Application of artificial intelligence techniques in the petroleum industry: a review

• Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A comprehensive review of swarm optimization algorithms. PLoS ONE 10(5):e0122827

  Article  Google Scholar 

• Abraham A, Guo H, Liu H (2006) Swarm intelligence: foundations, perspectives and applications. Swarm intelligent systems. Springer, Berlin, pp 3–25

• Afshar M, Gholami A, Asoodeh M (2014) Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling. Korean J Chem Eng 31(3):496–502

  Article  Google Scholar 

• Ahmadi MA (2011) Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Pet Explor Prod Technol 1(2–4):99–106

  Article  Google Scholar 

• Ahmadi M-A, Ebadi M (2014) Fuzzy modeling and experimental investigation of minimum miscible pressure in gas injection process. Fluid Phase Equilib 378:1–12

  Article  Google Scholar 

• Ahmadi MA, Golshadi M (2012) Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. J Pet Sci Eng 98:40–49

  Article  Google Scholar 

• Ahmadi MA, Zendehboudi S, Lohi A et al (2013) Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization. Geophys Prospect 61(3):582–598

  Article  Google Scholar 

• Ahmadi MA, Zendehboudi S, Bahadori A et al (2014) Recovery rate of vapor extraction in heavy oil reservoirs? Experimental, statistical, and modeling studies. Ind Eng Chem Res 53(41):16091–16106

  Article  Google Scholar 

• Alam S, Dobbie G, Koh YS et al (2014) Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol Comput 17:1–13

  Article  Google Scholar 

• Algosayir MM (2012) Optimization of steam/solvent injection methods: application of hybrid techniques with improved algorithm configuration. University of Alberta, Edmonton

  Google Scholar 

• Andersen MG (2009) Reservoir production optimization using genetic algorithms and artificial neural networks Master’s thesis, Department of Computer Science, NTNU. http://hdl.handle.net/11250/251403

• Assareh E, Behrang M, Assari M et al (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35(12):5223–5229

  Article  Google Scholar 

• Attia M, Mahmoud M, Abdulraheem A et al (2013) Evaluation of the pressure drop due to multi phase flow in horizontal pipes using fuzzy logic and neural networks. In: SPE Middle East oil and gas show and conference, society of petroleum engineers

• Atyabi A, Powers DM (2013) Cooperative area extension of PSO-transfer learning vs. uncertainty in a simulated swarm robotics. In: ICINCO (1)

• Awotunde AA, Mutasiem MA (2014) Efficient drilling time optimization with differential evolution. In: SPE Nigeria annual international conference and exhibition, society of petroleum engineers

• Azizi S, Awad MM, Ahmadloo E (2016) Prediction of water holdup in vertical and inclined oil–water two-phase flow using artificial neural network. Int J Multiph Flow 80:181–187

  Article  MathSciNet  Google Scholar 

• Bakyani AE, Sahebi H, Ghiasi MM et al (2016) Prediction of CO\(_2\)–oil molecular diffusion using adaptive neuro-fuzzy inference system and particle swarm optimization technique. Fuel 181:178–187

  Article  Google Scholar 

• Ballester PJ, Carter JN (2007) A parallel real-coded genetic algorithm for history matching and its application to a real petroleum reservoir. J Pet Sci Eng 59(3):157–168

  Article  Google Scholar 

• Bian X-Q, Han B, Du Z-M et al (2016) Integrating support vector regression with genetic algorithm for CO\(_2\)–oil minimum miscibility pressure (MMP) in pure and impure CO\(_2\) streams. Fuel 182:550–557

  Article  Google Scholar 

• Bittencourt AC, Horne RN (1997) Reservoir development and design optimization. In: SPE annual technical conference and exhibition, society of petroleum engineers

• Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. Workshops on applications of evolutionary computation. Springer, Berlin

• Boender CGE, Romeijn HE (1995) Stochastic methods. Handbook of global optimization. Springer, Berlin, pp 829–869

  Book  MATH  Google Scholar 

• Bonabeau E, Meyer C (2001) Swarm intelligence: a whole new way to think about business. Harv Bus Rev 79(5):106–115

  Google Scholar 

• Brits R, Engelbrecht AP, Van den Bergh F (2002) A niching particle swarm optimizer. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning. Orchid Country Club, Singapore

• Carroll JA Jr, Horne RN (1992) Multivariate optimization of production systems. J Pet Technol 44(07):782–831

