№2, 2018


Ramiz H. Shikhaliyev

The article deals with the application of intelligent technologies in oil and gas fields for solving various problems. These include the intellectualization of analysis of large amounts of data collected from oil and gas fields, the intellectualization of the drilling process, the forecast of reserves and the optimization of oil and gas production activity, the optimization of the location and management of oil and gas fields, etc. At the same time, intellectualization of oil and gas fields is impossible without the use of information technology. The intellectualization of oil and gas fields leads to an increase in the effectiveness of remote monitoring and management of oil and gas production, the accuracy of geophysical research, safety, and the reduction of structural costs and the continued competitiveness of oil companies (pp.41-47).

Keywords: oil and gas fields, artificial intelligence, intelligent technologies, information technologies, exploration and exploitation of oil and gas fields, forecast of reserves and optimization of oil and gas production activity, drilling process intellectualization.
  • Anthony E. P., et al., Value Generated Through Automated Workflows Using Digital Oilfield Concepts - Case Study / SPE Kuwait Oil and Gas Show and Conference, SPE-167327-MS. 2013.
  • Kochnev A.A. The concept of an "intellectual" field // Master`s Journal, 2015, No. 2, pp.165–171.
  • https://rogtecmagazine.com/wp-content/uploads/2014/10/083.pdf
  • Zhu Z.-P., Pan R.-F., Chen Z., Li G.-Q., Zheng G.-S., Analysis on cloud data service platform for digital oilfields // Journal of Digital Information Management, 2016, vol.14, no.6, pp.413–422.
  • Aliguliyev R.M., Imamverdiyev Y.N., Abdullayeva F., Investigation of Big Data Analytics for Oil and Gas Industry as Analytics-As-A-Service on Cloud Computing Platform // Problems of Information Technologies, 2016, No1, pp.11–26.
  • marketsandmarkets.com/PressReleases/digital-oilfield.asp
  • Aminzadeh F., Applications of AI and soft computing for challenging problems in the oil industry // Journal of Petroleum Science and Engineering, 2005, 47, pp.5–14.
  • Making the Intelligent Oil Field a Reality: www-03.ibm.com/industries/ca/en/portal/ files/making_the_intelligent_oilfield_reality.pdf
  • The intelligent oilfield: meeting the challenges of today’s oil and gas exploration and production industry: www-935.ibm.com/services/us/imc/pdf/ge510-3882-oil-gas-challenges. pdf
  • Cyber security vulnerabilities for the oil and gas industry: www.dnvgl.com/oilgas/download/lysne-committee-study.html
  • The active cyber defense cycle: A strategy to ensure oil and gas infrastructure cyber security: www.plantengineering.com/single-article/the-active-cyber-defense-cycle-a-strategy-to-ensure-oil-and-gas-infrastructure-cyber-security/8d7b2eaedb5c1875131c5e8a0e5efbad.html
  • Bello O., Holzmann J., Yaqoob T., Teodoriu C., Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art // Journal of Artificial Intelligence and Soft Computing Research, 2015, vol.5, no.2, pp.121–139.
  • Elkatatny S., Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data // Journal of Energy Resources Technology, 2018, vol.140, no.7, pp.
  • Srivastava A., Artificial Intelligence: The Future Of Oil And Gas, http://www.digitalistmag.com/digital-supply-networks/2017/08/07/artificial-intelligence-future-of-oil-gas-05259467
  • Handhel A. M., Prediction of reservoir permeability from wire logs data using artificial neural networks // Iraqi Journal of Science, 2009, vol.50, no.1, pp.67–74.
  • Romero-Salcedo M., Ramirez-Sabag J., Lopez H., Hernandez D.A., Ramirez R., Analysis of prediction of pressure data in oil wells using artificial neural networks / Electronics, Robotics and Automotive Mechanics Conference (CERMA), 2010, pp.51–55.
  • Mohseni H., Esfandyari M., Asl E. H., Application of artificial neural networks for prediction of Sarvak Formation lithofacies based on well log data, Marun oil field, SW Iran // Geopersia, 2015, vol. 5, no.2, pp.111–123.
  • Ghazwan J., Application of neural network to optimize oil field production // Asian Transactions on Engineering, 2012, vol.2, no.3, pp.10–23.
  • Bello O., Teodoriu C., Yaqoob T. Obinwanne A., et al., Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways // SPE Nigeria Annual International Conference and Exhibition, 2016.
  • Kadkhodaie‐Ilkhchi A., A fuzzy logic approach to estimating hydraulic flow units from well log data: a case study from the ahwaz oilfield, south Iran // Journal of Petroleum Geology, 2009, vol.32, no.1, pp.67–78.
