A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering
Abstract
:1. Introduction
2. Proxy Modeling Classification
3. Methodology
3.1. Sensitivity Analysis (SA)
3.2. Sampling
3.3. Popular Models for PM Construction
3.3.1. Polynomial Regression (PR)
3.3.2. Kriging (KG)
3.3.3. Multivariate Adaptive Regression Splines (MARS)
3.3.4. Artificial Neural Networks (ANN)
3.3.5. Radial Basis Function (RBF)
3.3.6. Support Vector Regression (SVR)
3.3.7. Genetic Programming (GP)
3.3.8. Random Forest (RF)
3.3.9. Extreme Gradient Boosting (XGboost)
3.3.10. Polynomial Chaos Expansion (PCE)
3.4. Optimization
4. Application of Proxy Models in the Oil and Gas Industry
4.1. Multi-Fidelity Models (MFM)
4.2. Reduced-Order Models (ROM)
4.3. Traditional Proxy Models (TPM)
4.4. Smart Proxy Models (SPM)
Ref. | Subject | Sampling Technique | Underlying Model | Optimizer | Class |
Kovscek and Wang [125] | Uncertainty quantification in a carbon dioxide storage case | - | Streamlines | - | MFM |
Tanaka et al. [123] | Production optimization in waterflooding | - | Streamlines | GA | MFM |
Wang and Kovscek [126] | History matching in a heterogeneous reservoir | - | Streamlines | - | MFM |
Tang et al. [195] | Investigating the effects of the permeability heterogeneity and well completion in near-wellbore region | - | Streamlines | - | MFM |
Kam et al. [128] | Three-phase history matching | - | Streamlines | GA | MFM |
Taware et al. [130] | Well placement optimization in a mature carbonate field | - | Streamlines | - | MFM |
Allam et al. [134] | History matching | - | Upscaling | - | MFM |
Yang et al. [136] | Multiphase uncertainty quantification and history matching | - | Upscaling | - | MFM |
Holanda et al. [141] | Reservoir characterization and history matching | - | CRM | - | MFM |
Artun [142] | Characterizing interwell reservoir connectivity | - | CRM | - | MFM |
Cardoso and Durlofsky [150] | Production optimization in waterflooding | - | TPWL/POD | Gradient-based | ROM |
Xiao et al. [152] | History matching | Smolyak sparse [196] | TPWL/POD | Gradient-based | ROM |
Rousset et al. [154] | Production prediction of SAGD operation | - | TPWL/POD | - | ROM |
He and Durlofsky [155] | Compositional simulation of the reservoir | - | TPWL/POD | - | ROM |
Gildin et al. [156] | Simulation of flow in heterogeneous porous media | - | DEIM/POD | - | ROM |
Li et al. [158] | Compressible gas flow in porous media | - | DEIM/POD | - | ROM |
Alghareeb and Williams [160] | Production optimization in waterflooding | - | DEIM/POD | - | ROM |
Al-Mudhafar [100] | Production optimization in cyclic CO2 flooding | - | PR, MARS, RF | - | TPM |
Golzari et al. [167] | Production optimization in three different cases to increase recovery and net present value (NPV) | Adaptive LHS | ANN | GA | TPM |
Amiri Kolajoobi et al. [168] | Uncertainty quantification and determination of cumulative oil production | LHS | ANN | - | TPM |
Peng and Gupta [169] | Uncertainty quantification in a fluvial reservoir | Factorial | PR | - | TPM |
Zubarev [170] | History matching and production optimization | LHS | PR, KG, ANN | GA | TPM |
Guo et al. [171] | History matching in a channelized reservoir | Random selection | SVR | Distributed Gauss–Newton [197] | TPM |
Avansi [172] | Field development planning | BBD | PR | - | TPM |
Ligero et al. [173] | Risk assessment in economic and technical parameters on an offshore field | Factorial | PR | - | TPM |
Risso et al. [174] | Assessment of risk curves for uncertainties in the reservoir | BBD, CCD | PR | - | TPM |
Ghassemzadeh and Charkhi [175] | Gas lift optimization to maximize recovery and NPV | - | ANN | GA | TPM |
Ebrahimi and E. Khamehchi [109] | Gas lift optimization in NGL process | LHS | SVR | PSO, GA | TPM |
Zangl et al. [176] | Gas storage management and optimization for pressure | Factorial | ANN | GA | TPM |
Artun et al. [177] | Screening and optimization of cyclic pressure pulsing in naturally fractured reservoirs | - | ANN | GA | TPM |
Gu et al. [180] | Waterflooding optimization in terms of watercut | - | XGboost | DE | TPM |
Chen et al. [181] | Waterflooding optimization in terms of recovery and NPV | LHS | KG | DE | TPM |
Ogbeiwi et al. [182] | Optimization of water injection rate and oil production rate in waterflooding | BBD | PR | GA | TPM |
Bruyelle and Guérillot [183] | Waterflooding optimization in terms of well parameters | BBD | ANN | Covariance matrix adaptation evolution strategy [198] | TPM |
Bruyelle and Guérillot [184] | Well placement optimization to maximize recovery and NPV | BBD | ANN | Covariance matrix adaptation evolution strategy | TPM |
Hassani et al. [185] | Optimization of the horizontal well placement | Optimal, LHS | PR, RBF | GA | TPM |
Nwachukwu et al. [186] | Injector well placement optimization to maximize recovery and NPV | Random selection | XGboost | - | TPM |
Aydin et al. [187] | Monitoring of a geothermal reservoir temperature and pressure from wellhead data | - | ANN | - | TPM |
Wang et al. [188] | Well control optimization to maximize recovery and NPV | LHS | SVR | Non-dominated sorting GA-II [199] | TPM |
