish that current use is increasing yield. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 196

  1. Aadnoy BS, Fazaelizadeh M, Hareland G (2010) A 3D analytical model for wellbore friction. J Can Pet Technol. https:// doi. org/ 10. 2118/ 141515-PA · DOI
  2. Abbas AK, Al-haideri NA, Bashikh AA (2019) Implementing artifi- cial neural networks and support vector machines to predict lost circulation. Egypt J Pet 28(4):339-347. https:// doi. org/ 10. 1016/j. ejpe. 2019. 06. 006 · DOI
  3. Abdelgawad K, Elkatatny S, Mousa T, Mahmoud M, Patil S (2018) Real time determination of rheological properties of spud drilling fluids using a hybrid artificial intelligence technique. J Energy Resour Technol. https:// doi. org/ 10. 2118/ 192257-MS · DOI
  4. Abdi Y, Garavand AT, Sahamieh RZ (2018) Prediction of strength parameters of sedimentary rocks using artificial neural net- works and regression analysis. Arab J Geosci 11:587. https:// doi. org/ 10. 1007/ s12517-018-3929-0 · DOI
  5. Abdulraheem A, Ahmed M, Vantala A, Parvez T, (2009) Predic- tion of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. In: Society of petroleum engineers-SPE Saudi Arabia section technical symposium 2009. Society of Petroleum Engineers. https:// doi. org/ 10. 2118/ 126094-ms · DOI
  6. Adesina FAS, Abiodun A, Anthony A, Olugbenga F (2015) Mod- eling the effect of temperature on environmentally safe oil based drilling mud using artificial neural network algorithm. Pet Coal 57(1):60-70
  7. Agwu OE, Akpabio JU, Alabi SB, Dosunmu A (2018) Artificial intelligence techniques and their applications in drilling fluid engineering: a review. J Pet Sci Eng 167:300-315. https:// doi. org/ 10. 1016/j. petrol. 2018. 04. 019 · DOI
  8. Ahmadi MA, Chen Z (2019) Comparison of machine learning meth- ods for estimating permeability and porosity of oil reservoirs via petro-physical logs. Petroleum 5:271-284. https:// doi. org/ 10. 1016/j. petlm. 2018. 06. 002 · DOI
  9. Ahmadi MA, Pournik M, Shadizadeh SR (2015) Toward connection- ist model for predicting bubble point pressure of crude oils: application of artificial intelligence. Petroleum 1:307-317. https:// doi. org/ 10. 1016/j. petlm. 2015. 08. 003 · DOI
  10. Ahmadi MA, Shadizadeh SR, Shah K, Bahadori A (2018) An accurate model to predict drilling fluid density at wellbore conditions. Egypt J Pet 27:1-10. https:// doi. org/ 10. 1016/j. ejpe. 2016. 12. 002 · DOI
  11. Ahmed A, Ali A, Elkatatny S, Abdulraheem A (2019) New artificial neural networks model for predicting rate of penetration in deep shale formation. Sustain. https:// doi. org/ 10. 3390/ su112 26527 · DOI
  12. Akbari M, Ameri MJ, Kharazmi S, Motamedi Y, Pournik M (2017) New correlations to predict fracture conductivity based on the rock strength. J Petr Sci Eng 152:416-426. https:// doi. org/ 10. 1016/j. petrol. 2017. 03. 003 · DOI
  13. Al-Anazi A, Gates ID (2010) On the capability of support vector machines to classify lithology from well logs. Nat Resour Res. https:// doi. org/ 10. 1007/ s11053-010-9118-9 · DOI
  14. Al-Bulushi NI, King PR, Blunt MJ, Kraaijveld M (2012) Artificial neural networks workflow and its application in the petroleum industry. Neural Comput Appl 21:409-421. https:// doi. org/ 10. 1007/ s00521-010-0501-6 · DOI
  15. Al-Hajri NM, Al-Ghamdi A, Tariq Z, Mahmoud M (2020) Scale-pre- diction/inhibition design using machine-learning techniques and probabilistic approach. SPE Prod Oper 35:0987-1009. https:// doi. org/ 10. 2118/ 198646-PA · DOI
  16. Al-Hameedi AT, Alkinani HH, Dunn-Norman S, Flori RE, Hilgedick SA, Amer AS, Alsaba MT (2018) using machine learning to pre- dict lost circulation in the rumaila field, Iraq. In: Paper presented at the SPE Asia pacific oil and gas conference and exhibition, Brisbane, Australia. https:// doi. org/ 10. 2118/ 191933-MS · DOI
  17. Al-Marhoun MA, Osman EA (2002) Using artificial neural networks to develop New PVT correlations for saudi crude oils. In: Society of petroleum engineers-Abu Dhabi international petroleum exhibi- tion and conference 2002, ADIPEC 2002. Society of Petroleum Engineers, Abu Dhabi. https:// doi. org/ 10. 2523/ 78592-ms · DOI
  18. Al-Shammasi AA (2001) A review of bubblepoint pressure and oil for- mation volume factor correlations. SPE Res Eval Eng 4:146-160. https:// doi. org/ 10. 2118/ 71302-PA Alarifi SA, Miskimins J (2021) A new approach to estimating ultimate recovery for multistage hydraulically fractured horizontal wells by utilizing completion parameters using machine learning. SPE Prod Oper 36:468-483. https:// doi. org/ 10. 2118/ 204470-PA · DOI
  19. Alarifi S, AlNuaim S, Abdulraheem A (2015) Productivity index pre- diction for oil horizontal wells using different artificial intel- ligence techniques. In: All days. SPE. https:// doi. org/ 10. 2118/ 172729-MS Alkinani HH, Al-Hameedi ATT, Dunn-Norman S, Lian D (2020) Application of artificial neural networks in the drilling processes: can equivalent circulation density be estimated prior to drilling? Egypt J Pet. https:// doi. org/ 10. 1016/j. ejpe. 2019. 12. 003 · DOI
  20. Ameen MS, Smart BGD, Somerville JM, Hammilton S, Naji NA (2009) Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-D reservoir, Ghawar field, Saudi Arabia). Mar Pet Geol 26:430-444. https:// doi. org/ 10. 1016/j. marpe tgeo. 2009. 01. 017 · DOI
  21. Amiri M, Zahedi G, Yunan MH (2015) Water saturation estimation in tight shaly gas sandstones by application of progressive quasi- static (PQS) algorithm-a case study. J Nat Gas Sci Eng 22:468- 477. https:// doi. org/ 10. 1016/j. jngse. 2014. 12. 027 · DOI
  22. Amiri M, Salamati A, Hatampour A, Rostami A, Heidari M (2014) Application of progressive quasistatic (PQS) algorithm in predic- tion of water saturation in tight gas sandstones-A case study. In: 20th Formation evaluation symposium of Japan 2014
  23. Andrea TA, Kalayeh H (1991) Applications of neural networks in quan- titative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chemi 34(9):2824-2836. https:// doi. org/ 10. 1021/ jm001 13a022 · DOI
  24. Anemangely M, Ramezanzadeh A, Amiri H, Hoseinpour S-A (2019) Machine learning technique for the prediction of shear wave velocity using petrophysical logs. J Pet Sci Eng 174:306-327. https:// doi. org/ 10. 1016/j. petrol. 2018. 11. 032 · DOI
