Abstract
Evaluating reservoir performance could be challenging, especially when available data are only limited to pressures and rates from oil field production and/or injection wells. Numerical simulation is a typical approach to estimate reservoir properties using the history match process by reconciling field observations and model predictions. Performing numerical simulations can be computationally expensive by considering a large number of grids required to capture the spatial variation in geological properties, detailed structural complexity of the reservoir, and numerical time steps to cover different periods of oil recovery. In this work, a simplified physics-based model is used to estimate specific reservoir parameters during CO2 storage into a depleted oil reservoir. The governing equation is based on the integrated capacitance resistance model algorithm. A multivariate linear regression method is used for estimating reservoir parameters (injectivity index and compressibility). Synthetic scenarios were generated using a multiphase flow numerical simulator. Then, the results of the simplified physics-based model in terms of the estimated fluid compressibility were compared against the simulation results. CO2 injection data including bottom hole pressure and injection rate were also gathered from a depleted oil reef in Michigan Basin. A field application of the simplified physics-based model was presented to estimate above-mentioned parameters for the case of CO2 storage in a depleted oil reservoir in Michigan Basin. The results of this work show that this simple lumped parameter model can be used for a quick estimation of the specific reservoir parameters and its changes over the CO2 injection period.
Highlights
Storage of carbon dioxide into geological formations is a mitigation strategy to reduce mankind’s greenhouse gas emissions into the atmosphere
Among them, depleted oil reservoirs are considered a promising candidate for injection and long-term storage of carbon dioxide due to its capability to achieve two objectives simultaneously: (1) enhanced oil recovery (EOR) and (2) CO2 storage during and after EOR (Alvarado and Manrique 2010)
We describe the governing equations based on which a simplified physics-based model will be developed to establish a relationship between the well data (i.e., BHP and injected volume) and limited reservoir parameters at first
Summary
Storage of carbon dioxide into geological formations is a mitigation strategy to reduce mankind’s greenhouse gas emissions into the atmosphere. The alternative method, presented in this study, is based on a simplified physics-based lumped parameter modeling approach to study fluid flow behavior of a depleted oil reservoir during C O2 storage. We describe the governing equations based on which a simplified physics-based model will be developed to establish a relationship between the well data (i.e., BHP and injected volume) and limited reservoir parameters (i.e., initial reservoir pressure) at first. The capacitance resistance model (CRM) is an input–output model that can quantify the relationship between injectors, producers, and reservoir properties (Kim et al 2012; Lake et al 2007; Sayarpour 2008; Sayarpour et al 2009a, 2009b; Weber et al 2009) As documented in these references, CRM has been applied to predict reservoir parameters during primary and secondary recovery using oil field data including the fields with many wells. If the total system compressibility is known, the pore volume can be calculated for different time intervals resulting in a series of estimated pore volumes over time
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