Application and development trend of artificial intelligence in petroleum exploration and development

MCCARTHY, 2006, A proposal for the Dartmouth summer research project on artificial intelligence, AI Magazine, 27, 12

ZHANG, 2018, Synthetic well logs generation via Recurrent Neural Networks, Petroleum Exploration and Development, 45, 598, 10.1016/S1876-3804(18)30068-5

JIANG, 2018, Lithology identification model by well logging based on boosting tree algorithm, Well Logging Technology, 42, 395

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ZHANG H, LIU Y, ZHANG Y, et al. Automatic seismic facies interpretation based on an enhanced encoder-decoder structure. San Antonio: 2019 SEG Annual Meeting, 2019.

PHAN S, SEN M. Deep learning with cross-shape deep Boltzmann machine for pre-stack inversion problem. San Antonio: 2019 SEG Annual Meeting, 2019.

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JIA, 2020, Data-driven optimization for fine water injection in a mature oil field, Petroleum Exploration and Development, 47, 629, 10.1016/S1876-3804(20)60084-2

JIA, 2018, Oil reservoir water flooding flowing area identification based on the method of streamline clustering artificial intelligence, Petroleum Exploration and Development, 45, 312, 10.1016/S1876-3804(18)30036-3

WANG, 2020, Production prediction at ultra-high water cut stage via Recurrent Neural Network, Petroleum Exploration and Development, 47, 1009, 10.1016/S1876-3804(20)60119-7

KUBOTA L K, REINERT D. Machine learning forecasts oil rate in mature onshore field jointly driven by water and steam injection. SPE 196152-MS, 2019.

BAO Anqi, GILDIN E, HUANG Jianhua, et al. Data-driven end-to-end production prediction of oil reservoirs by EnKF-enhanced Recurrent Neural Networks. SPE 199005-MS, 2020.

TARIQ Z, ABDULRAHEEM A. An artificial intelligence approach to predict the water saturation in carbonate reservoir rocks. SPE 195804-MS, 2019.

SHAHKARAMI, 2020, Applications of smart proxies for subsurface modeling, Petroleum Exploration and Development, 47, 372, 10.1016/S1876-3804(20)60057-X

ARTUN, 2020, Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference, Petroleum Exploration and Development, 47, 383, 10.1016/S1876-3804(20)60058-1

SENGEL A, TURKARSLAN G. Assisted history matching of a highly heterogeneous carbonate reservoir using hydraulic flow units and artificial neural networks. SPE 200541-MS, 2020.

ZHANG, 2017

COSTA, 2014, Application of artificial neural networks in a history matching process, Journal of Petroleum Science and Engineering, 123, 30, 10.1016/j.petrol.2014.06.004