Production Allocation Optimization of Gas Reservoirs with Edge and Bottom Aquifer Based on a Parallel-Structured Genetic Algorithm
As gas reservoir pressure decreases, edge and bottom water irregularly flow into the reservoir through storage and permeability spaces. Water influx poses a significant challenge for the development of gas reservoirs, impacting development efficiency and the ultimate recovery rate. Therefore, explor...
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| Published in: | ACS omega Vol. 9; no. 25; pp. 27329 - 27337 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
American Chemical Society
25.06.2024
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| ISSN: | 2470-1343, 2470-1343 |
| Online Access: | Get full text |
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| Summary: | As gas reservoir pressure decreases, edge and bottom water irregularly flow into the reservoir through storage and permeability spaces. Water influx poses a significant challenge for the development of gas reservoirs, impacting development efficiency and the ultimate recovery rate. Therefore, exploring rational optimization methods for gas well allocation is essential. This study utilizes the vertical well productivity equation considering two-phase flow and employs the net present value (NPV) to evaluate the economic benefits of gas well production. A parallel-structured genetic algorithm (GA) is developed to account for dynamic reservoir inflow, wellbore conditions, and surface facilities engineering. The new model is applied to investigate the optimal allocation of the B-21 well in the Amu Darya right bank gas reservoirs in Turkmenistan. Results indicate a match of over 90% between the cumulative gas production and water/gas ratio calculated by the proposed method and those calculated by a numerical simulation model. Compared with the traditional genetic algorithm, the new approach reduces the number of iterations to approximately 2100 (a 72.4% decrease) and significantly improves the convergence rate. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2470-1343 2470-1343 |
| DOI: | 10.1021/acsomega.4c01877 |