Optimizing CO2- and Field-Gas-Injection EOR in Unconventional Reservoirs Using the Fast-Marching Method
- Atsushi Iino (Texas A&M University) | Tsubasa Onishi (Texas A&M University) | Akhil Datta-Gupta (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2020
- Document Type
- Journal Paper
- 261 - 281
- 2020.Society of Petroleum Engineers
- fast marching method, unconventional reservoirs, huff-n-puff, enhanced oil recovery, rapid optimization
- 16 in the last 30 days
- 225 since 2007
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Recently, there has been an increasing interest in enhanced oil recovery (EOR) from shale-oil reservoirs, including injection of carbon dioxide (CO2) and field gas. For the performance assessment and optimization of CO2- and field-gas-injection processes, compositional simulation is a powerful and versatile tool because of the capability to incorporate reservoir heterogeneity, complex fracture geometry, and multiphase and multicomponent effects in nanoporous rocks. However, flow simulation accounting for such complex physics can be computationally expensive. In particular, field-scale optimization studies requiring a large number of high-resolution compositional simulations can be challenging and sometimes computationally prohibitive. In this paper, we present a rapid and efficient approach for the optimization of CO2- and field-gas-injection EOR in unconventional reservoirs using a fast-marching-method (FMM) -based flow simulation.
The FMM-based simulation uses the concept of diffusive time of flight (DTOF). The DTOF is a representation of the travel time of pressure-front propagation and accounts for geological heterogeneity, well architecture, and complex fracture geometry. The DTOF can be efficiently obtained by solving the Eikonal equation using the FMM. The 3D flow equation is then transformed into an equivalent 1D equation using the DTOF as a spatial coordinate, leading to orders of magnitude faster computation for high-resolution and compositional models as compared to full 3D simulations. The speed of computation enables using robust population-based optimization techniques such as genetic- or evolutionary-based algorithms that typically require a large number of simulation runs to optimize the operational and process parameters.
We demonstrate the efficiency and robustness of our proposed approach using synthetic and field-scale examples. We first validate the FMM-based simulation approach using an example of CO2 huff ‘n’ puff for a synthetic heterogeneous dual-porosity model with a multistage hydraulically fractured well. Next, we present a field-scale optimization of operating strategies for gas-injection EOR in the Eagle Ford Formation. The rapid computation of the FMM-based approach enabled a comprehensive evaluation of the EOR project, including sensitivity studies, parameter-importance analysis, and optimal operating strategies using high-resolution geologic models.
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