  • Atiaa A. M., Handhel A. M., A fuzzy logic approach to infer reservoir permeability from depth and porosity measurements for Mishrif limestone Formation at Nasyria Oil Field, south of Iraq // Journal of Al-Anbar university for pure science, 2009, vol.3, no.1, pp.
  • Widarsono B., Atmoko H., et al., Application of Fuzzy Logic for Determining Production Allocation in Commingle Production Wells // SPE Asia Pacific Oil and Gas Conference and Exhibition, 2005.
  • Odedele T. O., Ibrahim H. D., Oil well performance diagnosis system using fuzzy logic inference models / Proceedings of the World Congress on Engineering, 2014, vol.1, pp.80–85.
  • Bermudez F., Carvajal G.A., Moricca G., Dhar J., Adam F. M., Al-Jasmi A., H.K. Goel H. Nasr, A Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of KwIDF Project), / SPE Intelligent Energy Conference & Exhibition, 2014, pages 13.
  • Jain R., Expert Systems: A Management Perspective / Vikalpa, 1989, vol.14, no.4, pp.17–28.
  • Zhong Y., Zhu M., Zhang Z., An Intelligent Prediction Model for Oilfield Production Based on Fuzzy Expert System // Fuzzy Engineering and Operations Research, 2012, pp.475–484.
  • Nanjun L., Wan D., Jie W., Xia X., Junhui L., Application of Rule Based Expert System to Sand Control in Oil Fields / Fifth International Conference on Intelligent Computation Technology and Automation, 2012, pp.110–113.
  • Ghallab S. A., Badr N., Hashem M., Salem A. B. and Tolba M. F., A Fuzzy Expert System For Petroleum Prediction / Recent Advances in Computer Science, 2013, pp.70–75.
  • allerin.com/blog/how-machine-learning-is-revolutionizing-the-oil-and-gas-industry
  • Oliveira V. L. C., et al., A multi-agent system for oil field management / 11th IFAC Workshop on Intelligent Manufacturing Systems The International Federation of Automatic Control, 2013, pp.35–40.
  • Matei N. M., Dobrescu S.-L., Ichim L., Popescu D., Pricop E., A Multi-Agent System for Management of Control Functions as Services in Onshore Oilfield / 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2017.
  • Montes G., et al., The Use of Genetic Algorithms in Well Placement Optimization / SPE Latin American and Caribbean Petroleum Engineering Conference, 2001.
  • Lazo Lazo J., Pacheco M., Vellasco M., Dias M., Real Option Decision Rules For Oil Field Development Under Market Uncertainty Using Genetic Algorithms And Monte Carlo Simulation / 7th Annual Real Options International Conference, 2003.
  • Lazo Lazo J., et al., Real Options and Genetic Algorithms to Approach of the Optimal Decision Rule for Oil Field Development Under Uncertainties // Theoretical Advances and Applications of Fuzzy Logic and Soft Computing, 2007, pp.445–454.
  • Ariadji T., Sukarno P., et al., Optimization of Vertical Well Placement for Oil Field Development Based on Basic Reservoir Rock Properties using a Genetic Algorithm // Journal of Engineering and Technological Sciences, 2012, vol.44, no.2, pp.106–127.
  • Abou-Sayed A., Data Mining Applications in the Oil and Gas Industry // Journal of Petroleum Technology, 2015, vol.64, no.10, pp.88–95.
  • Aulia A., Keat T. B., et al., Smart Oilfield Data Mining for Reservoir Analysis // International Journal of Engineering & Technology IJET-IJENS, 2010, vol.10, no.06, pp.78–88.
  • Kravis S., Irrgang R., A Case Based System for Oil and Gas Well Design with Risk Assessment // Applied Intelligence, 2005, vol.23, no.1, pp.39–53.
  • Jafar A., Analysing Complex Oil Well Problems through Case-Based Reasoning, Doctoral thesis, 2007.
  • Chang L., Yan T., et al., The Application of Based on Case-Based Reasoning for Drilling Parameter Optimization in HaiTa Oilfield // Advanced Materials Research, 2014, vol.936, pp.1560–1564.
  • Aliguliyev R.M., Imamverdiyev Y.N. Conceptual Big Data Architecture for Oil and Gas Industry // Problems of Information Technologies, 2017, No1, pp.3-14.
  • Aliguliyev R.M., Imamverdiyev Y.N. Big Data Strategy for Oil and Gas Industry: General Trends // Information Technologies Problems, 2017, No2, pp.34-47.
  • Hacirahimova M.Sh. "Big Data" as a key component of decision-making in oil and gas industry. I Republic scientific-practical conference "Big data: capabilities, multidisciplinary problems and perspectives", Baku, 2016, pp. 162-165,
  • Xiong C., Zhang X., Zhao R. et al., Internet of Things Remote Intelligent Monitor System of Oil & Gas Field / 2nd International Conference on Manufacturing Science and Information Engineering, 2017, pp.52–56.
  • Eremin N.A., Dmitrievsky A.N., Tikhomirov L.I. Present and future of intellectual fields // Oil. Gas. Innovations, 2015, no. 12, pp. 44–49.