  25. Anifowose F, Abdulraheem A (2011) Fuzzy logic-driven and SVM- driven hybrid computational intelligence models applied to oil and gas reservoir characterization. J Nat Gas Sci Eng. https:// doi. org/ 10. 1016/j. jngse. 2011. 05. 002 · DOI
  26. Anifowose FA, Abdulraheem A, Al-Shuhail A, Schmitt DP (2013a) Improved permeability prediction from seismic and log data using artificial intelligence techniques. SPE Middle East Oil Gas Show Conf MEOS Proc 3:2190-2196. https:// doi. org/ 10. 2118/ 164465-ms · DOI
  27. Anifowose FA, Labadin J, Abdulraheem A (2017) Ensemble machine learning: an untapped modeling paradigm for petroleum reservoir characterization. J Pet Sci Eng 151:480-487. https:// doi. org/ 10. 1016/j. petrol. 2017. 01. 024 · DOI
  28. Anifowose FA (2012) Advances in hybrid computational intelligence application in oil and gas reservoir characterization. In: Soci- ety of petroleum engineers-SPE Saudi Arabia section young professionals technical symposium 2012, YPTS 2012. pp. 1-8. https:// doi. org/ 10. 2118/ 160921-ms · DOI
  29. Arabjamaloei R, Shadizadeh S (2011) Modeling and optimizing rate of penetration using intelligent systems in an Iranian southern oil field (ahwaz oil field). Pet Sci Technol. https:// doi. org/ 10. 1080/ 10916 46090 28828 18 · DOI
  30. Aulia A, Rahman A, Quijano Velasco JJ (2014) Strategic well test planning using random forest. In: All days. SPE. https:// doi. org/ 10. 2118/ 167827-MS · DOI
  31. Awoleke OO, Lane RH (2011) Analysis of data from the barnett shale using conventional statistical and virtual intelligence techniques. SPE Reserv Eval Eng. https:// doi. org/ 10. 2118/ 127919-PA Ba alawi M, Gharbi S, Mahmoud M (2020) Modeling and prediction of resistivity, capillary pressure and relative permeability using arti- ficial neural Network. In: Day 3 Wed, January 15, 2020. IPTC. https:// doi. org/ 10. 2523/ IPTC-19854-MS · DOI
  32. Baarimah SO, Abdulraheem A, Anifowose FA (2014) Artificial intelligence (AI) techniques for predicting the reservoir fluid properties of crude-oil systems. In: Society of petroleum engi- neers-international petroleum technology conference 2014, IPTC 2014-innovation and collaboration: keys to affordable energy. pp. 3953-3968
  33. Bageri BS, Anifowose FA, Abdulraheem A (2015) Artificial intel- ligence based estimation of water saturation using electrical measurements data in a carbonate reservoir. In: SPE middle east oil and gas show and conference, MEOS, Proceedings. Society of Petroleum Engineers, pp. 499-515. https:// doi. org/ 10. 2118/ 172564-ms · DOI
  34. Bagheri A, Nazari A, Sanjayan J (2019) The use of machine learning in boron-based geopolymers: function approximation of com- pressive strength by ANN and GP. Meas J Int Meas Confed. https:// doi. org/ 10. 1016/j. measu rement. 2019. 03. 001 · DOI
  35. Bahorich M, Farmer S (1995) The coherence cube. Lead Edge 14(10):1053-1058
  36. Bahorich MS, Farmer SL (1995) 3-D seismic discontinuity for faults and stratigraphic features: the coherence cube. In: 1995 SEG annual meeting. pp. 93-96. https:// doi. org/ 10. 1190/1. 18875 23 · DOI
  37. Barbosa LFFM, Nascimento A, Mathias MH, de Carvalho JA (2019) Machine learning methods applied to drilling rate of pene- tration prediction and optimization: a review. J Pet Sci Eng 183:106332. https:// doi. org/ 10. 1016/j. petrol. 2019. 106332 · DOI
  38. Berthelot A, Solberg AHS, Gelius LJ (2013) Texture attributes for detection of salt. J Appl Geophys. https:// doi. org/ 10. 1016/j. jappg eo. 2012. 09. 006 · DOI
  39. Bhattacharya S, Mishra S (2018) Applications of machine learning for facies and fracture prediction using bayesian network theory and random forest: case studies from the Appalachian basin. USA J Pet Sci Eng 170:1005-1017. https:// doi. org/ 10. 1016/j. petrol. 2018. 06. 075 · DOI
  40. Bilgesu HI, Tetrick LT, Altmis U, Mohaghegh S, Ameri S (1997) New approach for the prediction of rate of penetration (ROP) values. In: Proceedings-SPE eastern regional conference and exhibi- tion. https:// doi. org/ 10. 2523/ 39231-ms · DOI
  41. da Bispo VDS, Scheid CM, Calcada LA, da Meleiro LAC (2017) Development of an ANN-based soft-sensor to estimate the appar- ent viscosity of water-based drilling fluids. J Pet Sci Eng. https:// doi. org/ 10. 1016/j. petrol. 2016. 11. 030 · DOI
  42. Bondi G, Creamer R, Ferrari A, Fenton O, Wall D (2018) Using machine learning to predict soil bulk density on the basis of visual parameters: tools for in-field and post-field evaluation. Geoderma 318:137-147. https:// doi. org/ 10. 1016/j. geode rma. 2017. 11. 035 · DOI
  43. Bourgoyne AT, Young FS (1999) A multiple regression approach to optimal drilling and abnormal pressure detection. SPE Repr Ser. https:// doi. org/ 10. 2118/ 4238-pa · DOI
  44. Brazell S, Bayeh A, Ashby M, Burton D (2019) A Machine-learning- based approach to assistive well-log correlation. Petrophysics 60:469-479. https:// doi. org/ 10. 30632/ PJV60 N4-2019a1 · DOI
  45. Buhulaigah A, Al-Mashhad AS, Al-Arifi SA, Al-Kadem MS, Al-Dab- bous MS (2017) Multilateral wells evaluation utilizing artificial intelligence. In: SPE middle east oil & gas show and confer- ence. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 183688-MS · DOI
  46. Cheshmeh Sefidi A, Ajorkaran F (2019) A novel MLP-ANN approach to predict solution gas-oil ratio. Pet Sci Technol 37:2302-2308. https:// doi. org/ 10. 1080/ 10916 466. 2018. 14907 · DOI
  47. Chhantyal K, Viumdal H, Mylvaganam S (2017) Ultrasonic level scanning for monitoring mass flow of complex fluids in open channels-a novel sensor fusion approach using AI techniques. In: Proceedings of IEEE sensors. https:// doi. org/ 10. 1109/ ICS- ENS. 2017. 82340 10 · DOI
  48. Van Dao D, Ly HB, Trinh SH, Le TT, Pham BT (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials (basel). https:// doi. org/ 10. 3390/ ma120 60983 · DOI
  49. Deosarkar MP, Sathe VS (2012) Predicting effective viscosity of mag- netite ore slurries by using artificial neural network. Powder Technol. https:// doi. org/ 10. 1016/j. powtec. 2011. 12. 058 · DOI
