Robust and Accurate Equation of State Framework for Modeling Phase Behavior of Reservoir Fluids under Extreme Pressure/Temperature Conditions

在深部或超深部地层中发现的油气藏越来越多。在这种深部或超深部储集层中，油气资源受到极端温度/压力条件(高达300摄氏度和300兆帕)的影响。了解油藏流体在这种极端条件下的行为，对于建立更准确的模拟模型，很好地捕捉油藏和井筒中的多相流具有至关重要的作用。传统的描述油藏流体相态的模型不能很好地适应极端的温度/压力条件。尽管过去已经开发了各种体积平移立方状态方程(CEOS)模型来解决这一不足，但仍然缺乏准确的CEOS模型和可靠的混合规则，可以在低温到极端温度/压力条件下很好地处理油藏流体(可能包含极性化合物和非极性化合物)。其次，CEOS建模中的一个挑战是根据测量的相态数据确定CEOS参数。目前，工程师必须经验地回归CEOS参数以匹配测量的相态数据。我们缺乏一种可靠的方法来根据在极端压力/温度条件下测量的相行为数据来自动确定CEOS参数。第三，极端压力/温度条件下的多相平衡比低压/温度条件下的要复杂得多。在较高的压力/温度条件下，经常会出现多相平衡直至四相平衡(如气-液-水-沥青质平衡)。为了准确地描述油藏和井筒中的多相流动，需要稳健而高效的算法来解决基于CEOS的多相平衡计算问题。我们现在仍然缺乏一个强大的模拟框架来模拟如此复杂的多相平衡。鉴于上述问题，本研究的总体目标是实现对极端压力/温度条件下油藏流体相态的自动、稳健和准确的描述。我们将首先提高CEOS本身对纯物质的基本预测能力，以及提高用于将CEOS扩展到流体混合物的相行为模拟的混合规则的预测能力。其次，我们将利用人工智能算法开发一种基于在极端压力/温度条件下测量的相态数据来自动调整CEOS参数的技术。在前面工作成果的基础上，我们将最终开发一套稳健的算法，用于模拟不同条件下油藏流体的多相平衡。研究成果将在深层、超深层油气资源高效开采中得到应用。

Many oil/gas reservoirs are being increasingly discovered in deep or ultradeep formations. In such deep or ultradeep reservoirs, hydrocarbon resources are subjected to extreme temperature/pressure conditions (up to 300oC and 300 MPa). Knowing how reservoir fluids behave under such extreme conditions plays a crucial role in building more accurate simulation models that can well capture the multiphase flow in both reservoirs and the wellbores. The conventional models for describing phase behavior of reservoir fluids are not well suited for extreme temperature/pressure conditions. Although various volume translation cubic equation of state (CEOS) models have been developed in the past to tackle this deficiency, there is still a lack of an accurate CEOS model and a reliable mixing rule that can perform well for reservoir fluids (which may contain both polar and non-polar compounds) under low to extreme temperature/pressure conditions. Secondly, one challenge in CEOS modeling is the determination of CEOS parameters based on measured phase behavior data. Currently, engineers have to empirically regress the CEOS parameters to match the measured phase behavior data. We are lacking a robust methodology for automatic determination of CEOS parameters based on the measured phase behavior data under extreme pressure/temperature conditions. Thirdly, the multiphase equilibria under extreme pressure/temperature conditions tend to be more complex than those under low pressure/temperature conditions. Multiphase equilibria up to four-phase equilibria (such as vapor-liquid-aqueous-asphaltenes equilibrium) may frequently appear under higher pressure/temperature conditions. In order to accurately describe the multiphase flow in petroleum reservoir and wellbore, robust and efficient algorithms are required to tackle the multiphase equilibrium calculations based on CEOS. We are now still lacking a robust simulation framework for simulating such complex multiphase equilibria. Viewing the above issues, the general objective of the proposed research is thus to achieve automatic, robust and accurate description of the phase behavior of reservoir fluids under extreme pressure/temperature conditions. We will first improve the fundamental predictive capability of CEOS itself for pure substances as well as improve the predictive capability of the mixing rules used to extend CEOS to the phase-behavior modeling of fluid mixtures. Second, we will leverage artificial intelligence algorithms to develop an automatic tuning technique for CEOS parameters based on phase behavior data measured under extreme pressure/temperature conditions. Based on results achieved in the previous tasks, we will lastly develop a suite of robust algorithms for simulating multiphase equilibria of reservoir fluids under various conditions. The research results will find applications in the efficient recovery of oil/gas resources from deep and ultradeep reservoirs.