Simonov et al. [200] | Production optimization in a miscible flooding case | LHS | RF | MC | TPM |
Redouane et al. [201] | Well placement optimization to maximize recovery | LHS, Sobol, Halton | ANFIS [202] | GA | TPM |
Fedutenko et al. [189] | Production prediction of SAGD operation | LHS | PR, KG, RBF | - | TPM |
Al-Mudhafar and Rao [190] | Recovery evaluation in CO2-GAGD operation | LHS | PR, MARS, GBM | - | TPM |
Jaber et al. [191] | Recovery evaluation in miscible CO2-WAG flooding | BBD | PR | - | TPM |
Agada et al. [192] | Recovery and net gas utilization factor optimization of a CO2-WAG operation in a fractured reservoir | BBD | PCE | GA | TPM |
Elsheikh et al. [203] | Watercut determination in waterflooding cases | Nested sampling, MCMC | PCE | - | TPM |
Yu et al. [204] | History matching and production forecasting | Hammersley [205] | GP | - | TPM |
Kalla and White [206] | Optimization of a gas well with water conning | OAS | PR | - | TPM |
Ibiam et al. [193] | Sensitivity analysis and polymer flooding optimization | LHS | PR | PSO | TPM |
Kim and Durlofsky [207] | History matching and well-by-well oil and water flow rate prediction in waterflooding | Random selection | RNN | PSO | TPM |
Kim and Durlofsky [208] | Predicting NPV with time-varying BHP | Uniform distribution | RNN | PSO | TPM |
Kim et al. [209] | Multi-well placement optimization | Uniform distribution | CNN | PSO | TPM |
Haghshenas et al. [20] | Evaluating the effect of injection rates on oil saturation using the grid-based SPM | LHS | ANN | - | SPM |
Alenezi and Mohaghegh [194] | Evaluating the effect of injection rates on oil saturation and pressure using the grid-based SPM | Random selection | ANN | - | SPM |
Amini and Mohaghegh [21] | Gas injection monitoring in porous media using the grid-based SPM | - | ANN | Gradient descent | SPM |
Gholami et al. [18] | WAG monitoring and production optimization using the grid-based and well-based SPMs | LHS | ANN | - | SPM |
He et al. [19] | History matching using well-based SPM | LHS | ANN | DE | SPM |
Shahkarami et al. [22] | History matching using well-based SPM | LHS | ANN | - | SPM |
Ng et al. [23] | Production optimization in a fractured reservoir | - | ANN | PSO | SPM |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACO | ant colony optimization |
ANFIS | adaptive neuro fuzzy inference system |
ANN | artificial neural networks |
BBD | box–Behnken design |
BHP | bottom hole pressure |
CCD | central composite design |
CNN | convolutional neural networks |
CRM | capacitance-resistance modeling |
DE | differential evolution |
DEIM | discrete empirical interpolation method |
GA | genetic algorithm |
GAGD | gas-assisted gravity drainage |
GBM | gradient boosting machine |
GP | genetic programming |
GSA | global sensitivity analysis |
KG | kriging |
LHS | Latin hypercube sampling |
LSA | local sensitivity analysis |
MARS | multivariate adaptive regression splines |
MC | Monte Carlo |
MCMC | Markov chain Monte Carlo |
MFM | multi-fidelity model |
NPV | net present value |
OAS | orthogonal array sampling |
PBD | Plackett–Burman design |
PCE | polynomial chaos expansion |
PM | proxy model |
POD | proper orthogonal decompositions |
PR | polynomial regression |
PSO | particle swarm optimization |
RBF | radial basis functions |
ReLU | rectified linear unit |
RF | random forest |
RNN | recurrent neural networks |
ROM | reduced-order model |
RSM | response surface model |
SA | sensitivity analysis |
SAGD | steam-assisted gravity drainage |
SBO | surrogate-model-based optimization |
SPM | smart proxy model |
SVM | support vector machine |
SVR | support vector regression |
TPM | traditional proxy model |
TPWL | trajectory-piecewise linear |
WAG | water alternating gas |
XGboost | extreme gradient boosting |
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Modeling Technique | Advantages | Disadvantages |
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PR |
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KG |
| |
MARS |
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ANN |
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RBF |
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SVR |
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GP |
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RF |
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XGBoost |
|
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PCE |
|
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Bahrami, P.; Sahari Moghaddam, F.; James, L.A. A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering. Energies 2022, 15, 5247. https://doi.org/10.3390/en15145247
Bahrami P, Sahari Moghaddam F, James LA. A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering. Energies. 2022; 15(14):5247. https://doi.org/10.3390/en15145247
Chicago/Turabian StyleBahrami, Peyman, Farzan Sahari Moghaddam, and Lesley A. James. 2022. "A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering" Energies 15, no. 14: 5247. https://doi.org/10.3390/en15145247