  50. Desouky M, Tariq Z, Aljawad MS, Alhoori H, Mahmoud M, AlShehri D (2020a) Data-driven acid fracture conductivity correlations honoring different mineralogy and etching patterns. ACS Omega 5(27):16919-16931. https:// doi. org/ 10. 1021/ acsom ega. 0c021 23 · DOI
  51. Desouky M, Aljawad MS, Alhoori H, Al-Shehri D (2020b) Evaluating the effects of acid fracture etching patterns on conductivity esti- mation using machine learning techniques. In: Paper presented at the SPE Europec, Virtual. https:// doi. org/ 10. 2118/ 200527-MS Devi PRS, Baskaran R, Abirami S (2015) Multi-label learning with class-based features using extended centroid-based classification technique (CCBF). Procedia Comput Sci 54:405-411. https:// doi. org/ 10. 1016/j. procs. 2015. 06. 047 · DOI
  52. Di H, Gao D (2014) Gray-level transformation and Canny edge detec- tion for 3D seismic discontinuity enhancement. Comput Geosci 72:192-200. https:// doi. org/ 10. 1016/j. cageo. 2014. 07. 011 · DOI
  53. Di H, Gao D (2016) Improved estimates of seismic curvature and flex- ure based on 3D surface rotation in the presence of structure dip. Geophysics. https:// doi. org/ 10. 1190/ GEO20 15-0258. · DOI
  54. Du S, Liang B, Yuanbo L (2017) Field study: embedded discrete frac- ture modeling with artificial intelligence in permian basin for shale formation, In: SPE annual technical conference and exhi- bition. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 187202-MS Dupriest FE, Koederitz WL (2005) Maximizing drill rates with real- time surveillance of mechanical specific energy. In: All days. SPE. https:// doi. org/ 10. 2118/ 92194-MS Eleibide M, Hassan AM, Mahmoud M, Abdulraheem A, Elkatatny S (2018) Intelligent prediction of acid-fracturing performance in carbonates reservoirs. Society of Petroleum Engineers-SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018, · DOI
  55. Elkatatny S, Mahmoud M, Tariq Z, Abdulraheem A (2018a) New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network. Neural Comput Appl 30:2673-2683. https:// doi. org/ 10. 1007/ s00521-017-2850-x · DOI
  56. Elkatatny S, Tariq Z, Mahmoud M (2016) Real time prediction of drill- ing fluid rheological properties using artificial neural networks visible mathematical model (white box). J Pet Sci Eng 146:1202- 1210. https:// doi. org/ 10. 1016/j. petrol. 2016. 08. 021 · DOI
  57. Elkatatny S, Tariq Z, Mahmoud M, Abdulraheem A (2018b) New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs. Petroleum 4:408-418. https:// doi. org/ 10. 1016/j. petlm. 2018. 04. 002 · DOI
  58. Elkatatny S, Tariq Z, Mahmoud M, Abdulraheem A, Mohamed I (2019) An integrated approach for estimating static Young's modulus using artificial intelligence tools. Neural Comput Appl 31:4123-4135. https:// doi. org/ 10. 1007/ s00521-018-3344-1 · DOI
  59. El-Sebakhy EA (2009) Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. J Petrol Sci Eng 64(1-4):25-34. https:// doi. org/ 10. 1016/j. petrol. 2008. 12. 006 · DOI
  60. Feifei C, Yonggang D, Junbin ZW, Weifeng W (2015) Application of neural network and fuzzy mathematic theory in evaluating the adaptability of inflow control device in horizontal well. J Pet Sci Eng. https:// doi. org/ 10. 1016/j. petrol. 2015. 07. 020 · DOI
  61. Ganji-Azad E, Rafiee-Taghanaki S, Rezaei H, Arabloo M (2014) Hossein Ali Zamani, Reservoir fluid PVT properties modeling using adaptive neuro-fuzzy inference systems. J Nat Gas Sci Eng 21:951-961. https:// doi. org/ 10. 1016/j. jngse. 2014. 10. 009 · DOI
  62. Gharbi RBC, Elsharkawy AM (1999) Neural network model for esti- mating the PVT properties of middle east crude oils. SPE Reserv Eval Eng 2:255-265. https:// doi. org/ 10. 2118/ 56850-PA · DOI
  63. Gharbi RB, Elsharkawy AM, Karkoub M (1999) Universal neural- network-based model for estimating the PVT properties of crude oil systems. Energy Fuels 13:454-458. https:// doi. org/ 10. 1021/ ef980 143v · DOI
  64. Gholanlo HH, Amirpour M, Ahmadi S (2016) Estimation of water satu- ration by using radial based function artificial neural network in carbonate reservoir: a case study in Sarvak formation. Petroleum 2:166-170. https:// doi. org/ 10. 1016/j. petlm. 2016. 04. 002 · DOI
  65. Gidh Y, Purwanto A, Bits S (2012) Artificial neural network drill- ing parameter optimization system improves ROP by predict- ing/managing bit wear. Paper presented at the SPE Intelligent Energy International, Utrecht, The Netherlands. https:// doi. org/ 10. 2118/ 149801-MS Gowida A, Elkatatny S, Al-Afnan S, Abdulraheem A (2020) New com- putational artificial intelligence models for generating synthetic formation bulk density logs while drilling. Sustainability 12:686. https:// doi. org/ 10. 3390/ su120 20686 · DOI
  66. Guitton A, Wang H, Trainor-Guitton W (2017) Statistical imaging of faults in 3D seismic volumes using a machine learning approach. https:// doi. org/ 10. 1190/ segam 2017-17589 633. · DOI
  67. Hale D (2013) Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics. https:// doi. org/ 10. 1190/ GEO20 12-0331. · DOI
  68. Hassan A, Abdulraheem A, Elkatatny S, Ahmed M (2017) New approach to quantify productivity of fishbone multilateral well. In: Proceedings-SPE annual technical conference and exhibi- tion. https:// doi. org/ 10. 2118/ 187458-ms · DOI
  69. Hassan A, Al-Majed A, Elkatatny S, Mahmoud M, Abdulraheem A, Nader M, Abughaban M, Khamis M (2018) Developing an effi- cient drilling system by coupling torque modelling with mechani- cal specific energy. In: Paper presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia. https:// doi. org/ 10. 2118/ 192251-MS · DOI
  70. Hassan A, Mahmoud M, Al-Majed A, Abdulraheem A (2020) A new technique to quantify the productivity of complex wells using artificial intelligence tools. In: Paper presented at the interna- tional petroleum technology conference, Dhahran, Kingdom of Saudi Arabia. https:// doi. org/ 10. 2523/ IPTC-19706-Abstr act · DOI
  71. He J, Tang M, Hu C, Tanaka S, Wang K, Wen X, Yusuf N (2021) Deep reinforcement learning for generalizable field development opti- mization. SPE J. https:// doi. org/ 10. 2118/ 203951-PA · DOI
  72. Hegde C, Gray K (2018) Evaluation of coupled machine learning mod- els for drilling optimization. J Nat Gas Sci Eng. https:// doi. org/ 10. 1016/j. jngse. 2018. 06. 006 · DOI
