Production optimization under waterflooding with long short-term memory and metaheuristic algorithm

Wiggins, 1998, An approach to reservoir management, SPE Repr. Ser. Lake, 2014 Guyaguler, 2002, Optimization of well placement in a gulf of Mexico waterflooding project, SPE Reservoir Eval. Eng., 10.2118/78266-PA Mamghaderi, 2013, Optimization of waterflooding performance in a layered reservoir using a combination of capacitance-resistive model and genetic algorithm method, J. Energy Resour. Technol., 10.1115/1.4007767 Mogollón, 2017, New trends in waterflooding project optimization, SPE Lat. Am. Caribb. Pet. Eng. Conf. Proc. Hong, 2017, Robust production optimization with capacitance-resistance model as precursor, Comput. Geosci., 10.1007/s10596-017-9666-8 Ogbeiwi, 2018, An approach to waterflood optimization: case study of the reservoir X, J. Pet. Explor. Prod. Technol., 10.1007/s13202-017-0368-5 Rao Bellout, 2012, Joint optimization of oil well placement and controls, Comput. Geosci., 10.1007/s10596-012-9303-5 Liu, 2016, Gradient-based multi-objective optimization with applications to waterflooding optimization, Comput. Geosci., 20, 10.1007/s10596-015-9523-6 Al-Aghbari, 2021, Multi-objective optimization of Brugge field for short-term and long-term waterflood management, Arabian J. Sci. Eng. Mohaghegh, 2011, Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM), J. Nat. Gas Sci. Eng., 10.1016/j.jngse.2011.08.003 Mohaghegh, 2017 Mohaghegh, 2012, Grid-Based Surrogate Reservoir Modeling (SRM) for fast track analysis of numerical reservoir simulation models at the grid block level, Soc. Pet. Eng. West. Reg. Meet. Mohaghegh, 2006, Quantifying uncertainties associated with reservoir simulation studies using surrogate reservoir models, Proc. SPE Annu. Tech. Conf. Exhib. Mohaghegh, 2006, Uncertainty analysis of a giant oil field in the middle east using surrogate reservoir model Vida, 2019, Smart proxy modeling of SACROC CO2-EOR, Fluids, 10.3390/fluids4020085 Shahkarami, 2020, Applications of smart proxies for subsurface modeling, Petrol. Explor. Dev., 10.1016/S1876-3804(20)60057-X Shahkarami, 2014, Artificial intelligence (AI) assisted history matching He, 2016, Reservoir simulation using smart proxy in SACROC unit - case study Alenezi, 2017, Developing a smart proxy for the SACROC water-flooding numerical reservoir simulation model Jalali, 2009, Reservoir simulation and uncertainty analysis of enhanced CBM production using artificial neural networks Kalantari-Dahaghi, 2011, A new practical approach in modelling and simulation of shale gas reservoirs: application to New Albany Shale, Int. J. Oil Gas Coal Technol. Nait Amar, 2018, Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization, Arabian J. Sci. Eng., 10.1007/s13369-018-3173-7 Menad, 2019, An efficient methodology for multi-objective optimization of water alternating CO2 EOR process, J. Taiwan Inst. Chem. Eng., 99, 154, 10.1016/j.jtice.2019.03.016 Kim, 2020, Robust optimization of the locations and types of multiple wells using CNN based proxy models, J. Petrol. Sci. Eng., 193, 10.1016/j.petrol.2020.107424 Kim, 2021, Efficient and robust optimization for well patterns using a PSO algorithm with a CNN-based proxy model, J. Petrol. Sci. Eng., 207, 10.1016/j.petrol.2021.109088 Deng, 2021, Data-driven proxy model for waterflood performance prediction and optimization using Echo State Network with Teacher Forcing in mature fields, J. Petrol. Sci. Eng., 197, 10.1016/j.petrol.2020.107981 Ng, 2021, Smart proxy modeling of a fractured reservoir model for production optimization: implementation of metaheuristic algorithm and probabilistic application, Nat. Resour. Res., 30, 2431, 10.1007/s11053-021-09844-2 Nait Amar, 2021, Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms, J. Petrol. Sci. Eng., 10.1016/j.petrol.2021.109038 Ng, 2021, Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization, J. Pet. Explor. Prod. Technol., 10.1007/s13202-021-01199-x Yousefi, 2021, Interwell connectivity identification in immiscible gas-oil systems using statistical method and modified capacitance-resistance model: a comparative study, J. Petrol. Sci. Eng., 198, 10.1016/j.petrol.2020.108175 Nait Amar, 2020, Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR, Greenh. Gases Sci. Technol. Talebkeikhah, 2020, Experimental measurement and compositional modeling of crude oil viscosity at reservoir conditions, J. Taiwan Inst. Chem. Eng., 109, 35, 10.1016/j.jtice.2020.03.001 Nait Amar, 2020, Predicting thermal conductivity of carbon dioxide using group of data-driven models, J. Taiwan Inst. Chem. Eng., 113, 165, 10.1016/j.jtice.2020.08.001 Mehrjoo, 2020, Modeling interfacial tension of methane-brine systems at high pressure and high salinity conditions, J. Taiwan Inst. Chem. Eng., 114, 125, 10.1016/j.jtice.2020.09.014 Nait Amar, 2020, Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents, J. Taiwan Inst. Chem. Eng., 117, 63, 10.1016/j.jtice.2020.11.029 Alom, 2019, A state-of-the-art survey on deep learning theory and architectures, Electron, 8, 10.3390/electronics8030292 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Kennedy, 1995, Particle swarm optimization Shi, 1998, Modified particle swarm optimizer Du, 2016 Ezugwu, 2020, A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems, Neural Comput. Appl., 32, 10.1007/s00521-019-04132-w Ng, 2022, Well production forecast in Volve field: application of rigorous machine learning techniques and metaheuristic algorithm, J. Petrol. Sci. Eng., 208, 10.1016/j.petrol.2021.109468 Jansen, 2014, The egg model - a geological ensemble for reservoir simulation, Geosci. Data J., 10.1002/gdj3.21 McKay, 1979, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics Hammersley Sobol, 1967, On the distribution of points in a cube and the approximate evaluation of integrals, USSR Comput. Math. Math. Phys., 10.1016/0041-5553(67)90144-9 Kingma, 2015, Adam: a method for stochastic optimization