Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application Article Swipe
Lu Qiao
,
Huijun Wang
,
Shuangfang Lu
,
Yang Liu
,
Taohua He
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.1c05158
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.1c05158
Recently, production optimization has gained increasing interest in the petroleum industry. The most computationally intensive and critical part of the production optimization process is the evaluation of the production function performed by the numerical reservoir simulator. Employing proxy models as a substitute for the reservoir simulator is proposed for alleviating this high computational cost. In this study, a new approach to construct adaptive proxy models for production optimization problems is proposed. An adaptive difference evolution algorithm (SaDE) optimized least-squares support vector machine (LSSVM) is used as an approximation function, while training is performed using a self-adaptive response surface experimental design (SaRSE). SaDE selects the optimal hyperparameters of LSSVM during the training process to improve the prediction accuracy of the proxy model. Cross-validation methods are used in the recursive training and network evaluation phases. The developed method is used to optimize the production of block gas reservoir models. Computational results confirm that the developed adaptive proxy model outperforms traditional regression methods. It is further verified that when the experimental data are updated, the alternative model still has high prediction accuracy when performing the objective function evaluation. The results show that the proposed proxy modeling approach enhances the entire optimization process by providing a fast approximation of the actual reservoir simulation model with better accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acsomega.1c05158
- OA Status
- gold
- Cited By
- 13
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220820332
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220820332Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acsomega.1c05158Digital Object Identifier
- Title
-
Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical ApplicationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-28Full publication date if available
- Authors
-
Lu Qiao, Huijun Wang, Shuangfang Lu, Yang Liu, Taohua HeList of authors in order
- Landing page
-
https://doi.org/10.1021/acsomega.1c05158Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1021/acsomega.1c05158Direct OA link when available
- Concepts
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Computer science, Reservoir simulation, Mathematical optimization, Kriging, Metamodeling, Proxy (statistics), Hyperparameter, Support vector machine, Algorithm, Machine learning, Petroleum engineering, Mathematics, Engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 3, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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