  73. Helle HB, Bhatt A (2002) Fluid saturation from well logs using com- mittee neural networks. Pet Geosci 8:109-118. https:// doi. org/ 10. 1144/ petgeo. 8.2. 109 · DOI
  74. Hemmati-Sarapardeh A, Shokrollahi A, Tatar A, Gharagheizi F, Mohammadi AH, Naseri A (2014) Reservoir oil viscosity deter- mination using a rigorous approach. Fuel 116:39-48. https:// doi. org/ 10. 1016/j. fuel. 2013. 07. 072 · DOI
  75. Hossain M (2018) Frugal innovation: a review and research agenda. J Clean Prod 182:926-936
  76. Hu Y, Ding Y, Wen F, Liu L (2019) Reliability assessment in dis- tributed multi-state series-parallel systems. Energy Procedia 159:104-110. https:// doi. org/ 10. 1016/j. egypro. 2018. 12. 026 · DOI
  77. Hua Y, Guo J, Zhao H (2015) Deep belief networks and deep learning. In: Proceedings of 2015 international conference on intelligent computing and internet of things. IEEE, pp. 1-4. https:// doi. org/ 10. 1109/ ICAIOT. 2015. 71115 · DOI
  78. Huang L, Dong X, Clee TE (2017) A scalable deep learning platform for identifying geologic features from seismic attributes. Lead Edge. https:// doi. org/ 10. 1190/ tle36 030249. · DOI
  79. Huang Y, Zheng W, Zhang D, Xi Y (2020) A modified Herschel-Bulk- ley model for rheological properties with temperature response characteristics of poly-sulfonated drilling fluid. Energy Sour Part A Recover Util Environ Eff 42:1464-1475. https:// doi. org/ 10. 1080/ 15567 036. 2019. 16048 61 · DOI
  80. Iino A, Jung HY, Onishi T, Datta-Gupta A (2020) Rapid simula- tion accounting for well interference in unconventional reser- voirs using fast marching method. https:// doi. org/ 10. 15530/ urtec-2020-2468 · DOI
  81. Jacobs T (2019) The oil and gas chat bots are coming. J Pet Technol 71:34-36. https:// doi. org/ 10. 2118/ 0219-0034-JPT · DOI
  82. Jafari Kenari SA, Mashohor S (2013) Robust committee machine for water saturation prediction. J Pet Sci Eng 104:1-10. https:// doi. org/ 10. 1016/j. petrol. 2013. 03. 009 · DOI
  83. Jeirani Z, Mohebbi A (2006) Artificial neural networks approach for estimating filtration properties of drilling fluids. J Japan Pet Inst 49:65-70. https:// doi. org/ 10. 1627/ jpi. 49. 65 · DOI
  84. Jui-Sheng C, Chien-Kuo C, Mahmoud F, Ismail A-T (2011) Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J Comput Civ Eng 25:242-253. https:// doi. org/ 10. 1061/ (ASCE) CP. 1943-5487. 00000 88 · DOI
  85. Kamalyar K, Sheikhi Y, Jamialahmadi M (2012) Using an artificial neural network for predicting water saturation in an iranian oil reservoir. Pet Sci Technol 30:35-45. https:// doi. org/ 10. 1080/ 10916 46100 37525 61 · DOI
  86. Kar S, Das S, Ghosh PK (2014) Applications of neuro fuzzy systems: a brief review and future outline. Appl Soft Comput 15:243-259. https:// doi. org/ 10. 1016/j. asoc. 2013. 10. 014 · DOI
  87. Kewalramani MA, Gupta R (2006) Concrete compressive strength pre- diction using ultrasonic pulse velocity through artificial neural networks. Autom Constr. https:// doi. org/ 10. 1016/j. autcon. 2005. 07. 003 · DOI
  88. Khamidy NI, Tariq Z, Syihab Z (2019) Development of ANN-based predictive model for miscible CO2 flooding in sandstone res- ervoir. In: SPE middle east oil and gas show and conference, MEOS, Proceedings. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 194726-MS Khashman A, Akpinar P (2017) Non-destructive prediction of con- crete compressive strength using neural networks. Procedia Comput Sci. https:// doi. org/ 10. 1016/j. procs. 2017. 05. 039 · DOI
  89. Khazaeni Y, Mohaghegh SD (2011) Intelligent production modeling using full field pattern recognition. SPE Reserv Eval Engi 14(6):735-749. https:// doi. org/ 10. 2118/ 132643-PA · DOI
  90. Kumar A, Ridha S, Ganet T, Vasant P, Ilyas SU (2020) Machine learn- ing methods for herschel-bulkley fluids in annulus: pressure drop predictions and algorithm performance evaluation. Appl Sci. https:// doi. org/ 10. 3390/ app10 072588 · DOI
  91. Lecampion B, Bunger A, Zhang X (2018) Numerical methods for hydraulic fracture propagation: a review of recent trends. J Nat Gas Sci Eng. https:// doi. org/ 10. 1016/j. jngse. 2017. 10. 012 · DOI
  92. Liu H, Lang B (2019) Machine learning and deep learning methods for intrusion detection systems: a survey. Appl Sci 9:4396. https:// doi. org/ 10. 3390/ app92 04396 · DOI · Paper page
  93. Livingstone DJ, Manallack DT (1993) Statistics using neural networks: chance effects. J Med Chem 36(9):1295-1297. https:// doi. org/ 10. 1021/ jm000 61a023 · DOI
  94. Livingstone D, Manallack D, Tetko I (1997) Data modelling with neu- ral networks: advantages and limitations. J Comput Aided Mol Des 11:135-142. https:// doi. org/ 10. 1023/A: 10080 74223 811 · DOI
  95. Lolon E, Hamidieh K, Weijers L, Mayerhofer M, Melcher H, Oduba O (2016) Evaluating the relationship between well parameters and production using multivariate statistical models: a middle bakken and three forks case history. In: Day 3 Thu, February 11, 2016. SPE. https:// doi. org/ 10. 2118/ 179171-MS · DOI
  96. Luo G, Tian Y, Bychina M, Ehlig-Economides C (2019) Production- strategy insights using machine learning: application for bakken shale. SPE Reservoir Eval Eng 22(3):800-816
  97. Luthi SM, Bryant ID (1997) Well-log correlation using a back-propa- gation neural network. Math Geol 29:413-425. https:// doi. org/ 10. 1007/ BF027 69643 · DOI
  98. Maghrabi S, Kulkarni D, Teke K, Kulkarni SD, Jamison D (2014) Modeling of shale-erosion behavior in aqueous drilling fluids. In: Society of petroleum engineers-European unconventional resources conference and exhibition 2014: unlocking European potential. https:// doi. org/ 10. 2118/ 167691-ms · DOI
  99. Maity D, Ciezobka J (2019) Designing a robust proppant detection and classification workflow using machine learning for subsurface fractured rock samples post hydraulic fracturing operations. J Petrol Sci Eng 172:588-606. https:// doi. org/ 10. 1016/j. petrol. 2018. 09. 062 · DOI
  100. Maniar H, Ryali S, Kulkarni MS, Abubakar A (2018) Machine-learning methods in geoscience. In: SEG technical program expanded abstracts 2018. Society of exploration geophysicists, pp. 4638- 4642. https:// doi. org/ 10. 1190/ segam 2018-29972 18. · DOI
