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

在深层或超深层地层中发现了越来越多的油气储层。在这种深层或超深层储层中，油气资源受到极端温度/压力条件（高达300℃和300 MPa）的影响。了解储层流体在这种极端条件下的行为对于建立更精确的模拟模型至关重要，这些模型可以很好地捕捉储层和井筒中的多相流。描述储层流体相行为的传统模型不适用于极端温度/压力条件。尽管过去已经开发了各种体积转换立方状态方程（ceo）模型来解决这一缺陷，但仍然缺乏准确的ceo模型和可靠的混合规则，可以在低到极端温度/压力条件下很好地处理储层流体（可能包含极性和非极性化合物）。其次，基于实测相位行为数据确定ceo参数是ceo建模的一大挑战。目前，工程师们只能通过经验回归ceo参数来匹配测量的相位行为数据。在极端压力/温度条件下，基于测量的相行为数据，我们缺乏一种可靠的方法来自动确定ceo参数。第三，极端压力/温度条件下的多相平衡比低压/温度条件下的多相平衡更为复杂。在较高的压力/温度条件下，可能经常出现高达四相平衡的多相平衡（如蒸汽-液体-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、高效的算法来处理基于ceo的多相平衡计算。我们现在仍然缺乏一个强大的模拟框架来模拟这种复杂的多相平衡。综上所述，本研究的总体目标是实现对极端压力/温度条件下储层流体相行为的自动、稳健和准确描述。我们将首先提高ceo本身对纯物质的基本预测能力，并提高用于将ceo扩展到流体混合物相行为建模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极端压力/温度条件下测量的相行为数据的ceo参数自动调谐技术。基于之前的工作成果，我们将开发一套鲁棒的算法来模拟各种条件下储层流体的多相平衡。研究成果将应用于深层和超深层油气资源的高效开采。

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.