Creating High-Resolution Simulations using Geologic Models
The State of Texas Advanced Resources Recovery (STARR) improved oil recovery group is dedicated to helping Texas operators manage their mature fields. Such fields are typically quite old and are approaching their limits of economic production. Our group provides guidance on improving existing recovery methods (e.g., water and CO2 flood) or applying new recovery methods.
My research begins with building 3D high-resolution geologic models in the Petrel software system. These models are based on utilizing interpreted wireline log data calibrated against core data, as well as detailed geologic interpretation provided by my fellow STARR team members. I then use geostatistical techniques to make models of the geology and flow properties of the reservoir rocks between wells. Most published simulations use coarse grid elements and upscale transport properties. By using state-of-the-art multiprocessing simulators and the powerful computers available at the Bureau and UT Austin's Texas Advanced Computing Center, I am able to make reservoir simulations with factors of 10 to 20 higher resolution than those normally used. I am also now employing machine-learning algorithms to assist reservoir flow simulation, history matching, and uncertainty quantification.
My research recently has focused on optimizing water alternating gas (WAG) injection into residual oil zones (ROZs), modeling the origins of ROZs, and optimizing oil production using foam-assisted WAG injection. Current projects include optimizing production from Perdure Petroleum’s Wellman Unit and Fasken Oil and Ranch’s Hanford San Andres Unit.
Ren, B., Duncan, I. J., Male, F., Baqués, V., Lake, L. W., 2020, Economic Assessment of Strategies for CO2-EOR and Storage in Residual Oil Zones: A Case Study from the Seminole San Andres Unit. SPE Improved Oil Recovery Conference, Tulsa, Oklahoma, USA, 29 August–2 September, 2020, https://doi.org/10.2118/200363-MS.
Ren, B., Duncan, I. J., 2019, Reservoir Simulation of Carbon Storage Associated with CO2 EOR in Residual Oil Zones, San Andres Formation of West Texas, Permian Basin, USA. Energy 167: 391–401, https://doi.org/10.1016/j.energy.2018.11.007
Zhong, Z., Sun, A. Y., Ren, B., Wang, Y. A, 2020 (accepted), Deep-Learning-Based Approach for Reservoir Production Forecast Under Uncertainty. SPEJ.