  101. Mardi M, Nurozi H, Edalatkhah S (2012) A water saturation prediction using artificial neural networks and an investigation on cementa- tion factors and saturation exponent variations in an Iranian oil well. Pet Sci Technol 30:425-434. https:// doi. org/ 10. 1080/ 10916 46090 34520 33 · DOI
  102. Martinelli G, Eidsvik J, Sinding-Larsen R, Rekstad S, Mukerji T (2013) Building bayesian networks from basin-modelling scenarios for improved geological decision making. Pet Geosci. https:// doi. org/ 10. 1144/ petge o2012-057 · DOI
  103. Masoudi R, Mohaghegh SD, Yingling D, Ansari A, Amat H, Mohamad N, Sabzabadi A, Dipak M (2020) Subsurface analytics case study; reservoir simulation and modeling of highly complex off- shore field in malaysia, using artificial intelligent and machine learning. In: SPE annual technical conference and exhibition, October 2020. https:// doi. org/ 10. 2118/ 201693-MS · DOI
  104. Matin SS, Farahzadi L, Makaremi S, Chelgani SC, Sattari G (2018) Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Appl Soft Comput 70:980-987. https:// doi. org/ 10. 1016/j. asoc. 2017. 06. 030 · DOI
  105. Mehrad M, Bajolvand M, Ramezanzadeh A (2020) Jalil Ghavidel Ney- charan, developing a new rigorous drilling rate prediction model using a machine learning technique. J Petrol Sci Eng 192:107338. https:// doi. org/ 10. 1016/j. petrol. 2020. 107338 · DOI
  106. Melville PD, Guruswamy S (2002) Integration of 3D seismic with a reservoir model for reservoir characterisation. In: Abu Dhabi international petroleum exhibition and conference. https:// doi. org/ 10. 2118/ 78511-MS · DOI
  107. Miri R, Sampaio JHB, Afshar M, Lourenco A (2007) Development of artificial neural networks to predict differential pipe sticking in iranian offshore oil fields. In: International oil conference and exhibition in Mexico. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 108500-MS · DOI
  108. Mishra S, Schuetter J, Zhong M, Lafollette R (2015) Data analytics for production optimization in unconventional reservoirs. https:// doi. org/ 10. 15530/ urtec-2015-21670 05 · DOI
  109. Mnati KH, Hadi HA (2018) Prediction of penetration rate and cost with artificial neural network for alhafaya oil field. Iraqi J Chem Pet Eng 19:21-27. https:// doi. org/ 10. 31699/ IJCPE. 2018.4.3 · DOI
  110. Mohaghegh S (2000) Virtual-intelligence applications in petroleum engineering: part 3-fuzzy logic. J Pet Technol 52:82-87. https:// doi. org/ 10. 2118/ 62415-JPT · DOI
  111. Mohaghegh S (2011) Reservoir simulation and modeling based on artificial intelligence and data mining (AI & DM). J Nat Gas Sci Eng 3(6):697-705. https:// doi. org/ 10. 1016/j. jngse. 2011. 08. 003 · DOI
  112. Mohaghegh SD (2017) Shale analytics: data-driven analytics in uncon- ventional resources. Springer International Publishing, Cham. https:// doi. org/ 10. 1007/ 978-3-319-48753-3 · DOI
  113. Mollajan A, Memarian H, Jalali MR (2013) Prediction of reservoir water saturation using support vector regression in an Iranian carbonate reservoir. In: 47th US rock mechanics/geomechanics symposium 2013. pp. 1872-1877
  114. Moore BA, Rougier E, O'Malley D, Srinivasan G, Hunter A, Viswana- than H (2018) Predictive modeling of dynamic fracture growth in brittle materials with machine learning. Comput Mater Sci. https:// doi. org/ 10. 1016/j. comma tsci. 2018. 01. 056 · DOI
  115. Murray AS, Cunningham RA (1955) Effect of mud column pressure on drilling rates. Trans AIME. https:// doi. org/ 10. 2118/ 505-g · DOI
  116. Murtaza M, Mahmoud M, Tariq Z (2020) Experimental investigation of a novel, efficient, and sustainable hybrid silicate system in oil and gas well cementing. Energy Fuels 34:7388-7396. https:// doi. org/ 10. 1021/ acs. energ yfuels. 0c010 01 · DOI
  117. Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng. https:// doi. org/ 10. 1016/j. jobe. 2018. 01. 007 · DOI
  118. Najibi AR, Ghafoori M, Lashkaripour GR, Asef MR (2015) Empirical relations between strength and static and dynamic elastic proper- ties of Asmari and Sarvak limestones, two main oil reservoirs in Iran. J Pet Sci Eng 126:78-82. https:// doi. org/ 10. 1016/j. petrol. 2014. 12. 010 · DOI
  119. Navrátil J, King A, Rios J, Kollias G, Torrado R, Codas A (2019) Accelerating physics-based simulations using end-to-end neural network proxies: an application in oil reservoir modeling. Front Big Data 2:33. https:// doi. org/ 10. 3389/ fdata. 2019. 00033 · DOI
  120. Negara A, Ali S, AlDhamen A, Kesserwan H, Jin G (2017) Unconfined compressive strength prediction from petrophysical properties and elemental spectroscopy using support-vector regression. In: Day 4 Thu, April 27, 2017. SPE. https:// doi. org/ 10. 2118/ 188077-MS · DOI
  121. Nooruddin HA, Anifowose F, Abdulraheem A (2014) Using soft com- puting techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density. Comput Geosci 64:72-80. https:// doi. org/ 10. 1016/j. cageo. 2013. 11. 007 · DOI
  122. Numbere OG, Azuibuike II, Ikiensikimama SS (2013) bubble point pressure prediction model for niger delta crude using artificial neural Network approach. In: Paper presented at the SPE Nigeria Annual international conference and exhibition, Lagos, Nigeria. https:// doi. org/ 10. 2118/ 167586-MS Oguntade T, Ojo T, Efajemue E, Oni B, Idaka J (2020) Application of ANN in predicting water based mud rheology and filtration properties. https:// doi. org/ 10. 2118/ 203720-MS · DOI
  123. Omar S, Ngadi A, Jebur HH (2013) machine learning techniques for anomaly detection: an overview. Int J Comput Appl 79:33-41. https:// doi. org/ 10. 5120/ 13715-1478 · DOI
  124. Osarogiagbon A, Muojeke S, Venkatesan R, Khan F, Gillard P (2020) A new methodology for kick detection during petroleum drilling using long short-term memory recurrent neural network. Process Saf Environ Prot 142:126-137. https:// doi. org/ 10. 1016/j. psep. 2020. 05. 046 · DOI
  125. Osman EA, Aggour MA (2003) Determination of drilling mud density change with pressure and temperature made simple and accurate by ANN. In: Proceedings of the middle east oil show. https:// doi. org/ 10. 2118/ 81422-ms · DOI
  126. Osman EA, Abdel-Wahhab OA, Al-Marhoun MA (2001) Prediction of Oil PVT Properties Using Neural Networks. Paper presented at the SPE Middle East Oil Show, Manama, Bahrain. https:// doi. org/ 10. 2118/ 68233-MS · DOI