  Article  Google Scholar 

• Chang Y, Yu G (2013) Multi-sub-swarm PSO classifier design and rule extraction In: International workshop on cloud computing and information security (CCIS), Atlantis Press

• Chapoy A, Mohammadi AH, Richon D (2007) Predicting the hydrate stability zones of natural gases using artificial neural networks. Oil Gas Sci Technol Revue de l’IFP 62(5):701–706

  Article  Google Scholar 

• Chen Z (2013) A genetic algorithm optimizer with applications to the SAGD process. University of Calgary, Calgary

  Google Scholar 

• Chen S, Li H, Yang D et al (2010) Optimal parametric design for water-alternating-gas (WAG) process in a CO\(_2\)-miscible flooding reservoir. J Can Petrol Technol 49(10):75–82

  Article  Google Scholar 

• Chiroma H, Abdulkareem S, Herawan T (2015) Evolutionary neural network model for West Texas Intermediate crude oil price prediction. Appl Energy 142:266–273

  Article  Google Scholar 

• Cohen SC, de Castro LN (2006) Data clustering with particle swarms. In: 2006 IEEE international conference on evolutionary computation. IEEE

• Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314

  Article  MathSciNet  MATH  Google Scholar 

• De Jong KA, Spears WM (1992) A formal analysis of the role of multi-point crossover in genetic algorithms. Ann Math Artif Intell 5(1):1–26

  Article  MATH  Google Scholar 

• De Reus N (1994) Assessment of benefits and drawbacks of using fuzzy logic, especially in fire control systems. Fysisch en Elektronisch Lab TNO the Hague (Netherlands)

• Decker J, Mauldon M (2006) Determining size and shape of fractures from trace data using a differential evolution algorithm. Golden Rocks 2006, the 41st US Symposium on Rock Mechanics (USRMS), American Rock Mechanics Association

• Dumitru C, Maria V (2013) Advantages and disadvantages of using neural networks for predictions. Ovidius Univ Ann Ser Econ Sci 13(1):444–449

• Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, NY

• Edelen MR (2003) Swarm intelligence and stigmergy: robotic implementation of foraging behavior Master’s thesis,Faculty of the Graduate School of the University of Maryland. http://hdl.handle.net/1903/107

• Edmunds N, Peterson J, Moini B (2010) Method for viscous hydrocarbon production incorporating steam and solvent cycling, Google Patents

• Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

  Book  MATH  Google Scholar 

• El-Abbasy MS, Senouci A, Zayed T et al (2014) Artificial neural network models for predicting condition of offshore oil and gas pipelines. Autom Constr 45:50–65

  Article  Google Scholar 

• Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, New York

  Google Scholar 

• Eslamloueyan R, Khademi M (2009) Estimation of thermal conductivity of pure gases by using artificial neural networks. Int J Therm Sci 48(6):1094–1101

  Article  Google Scholar 

• Farshi MM (2008) Improving genetic algorithms for optimum well placement. Stanford University, Stanford

  Google Scholar 

• Fathinasab M, Ayatollahi S (2016) On the determination of CO\(_2\)-crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods. Fuel 173:180–188

  Article  Google Scholar 

• Filgueiras PR, Portela NlA, Silva SR et al (2016) Determination of saturates, aromatics, and polars in crude oil by 13C NMR and support vector regression with variable selection by genetic algorithm. Energy Fuels 30(3):1972–1978

  Article  Google Scholar 

• Freuder E, Wallace M (2005) Constraint programming. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques, Chapter 9. Springer, pp 243–265

• Fujii H, Horne R (1995) Multivariate optimization of networked production systems. SPE Prod Facil 10(03):165–171

  Article  Google Scholar 

• Ganesan T, Elamvazuthi I, Shaari KZK et al (2013) Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production. Appl Energy 103:368–374

  Article  Google Scholar 

• Gomory RE (1958) Outline of an algorithm for integer solutions to linear programs. Bull Amer Math Soc 64(5): 275–278. https://projecteuclid.org/euclid.bams/1183522679

• Groth R (2000) Data mining: building competitive advantage. Prentice Hall PTR, Englewood Cliffs

  Google Scholar 

• Guria C, Goli KK, Pathak AK (2014) Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm. Pet Sci 11(1):97–110

  Article  Google Scholar 

• Güyagüler B, Horne RN, Rogers L et al (2002) Optimization of well placement in a Gulf of Mexico waterflooding project. SPE Reserv Eval Eng 5(03):229–236

