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

用于模拟极压/温度条件下储层流体相行为的稳健且准确的状态方程框架

• 批准号: RGPIN-2020-04571
• 负责人: Li, Huazhou
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717202
• 关键词: Robust; Accurate; Equation; State; Framework

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.

在深层或超过的地层中，越来越多地发现了许多石油/天然气储层。在如此深的或超过的储层中，烃资源经历了极端的温度/压力条件（最高300oC和300 MPa）。知道储层流体在这种极端条件下的行为如何在建立更准确的模拟模型中起着至关重要的作用，该模型可以很好地捕获储层和井中的多相流。描述储层流体相位行为的常规模型不太适合极端温度/压力条件。尽管过去已经开发出各种体积的状态（CEO）模型来解决这种缺陷，但仍然缺乏准确的CEO模型和可靠的混合规则，在低于极端温度/压力条件下，对于储层流体（可能含有极性和非极性化合物），可以很好地表现储层液（可能包含极性和非极性化合物）。其次，CEO建模中的一个挑战是基于测量的相行为数据确定CEO参数。当前，工程师必须经验回归CEO参数以匹配所测量的相行为数据。我们缺乏一种强大的方法，用于根据极端压力/温度条件下的测量相行为数据自动确定CEO参数。第三，在极端压力/温度条件下的多相平衡往往比低压/温度条件下的平衡更为复杂。在较高的压力/温度条件下，多相平衡（例如蒸气 - 液体 - 水斑呈平衡）可能经常出现在较高的压力/温度条件下。为了准确描述石油储层和井眼中的多相流，需要强大，有效的算法来解决基于CEO的多相平衡计算。 现在，我们仍然缺少一个可靠的模拟框架来模拟这种复杂的多相平衡。因此，查看上述问题，拟议研究的一般目标是实现对储层流体在极端压力/温度条件下的相位行为的自动，健壮和准确的描述。我们将首先提高CEO本身对于纯物质的基本预测能力，并提高用于将CEO扩展到流体混合物的相位行为模型的混合规则的预测能力。其次，我们将利用人工智能算法根据在极端压力/温度条件下测量的相行为数据开发为CEO参数的自动调整技术。基于先前任务中的结果，我们最后将开发一套可靠的算法，用于在各种条件下模拟储层流体的多相平衡。该研究结果将在从深层和超模型储层中有效回收石油/天然气资源时找到应用。