  127. Pandey SK, Janghel RR (2019) Recent deep learning techniques, challenges and its applications for medical healthcare system: a review. Neural Process Lett 50:1907-1935. https:// doi. org/ 10. 1007/ s11063-018-09976-2 · DOI
  128. Qi L, Carr TR (2006) Neural network prediction of carbonate litho- facies from well logs, big bow and sand arroyo creek fields, Southwest Kansas. Comput Geosci 32:947-964. https:// doi. org/ 10. 1016/j. cageo. 2005. 10. 020 · DOI
  129. Qi J, Li F, Marfurt K (2017) Multiazimuth coherence. Geophysics. https:// doi. org/ 10. 1190/ GEO20 17-0196. · DOI
  130. Rabbani E, Sharif F, Koolivand Salooki M, Moradzadeh A (2012) Application of neural network technique for prediction of uni- axial compressive strength using reservoir formation properties. Int J Rock Mech Min Sci 56:100-111. https:// doi. org/ 10. 1016/j. ijrmms. 2012. 07. 033 · DOI
  131. Rahmanifard H, Plaksina T (2018) Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs. J Nat Gas Sci Eng 52:367-378. https:// doi. org/ 10. 1016/j. jngse. 2018. 01. 047 · DOI
  132. Rahmati AS, Tatar A (2019) Application of radial basis function (RBF) neural networks to estimate oil field drilling fluid density at ele- vated pressures and temperatures. Oil Gas Sci Technol-Rev d'IFP Energies Nouv. https:// doi. org/ 10. 2516/ ogst/ 20190 21 · DOI
  133. Raj J (2019) A comprehensive survey on the computational intelligence techniques and its applications. J ISMAC 1:147-159. https:// doi. org/ 10. 36548/ jismac. 2019.3. 002 · DOI
  134. Ramamoorthy A, Yampolskiy R (2018) Beyond map?: the race for artificial general Intelligence. ITU J 1(1):77-84
  135. Ramirez AM, Valle GA, Romero F, Jaimes M (2017) Prediction of PVT properties in crude oil using machine learning techniques MLT. In: SPE Latin American and Caribbean petroleum engineering conference proceedings. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 185536-ms · DOI
  136. Rasheed A, San O, Kvamsdal T (2020) Digital twin: values, chal- lenges and enablers from a modeling perspective. IEEE Access 8:21980-22012. https:// doi. org/ 10. 1109/ ACCESS. 2020. 29701 · DOI · Paper page
  137. Rasheed Khan M, Tariq Z, Abdulraheem A (2018) Machine learning derived correlation to determine water saturation in complex lith- ologies. In: Society of petroleum engineers-SPE Kingdom of Saudi Arabia annual technical symposium and exhibition 2018, SATS 2018. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 192307-ms · DOI
  138. Rastogi A, Sharma A (2019) Quantifying the impact of fracturing chemicals on production performance using machine learning. In: Paper presented at the SPE Liquids-Rich Basins conference- North America, Odessa, Texas, USA. https:// doi. org/ 10. 2118/ 197095-MS · DOI
  139. Razi MM, Mazidi M, Razi FM, Aligolzadeh H, Niazi S (2013) Artifi- cial neural network modeling of plastic viscosity, yield point, and apparent viscosity for water-based drilling fluids. J Dispers Sci Technol. https:// doi. org/ 10. 1080/ 01932 691. 2012. 704746 · DOI
  140. Reiber F, Vos BE, Eide SE (1999) On-line torque & drag: a real-time drilling performance optimization tool. In: Proceedings of the IADC/SPE Asia Pacific drilling technology conference, APDT. https:// doi. org/ 10. 2523/ 52836-ms · DOI
  141. Rolon L, Mohaghegh SD, Ameri S, Gaskari R, McDaniel B (2009) Using artificial neural networks to generate synthetic well logs. J Nat Gas Sci Eng. https:// doi. org/ 10. 1016/j. jngse. 2009. 08. 003 · DOI
  142. Rooki R, Doulati Ardejani F, Moradzadeh A (2014) Hole cleaning prediction in foam drilling using artificial neural network and multiple linear regression. Geomaterials. https:// doi. org/ 10. 4236/ gm. 2014. 41005 · DOI
  143. Rooki R, Doulati Ardejani F, Moradzadeh A, Kelessidis VC, Nourozi M (2012) Prediction of terminal velocity of solid spheres falling through newtonian and non-newtonian pseudoplastic power law fluid using artificial neural network. Int J Miner Process. https:// doi. org/ 10. 1016/j. minpro. 2012. 03. 012 · DOI
  144. Safiuddin M, Raman SN, Salam MA, Jumaat MZ (2016) Modeling of compressive strength for self-consolidating high-strength con- crete incorporating palm oil fuel ash. Materials (basel). https:// doi. org/ 10. 3390/ ma905 0396 · DOI
  145. Sagheer A, Kotb M (2019) Time series forecasting of petroleum pro- duction using deep LSTM recurrent networks. Neurocomputing 323:203-213. https:// doi. org/ 10. 1016/j. neucom. 2018. 09. 082 · DOI
  146. Salehi SM, Honarvar B (2014) Automatic identification of formation iithology from well log data: a machine learning approach. J Pet Sci Res. https:// doi. org/ 10. 14355/ jpsr. 2014. 0302. · DOI
  147. Samnejad M, Gharib Shirangi M, Ettehadi R (2020) A digital twin of drilling fluids rheology for real-time rig operations . https:// doi. org/ 10. 4043/ 30738-MS Sebtosheikh MA, Salehi A (2015) Lithology prediction by support vector classifiers using inverted seismic attributes data and petro- physical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. J Pet Sci Eng 134:143-149. https:// doi. org/ 10. 1016/j. petrol. 2015. 08. 001 · DOI
  148. Seyyedattar M, Ghiasi MM, Zendehboudi S, Butt S (2020) Determina- tion of bubble point pressure and oil formation volume factor: extra trees compared with LSSVM-CSA hybrid and ANFIS mod- els. Fuel 269:116834. https:// doi. org/ 10. 1016/j. fuel. 2019. 116834 · DOI
  149. Shabbir J, Anwer T (2018) Artificial intelligence and its role in near future. J Latex Class Files 1(8):1-11
  150. Shadravan A, Tarrahi M, Amani M (2017) Intelligent tool to design drilling, spacer, cement slurry, and fracturing fluids by use of machine-learning algorithms. SPE Drill Complet 32:131-140. https:// doi. org/ 10. 2118/ 175238-PA · DOI
  151. Shahkarami A, Mohaghegh S, Gholami V, Haghighat A, Moreno D (2014) Modeling pressure and saturation distribution in a CO 2 storage project using a surrogate reservoir model (SRM). Greenh Gases Sci Technol 4:289-315. https:// doi. org/ 10. 1002/ ghg. 1414 · DOI
  152. Shahriar A, Nehdi ML (2011) Modeling rheological properties of oil well cement slurries using artificial neural networks. J Mater Civ Eng. https:// doi. org/ 10. 1061/ (ASCE) MT. 1943-5533. 00003 40 · DOI