  Article  Google Scholar 

• Hajizadeh Y, Christie MA, Demyanov V (2009) Application of differential evolution as a new method for automatic history matching. In: Kuwait international petroleum conference and exhibition, society of petroleum engineers

• Hajizadeh Y, Christie MA, Demyanov V (2010) Comparative study of novel population-based optimization algorithms for history matching and uncertainty quantification: PUNQ-S3 revisited. In: Abu Dhabi international petroleum exhibition and conference, society of petroleum engineers

• Holland J (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology. Control and artificial intelligence. MIT Press, Cambridge

  Book  Google Scholar 

• Huang Y, Huang G, Dong M et al (2003) Development of an artificial neural network model for predicting minimum miscibility pressure in CO\(_2\) flooding. J Pet Sci Eng 37(1):83–95

  Article  Google Scholar 

• Humphries TD, Haynes RD, James LA (2014) Simultaneous and sequential approaches to joint optimization of well placement and control. Comput Geosci 18(3–4):433–448

  Article  MathSciNet  Google Scholar 

• Husbands P, Copley P, Eldridge A et al (2007) An introduction to evolutionary computing for musicians. Evolutionary computer music. Springer, Berlin, pp 1–27

  Google Scholar 

• Jahangiri H (2007) Production optimization using smart well technology with differential evolution algorithm. University of Southern California, Graduate Student Symposium

• Jalalnezhad MJ, Kamali V (2016) Development of an intelligent model for wax deposition in oil pipeline. J Pet Explor Prod Technol 6(1):129–133

  Article  Google Scholar 

• Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence [Book Review]. IEEE Trans 42(10):1482–1484

• Kaewkamnerdpong B, Bentley PJ (2005) Perceptive particle swarm optimisation: an investigation. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005 (SIS 2005). IEEE

• Kamrani E (2010) Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO) . Master thesis, Department of Computer Engineering of Dalarna University

• Kar S, Das S, Ghosh PK (2014) Applications of neuro fuzzy systems: a brief review and future outline. Appl Soft Comput 15:243–259

  Article  Google Scholar 

• Kathrada M (2009) Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering. Heriot-Watt University, Edinburgh

  MATH  Google Scholar 

• Khademi M, Rahimpour M, Jahanmiri A (2010) Differential evolution (DE) strategy for optimization of hydrogen production, cyclohexane dehydrogenation and methanol synthesis in a hydrogen-permselective membrane thermally coupled reactor. Int J Hydrog Energy 35(5):1936–1950

  Article  Google Scholar 

• Kim Y, Jang H, Kim J et al (2017) Prediction of storage efficiency on CO\(_2\) sequestration in deep saline aquifers using artificial neural network. Appl Energy 185:916–928

  Article  Google Scholar 

• Klee V, Minty GJ (1970) How good is the simplex algorithm, DTIC Document

• Land AH, Doig AG (2010) An automatic method for solving discrete programming problems. 50 Years of integer programming 1958–2008. Springer, Berlin, pp 105–132

  MATH  Google Scholar 

• Lin C, Qing A, Feng Q (2011) A comparative study of crossover in differential evolution. J Heuristics 17(6):675–703

  Article  MATH  Google Scholar 

• Liu Y, Passino KM (2000) Swarm intelligence: literature overview. Department of Electrical Engineering, the Ohio State University

• Ma X, Gildin E, Plaksina T (2015) Efficient optimization framework for integrated placement of horizontal wells and hydraulic fracture stages in unconventional gas reservoirs. J Unconv Oil Gas Resour 9:1–17

  Article  Google Scholar 

• Mardani A, Jusoh A, Zavadskas EK (2015) Fuzzy multiple criteria decision-making techniques and applications-two decades review from 1994 to 2014. Expert Syst Appl 42(8):4126–4148

  Article  Google Scholar 

• Maria G (1998) IDENTIFICATION/DIAGNOSIS-adaptive random search and short-cut techniques for process model identification and monitoring. AIChE Symposium Series. American Institute of Chemical Engineers, New York, NY, 1971-c2002

• Mariajayaprakash A, Senthilvelan T, Gnanadass R (2015) Optimization of process parameters through fuzzy logic and genetic algorithm—a case study in a process industry. Appl Soft Comput 30:94–103

