Integrated modeling and parallelized optimization of enhanced oil recovery processes in hydrocarbon reservoirs under uncertainty

不确定性下油气藏提高采收率过程集成建模与并行优化

基本信息

批准号：
341388-2011
负责人：
Yang, Daoyong
金额：
$ 1.53万
依托单位：
University of Regina
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2015
资助国家：
加拿大
起止时间：
2015-01-01 至 2016-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572241
关键词：
Integrated modeling parallelized optimization enhanced

项目摘要

A considerable portion of current world oil production comes from mature oilfields by applying enhanced oil recovery (EOR) techniques since new discoveries have been declining steadily over the last few decades. Prior to successfully implementing an EOR process, it is essential that the amount and location of the residual oil in a mature oilfield be accurately determined by calibrating a static geologic model to dynamic production data and then the production-injection strategy can be optimized. Due mainly to the complex and nonlinear relationship between inputs of a simulator and production data, simulation methods used, data collected, and model scale, accuracy of a reservoir geological model is normally compromised. The presence of geological and economic uncertainty not only makes it more difficult to accurately and dynamically update the geological model, but also elevates the difficulty of optimization to the level of a risk assessment problem. Therefore, it is of fundamental and practical importance to generate multiple reservoir models conditional to static and dynamic data and subsequently optimize the corresponding reservoir performance under uncertainty. The major objective of this proposed research is to develop a pragmatic assisted history matching technique for dynamically and accurately updating the reservoir geological model as well as a robust and parallelized optimizer for efficiently solving large-scale and closed-loop nonlinear systems in the presence of economic and geological uncertainty. The proposed research project will allow us to not only fundamentally understand and visualize the reservoir dynamics and description, but also implicitly and inversely estimate reservoir petrophysical properties so as to improve accuracy of reservoir performance prediction in the presence of economic and geological uncertainty. The newly proposed techniques will not only improve prediction accuracy of reservoir geological models and optimize the ongoing EOR projects, but also facilitate designing the future EOR applications with maximum ultimate oil recovery in a cost-effective and sustainable manner.

由于过去几十年来新发现量一直在稳步下降，当前世界石油产量的很大一部分来自应用提高采收率（EOR）技术的成熟油田。在成功实施EOR过程之前，必须通过根据动态生产数据校准静态地质模型来准确确定成熟油田剩余油的数量和位置，然后优化生产注入策略。主要由于模拟器的输入和生产数据、所使用的模拟方法、收集的数据和模型规模之间的复杂和非线性关系，油藏地质模型的准确性通常会受到影响。地质和经济不确定性的存在不仅使地质模型的准确动态更新变得更加困难，而且将优化的难度提升到风险评估问题的水平。因此，生成基于静态和动态数据的多个油藏模型并随后在不确定性下优化相应的油藏性能具有重要的基础和实际意义。这项研究的主要目标是开发一种实用的辅助历史匹配技术，用于动态、准确地更新油藏地质模型，以及一种鲁棒的并行优化器，用于在经济存在的情况下有效求解大规模闭环非线性系统。和地质不确定性。所提出的研究项目将使我们不仅能够从根本上理解和可视化储层动力学和描述，而且能够隐式和反演地估计储层岩石物理性质，从而在存在经济和地质不确定性的情况下提高储层动态预测的准确性。新提出的技术不仅将提高油藏地质模型的预测精度并优化正在进行的EOR项目，而且有助于以具有成本效益和可持续的方式设计未来的EOR应用，以实现最大的最终石油采收率。