  153. Sharma MSR, O'Regan M, Baxter CDP, Moran K, Vaziri H, Naraya- nasamy R (2010) Empirical relationship between strength and geophysical properties for weakly cemented formations. J Pet Sci Eng 72:134-142. https:// doi. org/ 10. 1016/j. petrol. 2010. 03. 011 · DOI
  154. Shojaei M-J, Bahrami E, Barati P, Riahi S (2014) Adaptive neuro-fuzzy approach for reservoir oil bubble point pressure estimation. J Nat Gas Sci Eng 20:214-220. https:// doi. org/ 10. 1016/j. jngse. 2014. 06. 012 · DOI
  155. Shokir EMEM (2004) Prediction of the hydrocarbon saturation in low resistivity formation via artificial neural network. In: Proceedings of the SPE Asia Pacific conference on integrated modelling for asset management. Society of petroleum engineers, pp. 35-40. https:// doi. org/ 10. 2118/ 87001-ms · DOI
  156. Shokrollahi A, Tatar A, Safari H (2015) On accurate determination of PVT properties in crude oil systems: committee machine intelligent system modeling approach. J Taiwan Inst Chem Eng 55:17-26. https:// doi. org/ 10. 1016/j. jtice. 2015. 04. 009 · DOI
  157. Van Si L, Chon BH (2018) Effective prediction and management of a CO2 flooding process for enhancing oil recovery using artificial neural networks. J Energy Resour Technol Trans ASME. https:// doi. org/ 10. 1115/1. 40380 54 · DOI
  158. Sibai FN, Hosani HI, Naqbi RM, Dhanhani S, Shehhi S (2011) Iris recognition using artificial neural networks. Expert Syst Appl 38:5940-5946. https:// doi. org/ 10. 1016/j. eswa. 2010. 11. 029 · DOI
  159. Silva AA, Lima Neto IA, Misságia RM, Ceia MA, Carrasquilla AG, Archilha NL (2015) Artificial neural networks to support petro- graphic classification of carbonate-siliciclastic rocks using well logs and textural information. J Appl Geophys 117:118-125. https:// doi. org/ 10. 1016/j. jappg eo. 2015. 03. 027 · DOI
  160. Solomon O, Adewale D, Anyanwu C (2017) Fracture width prediction and loss prevention material sizing in depleted formations using artificial intelligence. In: Paper presented at the SPE Nigeria annual international conference and exhibition, Lagos, Nigeria. https:// doi. org/ 10. 2118/ 189068-MS · DOI
  161. Somasundaram S, Mund B, Soni R, Sharda R (2017) Seismic attribute analysis for fracture detection and porosity prediction: a case study from tight volcanic reservoirs, Barmer Basin, India. Lead Edge. https:// doi. org/ 10. 1190/ tle36 11094 7b1. · DOI
  162. Somvanshi M, Chavan P, Tambade S, Shinde SV (2016) A review of machine learning techniques using decision tree and support vector machine. In: 2016 International conference on computing communication control and automation (ICCUBEA). IEEE, pp. 1-7. https:// doi. org/ 10. 1109/ ICCUB EA. 2016. 78600 · DOI
  163. Srinivasan G, Hyman JD, Osthus DA, Moore BA, O'Malley D, Karra S, Rougier E, Hagberg AA, Hunter A, Viswanathan HS (2018) Quantifying topological uncertainty in fractured systems using graph theory and machine learning. Sci Rep. https:// doi. org/ 10. 1038/ s41598-018-30117-1 · DOI
  164. Sun H, Belhaj H, Tao G, Vega S, Liu L (2019) Rock properties evalu- ation for carbonate reservoir characterization with multi-scale digital rock images. J Pet Sci Eng 175:654-664. https:// doi. org/ 10. 1016/j. petrol. 2018. 12. 075 · DOI
  165. Sun Q, Ertekin T (2020) Screening and optimization of polymer flood- ing projects using artificial-neural-network (ANN) based proxies. J Pet Sci Eng. https:// doi. org/ 10. 1016/j. petrol. 2019. 106617 · DOI
  166. Tahmasebi P, Javadpour F, Sahimi M (2017) Data mining and machine learning for identifying sweet spots in shale reservoirs. Expert Syst Appl 88:435-447. https:// doi. org/ 10. 1016/j. eswa. 2017. 07. 015 · DOI
  167. Tariq Z, Elkatatny S, Mahmoud M, Abdulraheem A (2016) A New artificial intelligence based empirical correlation to predict sonic travel time, In: International petroleum technology conference. International petroleum technology conference. https:// doi. org/ 10. 2523/ iptc-19005-ms · DOI
  168. Tariq Z, Elkatatny S, Mahmoud M, Ali AZ, Abdulraheem A, (2017a) A new technique to develop rock strength correlation using artificial intelligence tools. In: Society of petroleum engineers-SPE res- ervoir characterisation and simulation conference and exhibition, RCSC 2017. Society of petroleum engineers, pp. 1340-1353. https:// doi. org/ 10. 2118/ 186062-MS · DOI
  169. Tariq Z, Elkatatny S, Mahmoud M, Ali AZ, Abdulraheem A (2017b) A new approach to predict failure parameters of carbonate rocks using artificial intelligence tools. In: Society of petroleum engi- neers-SPE Kingdom of Saudi Arabia annual technical sympo- sium and exhibition 2017. Society of petroleum engineers, pp. 1428-1440. https:// doi. org/ 10. 2118/ 187974-MS · DOI
  170. Tariq Z (2018) An automated flowing bottom-hole pressure prediction for a vertical well having multiphase flow using computational intelligence techniques. In: Society of petroleum engineers-SPE Kingdom of Saudi Arabia annual technical symposium and exhi- bition 2018, SATS 2018. Society of petroleum engineers. https:// doi. org/ 10. 2118/ 192184-MS · DOI
  171. Tariq Z, Abdulraheem A, Mahmoud M, Ahmed A (2018) A rigorous data-driven approach to predict Poisson's ratio of carbonate rocks using a functional network. Petrophysics 59:761-777. https:// doi. org/ 10. 30632/ PJV59 N6-2018a2 · DOI
  172. Tariq Z, Mahmoud M, Abdulraheem A (2019) Core log integration: a hybrid intelligent data-driven solution to improve elastic param- eter prediction. Neural Comput Appl. https:// doi. org/ 10. 1007/ s00521-019-04101-3 · DOI
  173. Tariq Z, Mahmoud M, Abdulraheem A (2020a) An intelligent data- driven model for dean-stark water saturation prediction in car- bonate rocks. Neural Comput Appl 32:11919-11935. https:// doi. org/ 10. 1007/ s00521-019-04674-z · DOI
  174. Tariq Z, Murtaza M, Mahmoud M (2020b) Effects of nanoclay and silica flour on the mechanical properties of class G cement. ACS Omega 5:11643-11654. https:// doi. org/ 10. 1021/ acsom ega. 0c009 · DOI
  175. Tariq Z, Murtaza M, Mahmoud M (2020c) Development of new rheo- logical models for class G cement with nanoclay as an additive using machine learning techniques. ACS Omega 5:17646-17657. https:// doi. org/ 10. 1021/ acsom ega. 0c021 22 · DOI
