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

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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.