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. MultiFidelity Models (MFM)
4.2. ReducedOrder 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 nearwellbore region    Streamlines    MFM 
Kam et al. [128]  Threephase 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  Gradientbased  ROM 
Xiao et al. [152]  History matching  Smolyak sparse [196]  TPWL/POD  Gradientbased  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 
AlMudhafar [100]  Production optimization in cyclic CO_{2} 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  Nondominated sorting GAII [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 
AlMudhafar and Rao [190]  Recovery evaluation in CO_{2}GAGD operation  LHS  PR, MARS, GBM    TPM 
Jaber et al. [191]  Recovery evaluation in miscible CO_{2}WAG flooding  BBD  PR    TPM 
Agada et al. [192]  Recovery and net gas utilization factor optimization of a CO_{2}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 wellbywell oil and water flow rate prediction in waterflooding  Random selection  RNN  PSO  TPM 
Kim and Durlofsky [208]  Predicting NPV with timevarying BHP  Uniform distribution  RNN  PSO  TPM 
Kim et al. [209]  Multiwell placement optimization  Uniform distribution  CNN  PSO  TPM 
Haghshenas et al. [20]  Evaluating the effect of injection rates on oil saturation using the gridbased SPM  LHS  ANN    SPM 
Alenezi and Mohaghegh [194]  Evaluating the effect of injection rates on oil saturation and pressure using the gridbased SPM  Random selection  ANN    SPM 
Amini and Mohaghegh [21]  Gas injection monitoring in porous media using the gridbased SPM    ANN  Gradient descent  SPM 
Gholami et al. [18]  WAG monitoring and production optimization using the gridbased and wellbased SPMs  LHS  ANN    SPM 
He et al. [19]  History matching using wellbased SPM  LHS  ANN  DE  SPM 
Shahkarami et al. [22]  History matching using wellbased 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  capacitanceresistance modeling 
DE  differential evolution 
DEIM  discrete empirical interpolation method 
GA  genetic algorithm 
GAGD  gasassisted 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  multifidelity 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  reducedorder model 
RSM  response surface model 
SA  sensitivity analysis 
SAGD  steamassisted gravity drainage 
SBO  surrogatemodelbased optimization 
SPM  smart proxy model 
SVM  support vector machine 
SVR  support vector regression 
TPM  traditional proxy model 
TPWL  trajectorypiecewise linear 
WAG  water alternating gas 
XGboost  extreme gradient boosting 
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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