  176. Tariq Z, Mahmoud M, Abdulraheem A (2021) Machine learning-based improved pressure-volume-temperature correlations for black oil reservoirs. J Energy Resour Technol. https:// doi. org/ 10. 1115/1. 40505 79 · DOI
  177. Taunk K, De S, Verma S, Swetapadma A (2019) A Brief review of nearest neighbor algorithm for learning and classification. In: 2019 International conference on intelligent computing and con- trol systems (ICCS). IEEE, pp. 1255-1260. https:// doi. org/ 10. 1109/ ICCS4 5141. 2019. 90657 · DOI
  178. Tian X, Daigle H (2019) Preferential mineral-microfracture associa- tion in intact and deformed shales detected by machine learning object detection. J Nat Gas Sci Eng 63:27-37. https:// doi. org/ 10. 1016/j. jngse. 2019. 01. 003 · DOI
  179. Tomiwa O, Oluwatosin R, Temiloluwa O, Oluwasanmi O, Joy I (2019) Improved water based mud using solanum tuberosum formu- lated biopolymer and application of artificial neural network in predicting mud rheological properties . https:// doi. org/ 10. 2118/ 198861-MS Trtnik G, Kavčič F, Turk G (2009) Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrason- ics. https:// doi. org/ 10. 1016/j. ultras. 2008. 05. 001 · DOI
  180. Tschannen V, Delescluse M, Ettrich N, Keuper J (2020) Extracting horizon surfaces from 3D seismic data using deep learning. Geo- physics 85:N17-N26. https:// doi. org/ 10. 1190/ geo20 19-0569. · DOI
  181. Van SL, Chon BH (2017b) Applicability of an artificial neural network for predictingwater-alternating-CO2 performance. Energies. https:// doi. org/ 10. 3390/ en100 70842 · DOI
  182. Le Van S, Chon BH (2017a) Evaluating the critical performances of a CO2-Enhanced oil recovery process using artificial neural network models. J Pet Sci Eng. https:// doi. org/ 10. 1016/j. petrol. 2017. 07. 034 · DOI
  183. Virginia D (2018) Responsible artificial intelligence: designing AI for human values. ITU J 1(1):1-8
  184. Wang S, Chen Z, Chen S (2019) Applicability of deep neural networks on production forecasting in Bakken shale reservoirs. J Pet Sci Eng 179:112-125. https:// doi. org/ 10. 1016/j. petrol. 2019. 04. 016 · DOI
  185. Wattenbarger R, El-Banbi A, Villegas M, Maggard J (1998) Production analysis of linear flow into fractured tight gas wells. In: Paper SPE 39931 presented at the SPE rocky mountain regional/low- permeability reservoirs symposium, Denver, Colorado, pp. 5-8 April. Paper Presented at the Low-permeability Reservoirs Sym- posium, Denver, CO Weyrauch T, Herstatt C (2016) What is frugal innovation? Three defin- ing criteria. J Frugal Innov 2(1):1-17
  186. Xiong W, Ji X, Ma Y, Wang Y, Benhassan NM, Ali MN, Luo Y (2018) Seismic fault detection with convolutional neural network. Geo- physics. https:// doi. org/ 10. 1190/ geo20 17-0666. · DOI
  187. YIlmaz I, Yuksek AG (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41:781-795. https:// doi. org/ 10. 1007/ s00603-007-0138-7 · DOI
  188. Yadollahi MM, Benli A, Demirboğa R (2015) Prediction of compres- sive strength of geopolymer composites using an artificial neu- ral network. Mater Res Innov 19(6):453-458. https:// doi. org/ 10. 1179/ 14330 75X15Y. 00000 00020 · DOI
  189. Yang D, Rahman MK, Chen Y (2008) Bottomhole assembly analysis by finite difference differential method. Int J Numer Methods Eng. https:// doi. org/ 10. 1002/ nme. 2221 · DOI
  190. You L, Tan Q, Kang Y, Xu C, Lin C (2018) Reconstruction and predic- tion of capillary pressure curve based on particle swarm opti- mization-back propagation neural network method. Petroleum 4:268-280. https:// doi. org/ 10. 1016/j. petlm. 2018. 03. 004 · DOI
  191. Yu W, Sepehrnoori K (2013) Optimization of multiple hydraulically fractured horizontal wells in unconventional gas reservoirs. In: SPE production and operations symposium, proceedings. https:// doi. org/ 10. 2118/ 164509-ms · DOI
  192. Zapico JL, Gonzlez-Buelga A, Gonzlez MP, Alonso R (2008) Finite element model updating of a small steel frame using neural net- works. Smart Mater Struct. https:// doi. org/ 10. 1088/ 0964-1726/ 17/4/ 045016 · DOI
  193. Zhao T, Jayaram V, Roy A, Marfurt KJ (2015) A comparison of classi- fication techniques for seismic facies recognition. Interpretation. https:// doi. org/ 10. 1190/ INT-2015-0044. · DOI
  194. Zheng Y, Zhang Q, Yusifov A, Shi Y (2019) Applications of supervised deep learning for seismic interpretation and inversion. Lead Edge 38:526-533. https:// doi. org/ 10. 1190/ tle38 070526. · DOI
  195. Zhou H, Niu X. Fan H, Wang G (2016) Effective calculation model of drilling fluids density and ESD for HTHP well while drilling. In: Society of petroleum engineers-IADC/SPE Asia Pacific drill- ing technology conference. https:// doi. org/ 10. 2118/ 180573-MS · DOI
  196. Zou G, Ren K, Sun Z, Peng S, Tang Y (2019) Fault interpretation using a support vector machine: A study based on 3D seismic mapping of the Zhaozhuang coal mine in the Qinshui Basin, China. J Appl Geophys. https:// doi. org/ 10. 1016/j. jappg eo. 2019. 103870 · DOI

Authors
Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E., El-Husseiny, A., Alarifi, S. A., Mahmoud, M., & Abdulraheem, A.
Affiliations
King Fahd University of Petroleum and Minerals (SA)
Corresponding author
Alarifi, Sulaiman A.
ORCID
Venue
Journal of Petroleum Exploration and Production Technology
Publisher
Springer Science and Business Media LLC
Published
2021-09-24
Volume
11
Issue
12
Pages
4339-4374
ISSN
  • 2190-0558 (ISSN-L)
  • 2190-0566
DOI
10.1007/s13202-021-01302-2
OpenAlex
W3204654155
Cited by
232
References
2
Citations by year
2026: 50 · 2025: 73 · 2024: 59 · 2023: 36 · 2022: 14
FWCI
3.39
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Petroleum Exploration and Production Technology (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Mechanics of Materials › Hydrocarbon exploration and reservoir analysis
  • Physical Sciences › Engineering › Ocean Engineering › Oil and Gas Production Techniques
  • Physical Sciences › Engineering › Ocean Engineering › Reservoir Engineering and Simulation Methods
Keywords
machine learning, data science, oil and gas industry, petroleum exploration, reservoir characterization, oil well drilling, well stimulation, unconventional reservoirs, logs, prediction, classification, clustering, big data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review