成熟外围注水性能预测与优化的电容电阻聚类模型

B. Aslam, Hasto Nugroho, Fahriza Mahendra, Rani Kurnia, T. Marhaendrajana, S. Siregar
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引用次数: 0

摘要

优化注水作业中的注水速率分布是油藏管理的一个重要方面,因为注水能力可能会受到地理位置和设施限制的限制。传统上,基于数值网格的油藏模拟用于水驱性能评价和预测。然而,由于成熟油田中有大量的井数据,储层模拟方法可能耗时且昂贵。电容电阻模型(CRM)作为一种基于数据驱动的物理模型,近年来在注水开发项目中得到了广泛的应用。尽管CRM的计算量比油藏数值模拟小,但包含数百口井的大型成熟油田仍然对模型校准和优化提出了挑战。在这项研究中,我们提出了一种替代解决方案,以改进CRM在大规模注水中的应用,该解决方案特别适合外围注入配置。我们的方法试图通过使用聚类算法自动对具有不规则外围模式的生产井进行分组来减少CRM问题的规模。井组的选择考虑了井位和高通量井(关键井)。我们通过在南苏门答腊一个成熟的外围注水油田的应用验证了我们的解决方案。基于案例研究,由于参数的减少,我们的计算速度提高了18.2倍,具有优异的历史匹配精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capacitance Resistance Clustered Model for Mature Peripheral Waterflood Performance Prediction & Optimization
Optimizing water injection rate distribution in waterflooding operations is a vital reservoir management aspect since water injection capacities may be constrained due to geographic location and facility limitations. Traditionally, numerical grid-based reservoir simulation is used for waterflood performance evaluation and prediction. However, the reservoir simulation approach can be time-consuming and expensive with the vast amount of wells data in mature fields. Capacitance Resistance Model (CRM) has been widely used recently as a data-driven physics-based model for rapid evaluation in waterflood projects. Even though CRM has a smaller computation load than numerical reservoir simulation, large mature fields containing hundreds of wells still pose a challenge for model calibration and optimization. In this study, we propose an alternative solution to improve CRM application in large-scale waterfloods that is particularly suitable for peripheral injection configuration. Our approach attempts to reduce CRM problem size by employing a clustering algorithm to automatically group producer wells with an irregular peripheral pattern. The selection of well groups considers well position and high throughput well (key well). We validate our solution through an application in a mature peripheral waterflood field case in South Sumatra. Based on the case study, we obtained up to 18.2 times increase in computation speed due to parameter reduction, with excellent history match accuracy.
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