  Article  Google Scholar 

• Mirzabozorg A (2015) Incorporation of engineering knowledge in history matching, optimization, and uncertainty assessment frameworks with application to the SAGD process. University of Calgary, Calgary

  Google Scholar 

• Mohaghegh S (2000) Virtual-intelligence applications in petroleum engineering: part 1—artificial neural networks. J Pet Technol 52(09):64–73

  Article  Google Scholar 

• Mohagheghian E (2016) An application of evolutionary algorithms for WAG optimisation in the Norne Field, Memorial University of Newfoundland

• Mohamed L, Christie MA, Demyanov V (2011) History matching and uncertainty quantification: multiobjective particle swarm optimisation approach. Society of Petroleum Engineers, SPE EUROPEC/EAGE annual conference and exhibition

• Mohammadi M, Kharrat R, Hashemi A (2015) Developing a fuzzy logic model to predict asphaltene precipitation during natural depletion based on experimental data. Iran J Oil Gas Sci Technol 4(2):40–49

  Google Scholar 

• Mohanty S (2005) Estimation of vapour liquid equilibria of binary systems, carbon dioxide-ethyl caproate, ethyl caprylate and ethyl caprate using artificial neural networks. Fluid Phase Equilib 235(1):92–98

  Article  Google Scholar 

• Novák V, Perfilieva I, Mockor J (2012) Mathematical principles of fuzzy logic. Springer, Berlin

  MATH  Google Scholar 

• Olatunji SO, Selamat A, Azeez ARA (2015) Modeling permeability and PVT properties of oil and gas reservoir using hybrid model based on type-2 fuzzy logic systems. Neurocomputing 157:125–142

  Article  Google Scholar 

• Onwunalu JE, Durlofsky LJ (2010) Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput Geosci 14(1):183–198

  Article  MATH  Google Scholar 

• Ouenes A (2000) Practical application of fuzzy logic and neural networks to fractured reservoir characterization. Comput Geosci 26(8):953–962

  Article  Google Scholar 

• Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, Berlin

  MATH  Google Scholar 

• Ramadhas A, Jayaraj S, Muraleedharan C et al (2006) Artificial neural networks used for the prediction of the cetane number of biodiesel. Renew Energy 31(15):2524–2533

  Article  Google Scholar 

• Rammay MH, Abdulraheem A (2014) Automated history matching using combination of adaptive neuro fuzzy system (ANFIS) and differential evolution algorithm. SPE Large Scale Computing and Big Data Challenges in Reservoir Simulation Conference and Exhibition, Society of Petroleum Engineers

• Ravandi EG, Nezamabadi-Pour H, Monfared AF et al (2014) Reservoir characterization by a combination of fuzzy logic and genetic algorithm. Pet Sci Technol 32(7):840–847

  Article  Google Scholar 

• Rumelhart DE, McClelland JL, Group PR (1986) Parallel distributed processing: explorations in the microstructure of cognition: foundations, vol. 1

• Saemi M, Ahmadi H, Varjani A (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59(1):97–105

  Article  Google Scholar 

• Salmachi A, Sayyafzadeh M, Haghighi M (2013) Infill well placement optimization in coal bed methane reservoirs using genetic algorithm. Fuel 111:248–258

  Article  Google Scholar 

• Santhosh EC, Sangwai JS (2016) A hybrid differential evolution algorithm approach towards assisted history matching and uncertainty quantification for reservoir models. J Pet Sci Eng 142:21–35

  Article  Google Scholar 

• Senthilkumar S (2014) Practical applications of swarm intelligence and evolutionary computation, hybrid soft computing. In: International journal of swarm intelligence and evolutionary computation 2014

• Shen Q, Shi W-M, Yang X-P et al (2006) Particle swarm algorithm trained neural network for QSAR studies of inhibitors of platelet-derived growth factor receptor phosphorylation. Eur J Pharm Sci 28(5):369–376

  Article  Google Scholar 

• Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation, 1999 (CEC 99). IEEE

• Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, Berkeley

  MATH  Google Scholar 

• Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325

  Article  MathSciNet  MATH  Google Scholar 

• Üçoluk G (2002) Genetic algorithm solution of the TSP avoiding special crossover and mutation. Intell Autom Soft Comput 8(3):265–272

  Article  Google Scholar 

• Vazquez O, Fursov I, Mackay E (2016) Automatic optimization of oilfield scale inhibitor squeeze treatment designs. J Pet Sci Eng 147:302–307

