Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations. In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach. The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling. Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.
To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations. In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach. The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling. Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.
The Postle field is located in Texas County in the Oklahoma Panhandle and has been waterflooded since 1960 and CO2 flooded since 1996. In 2022, a streamline-based flood management workflow was initiated to optimize flood performance and improve cashflow. Initial recommendations included cutting existing CO2 purchases by 50%, generating a savings of $7.5MM. In 2023, due to a deterioration in profitability, the field went through several iterations of cost-cutting measures culminating in the elimination of purchased CO2 and a reduction in workover job. The loss of purchased CO2 effectively reduced the volume of injection volumes by about 20%, reducing the VRR from 1.5 to 1.2. This reduced the processing rate in the reservoir from 7% to 5% per year. This work outlines a streamline-based flood management approach, and results achieved from the implementation of this new workflow. The new flood management approach was adopted across the entire Postle field that consists of 4 units. This encompasses nearly 250 producers and 100 injectors. A streamline surveillance model was built for the entire field across the 3 main producing zones. The results from the surveillance model were used to identify interwell connections between producers and injectors. This allowed the injection impact or value of each injector to be properly quantified. The injection efficiencies were used to allocate the limited volume of CO2, where higher efficiency injectors were allocated more CO2. WAG parameters were also calibrated based on rate targets from the surveillance optimization module (floodOPT). The combination of rate targets and ranking of injectors based on efficiencies were used to determine WAG cycle lengths, WAG ratios and injection volumes. The injector efficiencies (or volume of oil associated with each injector) proved an accurate measure to the engineering teams to accurate rank injectors in order of priority for workovers and determine whether an injector was worth repairing or plugging. The initial results from the streamline model was obtained in 2022 and validated and verified with other measurements. The surveillance model produced a connectivity map between injectors and producers (FMAP) that was consistent with flow path directions from a tracer study in 2021. Furthermore, the interwell connectivities in the model across multiple patterns were consistent with well connections from production-injection data and field staff experience. This highlighted the challenge with using classical geometrical pattern allocations that are time-invariant, compared to dynamic patterns that are influenced by geology and rate. The model also flagged underperforming injectors i.e. injectors with low efficiencies that were in relatively immature areas. These injectors were known to have vertical conformance issues. The streamline model-based recommendations continued to be used through 2023. The models were updated with production and injection data quarterly. Injection rates were adjusted based on the updated injection efficiencies calculated from floodOPT with constraints based on the availability of injection volumes, well injectivity limitations, well failures and field facility limits. WAG parameters including cycle lengths and WAG ratios were set based on injection efficiency, area maturity and available CO2. Injectors with higher efficiency and lower maturity were allocated lower WAG ratios. CO2 allocation became more critical when purchases were eliminated in 2023. When the new flood management workflow was implemented, the field oil cut declines were approximately 14% pa with increasing GOR and WOR. Over a period of 6 months, favorable results were seen in these trends; GOR and WOR trends stabilized and reduced, and the oil cut decline was mitigated to less than 10% pa. Despite losing 20% of the available CO2 for injection, the oil rate in multiple patterns increased. The impact of the oil rate increase was difficult to observe on a field level because of multiple well failures and operational challenges. Nevertheless, field oil rate declines reduced from 14% pa to 9% pa; at the current decline rate, the improved management would yield an additional 300,000 bbls of oil production.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.