Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.
Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.
This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.
 A. Aamodt, E. Plaza, Case-Based Reasoning: Fundamental Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications, Vol. 7, No. 1, pp. 39-59, 1994.
 F. Anifowose, A. Abdulraheem, Fuzzy Logic-Driven and SVM-Driven Hybrid Computational Intelligence Models Applied to Oil and Gas Reservoir Characterization. Journal of Natural Gas Science and Engineering 3, 2011.
 Bhattacharyya Pushpak, Introduction to Artificial Intelligent, 2011.
 D. Dashevskiy, V. Dubinsky, J.D Macpherson, Application of Neural Networks for Predictive Control in Drilling Dynamics. SPE 56442, Baker Hughes and University of Houston, 1999.
 A. Esmaeil, B. Elahifar, G. Thonhauser, R.K. Fruhwirth, ROP Modeling using Neural Network and Drill String Vibration Data. SPE 163330, University of Leoben, 2001.
 P. Fletcher, P.V. Coveney, C.M: Methven, Predicting The Quality and Performance of Oilfield Cements using Artificial Neural Networks and FTIR Spectroscopy, SPE 28824, 1994.
 Gentry Braswell, Artificial Intelligence Comes of Age in Oil and Gas. Journal of Petroleum Technology, Issues 2013-01, 2013.
 Gharbi et al. 2005. An Introduction to Artificial Intelligence Applications in Petroleum Exploration and Production.
 Y. Gidh, A. Purwanto, H. Ibrahim, Artificial Neural Network Drilling Parameter Optimization System Improves ROP By Predicting/Managing Bit Wear. SPE 149801, Smith Bits, 2012.
 Jianhong, Artificial intelligence and data mining: algorithms and applications, 2003.
 Jack V. Tu, Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes. Journal of Clinical Epidemiology, 1996.
 R. Jahanbakhshi, R. Keshavarzi, Intelligent Prediction of Wellbore Stability in Oil and GasWells: An Artificial Neural Network Approach. ARMA 12-243, Islamic Azad University, Tehran, Iran, 2012.
 James Lara, Artificial Neural Networks for Therapeutic Protein Engineering. Medicinal Protein Engineering. CRC Press, Dec 1, 2008.
 S.A.Kalogirou, Artificial Intelligence for the Modelling and Control Of Combustion Processes: A Review. Prog Energy Combust Science 2003; 29:515–66, 2003.
 S.A. Kalogirou, Artificial Intelligence in Energy and Renewable Energy Systems. Nova Publisher; 2007; 1-60021-261-1, 2007.
 V. Kecman, Learning and Soft Computing, Cambridge, Massachusetts, MIT Press, 2001.
 C.J. Lakhmi, N.M. Martin, Fusion of Neural Networks, Fuzzy Sets and Genetic Algorithms: Industrial Applications. ISBN:0849398045, 1998.
 Lian, Z., Zhou, Y., Zhao, Q., Hou, Z. 2010. A Study on Drilling Risk Real Time Recognition Technology Based on Fuzzy Reasoning. SPE 131886.
 Larry R. Medsker, Hybrid Intelligent Systems, 1995.
 L.R Medsker, Microcomputer Applications of Hybrid Intelligent Systems. Journal of Network and Computer Applications. Volume 19, Issue 2, Pages 213–234, April 1996.
 Mellit Adel, Artificial Intelligence Technique for Modeling and Forecasting Of Solar Radiation Data: A Review. Journal International Journal of Artificial Intelligence and Soft Computing archive Volume 1 Issue 1, Pages 52-76, 2008.
 A. Mellit, S.A. Kalogirou, L. Hontoria, S. Shaari, Artificial Intelligence Techniques for Sizing Photovoltaic Systems: A Review. Renewable and Sustainable Energy Reviews, 2008.
 Mohamed Benghanem, Artificial Intelligence Techniques for Prediction of Solar Radiation Data: A Review. International Journal of Renewable Energy Technology Issue Volume 3, Number 2/2012.
 National Oilwell Varco, Research paper. 2013. Drill Bit Selector, October 2013.
 A.S Nejad, K. Shahbazi, Petroleum University of Technology, Ahwaz, Iran, International Journal of Computer Applications (0975-8887), 2001.
 D. Patterson, Introduction to Artificial Intelligence and Expert Systems. Prentice Hall, Inc, 1990.
 A. Popa, C. Malamma, J. Hicks, Case-Based Reasoning Approach for Well Failure Diagnostics and Planning. SPE 114229-MS, SPE Western Regional and Pacific Section AAPG Joint Meeting, Bakersfield, California, USA, 29 March-2 April 2008.
 R. Rooki, F. D. Ardejani, A. Moradzadeh, Hole Cleaning Prediction in Foam Drilling using Artificial Neural Network and Multiple Linear Regression, Birjand University of technology, Iran, 2014.
 T. Sadiq, I.S Nashwi, Using neural networks for prediction of formation fracture gradient, Kuwait University, 2000.
 T. Sadiq, R. Gharbi, Prediction of Frictional Drag and Transmission Of Slack-Off Force In Horizontal Wells Using Neural Networks. SPE 51083, Kuwait University, 1998.
 L. Saputelli, H. Malki, J. Canelon, M. Nikolaou, A Critical Overview of Artificial Neural Network Applications in the Context of Continuous Oil Field Optimization. SPE Annual Technical Conference and Exhibition, 29 September-2 October, San Antonio, Texas, 2002.
 A. Shabalov, E. Semenkin, P. Galushin, Integration of Intelligent Information Technologies Ensembles for Modeling and Classification. Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems - Volume Part I, 2012.
 D. Shahab Mohaghegh, Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks. SPE 58046-JPT, 2000.
 Shahab D. Mohaghegh, Yasaman Khazaen, Application of Artificial Intelligence in the Upstream Oil and Gas Industry (pp.1-38). Nova Science Publishers, Inc., New York, 2011.
 S.R. Shadizadeh, F. Karimi, M. Zoveidavianpoor, Drilling Stuck Pipe Prediction in Iranian Oil Fields: An Artificial Neural Network Approach, Petroleum University of technology, Abadan, Iran, 2010.
 E.M. Shokir, Artificial Intelligence: a new tool in oil and gas industry, 2001.
 E.M Shokir, M.K. Emera, S.M. Eid, A.W.Wally, A New Optimization Model for 3d Well Design. Oil & Gas Science and Technology Vol. 59, No. 3, pp. 255-266, 2004.
 S.V. Shokouhi, P. Skalle, A. Aamodt, F. Srmo, Integration of Real-Time Data and Past Experiences for Reducing Operational Problems, 2009.
 C. Si ruvuri, S. Nagarakanti, R. Samuel, Stuck Pipe Prediction and Avoidance: A Convolutional Neural Network Approach. IADC/SPE 98378, 2006
 D. M. Tate, A.E. Smith, Unequal-Area Facility Layout by Genetic Search. IIE Transactions 27, 465-472, 1995.
 V. Vapnik, The Nature of Statistical Learning Theory. 2nd Ed, New York, Springer p.1-314, 1995.
 Y. Wang, M. Duan, D. Wang, J. Liu, Y. Dong, A Model for Deepwater Floating Platform Selection Based on BP Artificial Neural Networks. International Society of Offshore and Polar Engineers, 2011
 V. Yamaliev, E. Imaeva, T. Salakhov, About the Deep Drilling Equipment Technical Condition Recognition Method, Ufa State Petroleum Technological University, 2009.