  Article  Google Scholar 

• Velez-Langs O (2005) Genetic algorithms in oil industry: an overview. J Petrol Sci Eng 47(1):15–22

  Article  Google Scholar 

• Wang P (2003) Development and applications of production optimization techniques for petroleum fields. Stanford University, Stanford

  Google Scholar 

• Wang J, Buckley JS (2006) Automatic history matching using differential evolution algorithm. International Symposium of the Society of Core Analysis, Trondheim

  Google Scholar 

• Wang X, Qiu X (2013) Application of particle swarm optimization for enhanced cyclic steam stimulation in a offshore heavy oil reservoir. arXiv preprint arXiv:1306.4092

• Wang C, Gao J, Yang H et al (2011) Waveform inversion of cross-well data with cooperative coevolutionary differential evolution algorithm. SEG technical program expanded abstracts 2011, society of exploration geophysicists, pp 2752–2756

• Watson A, Seinfeld J, Gavalas G et al (1980) History matching in two-phase petroleum reservoirs. Soc Pet Eng J 20(06):521–532

  Article  Google Scholar 

• Wu W (2015) Oil and gas pipeline risk assessment model by fuzzy inference systems and artificial neural network. University of Regina, Faculty of Graduate Studies and Research

• Wu Y-C, Lee W-P, Chien C-W (2011) Modified the performance of differential evolution algorithm with dual evolution strategy. In: International conference on machine learning and computing

• Xu S, Zhang M, Zeng F et al (2015) Application of genetic algorithm (GA) in history matching of the vapour extraction (VAPEX) heavy oil recovery process. Nat Resour Res 24(2):221–237

  Article  Google Scholar 

• Xue Y, Cheng L, Mou J et al (2014) A new fracture prediction method by combining genetic algorithm with neural network in low-permeability reservoirs. J Pet Sci Eng 121:159–166

  Article  Google Scholar 

• Yang E (2009) Selection of target wells and layers for fracturing with fuzzy mathematics method. In: Sixth international conference on fuzzy systems and knowledge discovery, 2009 (FSKD’09). IEEE

• Yetilmezsoy K, Fingas M, Fieldhouse B (2011) An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation. Colloids Surf Physicochem Eng Asp 389(1):50–62

  Article  Google Scholar 

• Yilmaz S, Demircioglu C, Akin S (2002) Application of artificial neural networks to optimum bit selection. Comput Geosci 28(2):261–269

  Article  Google Scholar 

• Yin D, Wu T (2009) Notice of retraction optimizing well for fracturing by fuzzy analysis method of applying computer. In: 2009 First international conference on information science and engineering. IEEE

• Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

  Article  MATH  Google Scholar 

• Zendehboudi S, Ahmadi MA, James L et al (2012) Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization. Energy Fuels 26(6):3432–3447

  Article  Google Scholar 

• Zendehboudi S, Rajabzadeh AR, Bahadori A et al (2014) Connectionist model to estimate performance of steam-assisted gravity drainage in fractured and unfractured petroleum reservoirs: enhanced oil recovery implications. Ind Eng Chem Res 53(4):1645–1662

  Article  Google Scholar 

• Zhang D, Gong X, Peng L (2009) Estimating geostatistics variogram parameters based on hybrid orthogonal differential evolution algorithm. In: International symposium on intelligence computation and applications. Springer, Berlin

• Zhang J, Zhang H, Yu J et al (2014) Fast one-dimensional velocity model determination using station-pair differential times based on the differential evolution method in microseismic monitoring. SEG Technical Program Expanded Abstracts 2014, Society of Exploration Geophysicists, pp 4832–4836

• Zhang Y, Chen M, Jin Y et al (2016) Experimental study and artificial neural network simulation of the wettability of tight gas sandstone formation. J Nat Gas Sci Eng 34:387–400

  Article  Google Scholar 

• Zhou Q, Wu W, Liu D et al (2016) Estimation of corrosion failure likelihood of oil and gas pipeline based on fuzzy logic approach. Eng Fail Anal 70:48–55

  Article  Google Scholar 

• Zoveidavianpoor M, Samsuri A, Shadizadeh SR (2012) Fuzzy logic in candidate-well selection for hydraulic fracturing in oil and gas wells: a critical review. Int J Phys Sci 7(26):4049–4060

  Google Scholar 

Download references