聚合物驱模拟建模可行性研究:理解关键方面和设计优化

W. Hidayat, Nasser ALMolhem
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引用次数: 1

摘要

探讨了聚合物驱提高采收率的可行性研究方法。这项工作的重点是理解和量化聚合物驱的关键方面和设计参数优化案例。采用综合油藏模拟模型进行研究。第一阶段是识别和理解对聚合物驱响应影响最大的关键因素。分析中考虑了8个参数,如聚合物浓度、聚合物热降解、聚合物注入时间、聚合物-岩石性质(吸附、残余阻力因子等)。每个参数对采收率响应的影响通过其低、中、高值进行敏感化。对各参数的高、低采收率产出差进行排序,确定其显著性水平。灵敏度分析得到的前三个参数是聚合物注入时间、热降解和聚合物浓度。研究了聚合物注入性和热降解效应的敏感性。第二阶段是确定聚合物驱的最佳设计参数。考虑灵敏度分析结果中最显著的参数进行进一步优化。设计优化选择的三个参数包括聚合物注入时间、聚合物浓度和井距。将单纯形算法的优化工作流程与油藏模拟器相结合,通过改变优化参数的取值来生成优化案例。当达到目标函数的最大值即净收入时,优化迭代停止。针对岩石渗透率为500 md和1000 md的油藏进行了优化循环。对于低岩石渗透率油藏,除了避免潜在的注入问题外,井距应短,较低的聚合物浓度足以提供良好的响应。对于渗透率大于1000 md的储层,必须考虑最小注入能力问题。为了避免过于乐观的预测,在模拟模型中引入聚合物热降解是非常重要的。灵敏度分析结果对控制聚合物注入响应的参数的重要影响和潜在挑战提供了很好的理解。本研究采用的优化方法有助于在短时间内研究多种优化方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polymer Flooding Simulation Modeling Feasibility Study: Understanding Key Aspects and Design Optimization
The paper discusses the feasibility study approach of polymer flooding enhanced oil recovery. This work is focused on understanding and quantifying key aspects of polymer flooding and design parameter optimization case. A synthetic reservoir simulation model was employed for the study. The first stage is to identify and understand key factors that have most significant impact to polymer flooding response. There are eight parameters that are considered in the analysis, such as polymer concentration, polymer thermal degradation, polymer injection duration, and polymer-rock properties (adsorption, residual resistance factor, etc.). The impact of each parameter to oil recovery response was sensitized with its low, mid, and high values. The difference of high to low oil recovery output for all parameters was ranked to determine their significance levels. The top three parameters obtained from the sensitivity analysis are polymer injection duration, thermal degradation, and polymer concentration. Sensitivity cases of polymer injectivity and thermal degradation effects were covered in this work. The second stage is to determine optimum design parameters of polymer flooding. The most significant parameters from the sensitivity analysis results were considered for further optimization. Three parameters that were selected for design optimization include polymer injection duration, polymer concentration, and well spacing. An optimization workflow with simplex algorithm is linked with a reservoir simulator to generate optimization cases by varying values of optimized parameters. The optimization iteration stops when the maximum value of the objective function, which is the net revenue, is reached. The optimization cycle was done for rock permeability of 500 md and 1000 md. For a low rock permeability reservoir, the well spacing should be short and a lower polymer concentration is sufficient to provide a good response, in addition to avoiding potential injectivity problem. There should be minimum injectivity problem for reservoir with permeability above 1000 md. It is very important to apply polymer thermal degradation in the simulation model to avoid an optimistic performance prediction. The sensitivity analysis results provide a good understanding on the significance impact of parameters controlling polymer injection response and potential challenges. The optimization approach used in the study aids in investigating many optimization scenario within a short period of time.
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