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

目前世界石油产量的相当一部分来自通过应用提高石油采收率（EOR）技术的成熟油田，因为新发现的石油在过去几十年中一直在稳步下降。在成功实施EOR工艺之前，必须通过将静态地质模型校准到动态生产数据来准确确定成熟油田中剩余油的量和位置，然后可以优化生产-注入策略。主要由于模拟器的输入与生产数据、所使用的模拟方法、所收集的数据和模型规模之间的复杂和非线性关系，储层地质模型的准确性通常受到损害。地质和经济不确定性的存在不仅使得精确和动态地更新地质模型变得更加困难，而且还将优化的难度提升到风险评估问题的水平。因此，在不确定性条件下，根据静态和动态数据生成多个油藏模型，进而优化相应的油藏动态，具有重要的基础和实际意义。

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.