An advanced inverse modeling framework for efficient and flexible adjoint-based history matching of geothermal fields
文献类型：期刊论文
| 作者 | Tian, Xiaoming1,2; Volkov, Oleg2,3; Voskov, Denis2,3 |
| 刊名 | GEOTHERMICS
 |
| 出版日期 | 2024 |
| 卷号 | 116页码:17 |
| 关键词 | History matching Principal Component Analysis DARTS Adjoint method Geothermal energy |
| ISSN号 | 0375-6505 |
| DOI | 10.1016/j.geothermics.2023.102849 |
| 通讯作者 | Voskov, Denis(D.V.Voskov@tudelft.nl) |
| 英文摘要 | In this study, we present an efficient and flexible adjoint-based framework for history matching and forecasting geothermal energy extraction at a large scale. In this framework, we applied the Principal Component Analysis to reduce the parameter space for representing the complex geological model. The adjoint method is implemented for gradient calculation to speed up the history-matching iteration process. Operator-based linearization (OBL) used in this framework makes the calculation of the physical state and its derivatives very efficient and facilitates the matrix assembly in the adjoint method. This study primarily focuses on history matching based on combined observation of well production and in-situ electromagnetic measurements to predict the temperature front. However, different types of misfit terms can be added to the objective function based on practical considerations. For example, our history-matching case studies include model misfit terms applied for regularization purposes. The measurement data is extracted from the true model, and realistic measurement errors are considered. Also, in this work, we propose an optimal weighting strategy for the terms of the objective function to balance their sensitivity with respect to the model control variables. The high efficiency of the framework is demonstrated for the geothermal doublet model implemented at the heterogeneous reservoir with multiple realizations. The framework allows for generating posterior Randomized Maximum Likelihood (RML) estimates of the entire ensemble of the realizations with a reasonable computational cost. Results show that the framework can achieve reliable history-matching results based on the doublets production data and the reservoir electromagnetic measurement. |
| WOS关键词 | PRINCIPAL COMPONENT ANALYSIS ; OPERATOR-BASED LINEARIZATION ; OPTIMAL INJECTION POLICIES ; ENHANCED OIL-RECOVERY ; DIFFERENTIABLE PARAMETERIZATION ; FLOW ; ENSEMBLE ; CONDUCTIVITIES ; TEMPERATURE ; MEDIA |
| 资助项目 | China Scholarship Council (CSC) ; Stanford Smart Fields Consortium |
| WOS研究方向 | Energy & Fuels ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001101828500001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | China Scholarship Council (CSC) ; Stanford Smart Fields Consortium |
| 源URL | [http://ir.giec.ac.cn/handle/344007/40314]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Voskov, Denis |
| 作者单位 | 1.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou, Peoples R China 2.Delft Univ Technol, Dept Geosci & Engn, Delft, Netherlands 3.Stanford Univ, Dept Energy Resources Engn, Stanford, CA USA |
| 推荐引用方式 GB/T 7714 | Tian, Xiaoming,Volkov, Oleg,Voskov, Denis. An advanced inverse modeling framework for efficient and flexible adjoint-based history matching of geothermal fields[J]. GEOTHERMICS,2024,116:17. |
| APA | Tian, Xiaoming,Volkov, Oleg,&Voskov, Denis.(2024).An advanced inverse modeling framework for efficient and flexible adjoint-based history matching of geothermal fields.GEOTHERMICS,116,17. |
| MLA | Tian, Xiaoming,et al."An advanced inverse modeling framework for efficient and flexible adjoint-based history matching of geothermal fields".GEOTHERMICS 116(2024):17. |
入库方式： OAI收割
来源：广州能源研究所
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