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Although there is a wealth of literature on experimental studies and theories of foam behavior, industry experience of foam enhanced oil recovery is still limited, and it remains a challenge to move from laboratory studies to field scale predictive simulations. This work describes a reservoir simulation study of foam enhanced oil recovery (EOR) processes, starting from a matched coreflood experiment, and extending the model to investigate foam behavior at a larger scale and over longer times. A core-scale compositional simulation model is constructed, including chemical reactions to represent foam generation, adsorption and decay over time, with a functional representation of gas mobility reduction due to foam behavior. The simulation parameters are tuned to match production and pressure for a laboratory coreflood consisting of water, gas, surfactant and Surfactant-Alternating-Gas (SAG) injection phases. The core is modeled with 2D and 3D grids at coarse and fine scales and the results are extended to field scale sector models. Sensitivity runs are performed to determine the impact of key parameters on foam behavior for different model resolutions, sizes and timescales. The impact of gravity override, heterogeneity, adsorption and decay can be observed even in core-sized models, and visualization of the simulated results over time shows the effect of grid design in capturing foam behavior in the core. At the field scale, simulations demonstrate qualitatively how foam improves oil recovery by reducing gas mobility and delaying breakthrough. However, upscaling and numerical dispersion effects can be significant when modeling chemical reactions and foam physics. The complexity and uncertainties of foam processes mean that additional measured data from experiments and pilot projects are needed, to develop confidence in predictions of field performance. In this work, we aim to fill some of the knowledge gaps in simulation of foam EOR, by describing practical approaches for modeling and understanding foam processes at different scales. We focus on important parameters for upscaling from experimental results and make recommendations for further studies to assist with de-risking foam EOR projects.
Although there is a wealth of literature on experimental studies and theories of foam behavior, industry experience of foam enhanced oil recovery is still limited, and it remains a challenge to move from laboratory studies to field scale predictive simulations. This work describes a reservoir simulation study of foam enhanced oil recovery (EOR) processes, starting from a matched coreflood experiment, and extending the model to investigate foam behavior at a larger scale and over longer times. A core-scale compositional simulation model is constructed, including chemical reactions to represent foam generation, adsorption and decay over time, with a functional representation of gas mobility reduction due to foam behavior. The simulation parameters are tuned to match production and pressure for a laboratory coreflood consisting of water, gas, surfactant and Surfactant-Alternating-Gas (SAG) injection phases. The core is modeled with 2D and 3D grids at coarse and fine scales and the results are extended to field scale sector models. Sensitivity runs are performed to determine the impact of key parameters on foam behavior for different model resolutions, sizes and timescales. The impact of gravity override, heterogeneity, adsorption and decay can be observed even in core-sized models, and visualization of the simulated results over time shows the effect of grid design in capturing foam behavior in the core. At the field scale, simulations demonstrate qualitatively how foam improves oil recovery by reducing gas mobility and delaying breakthrough. However, upscaling and numerical dispersion effects can be significant when modeling chemical reactions and foam physics. The complexity and uncertainties of foam processes mean that additional measured data from experiments and pilot projects are needed, to develop confidence in predictions of field performance. In this work, we aim to fill some of the knowledge gaps in simulation of foam EOR, by describing practical approaches for modeling and understanding foam processes at different scales. We focus on important parameters for upscaling from experimental results and make recommendations for further studies to assist with de-risking foam EOR projects.
Immiscible water-alternating-gas (iWAG) flooding is often considered as a tertiary recovery technique in waterflooded or about-to-be waterflooded reservoirs to increase oil recovery due to better mobility control and potentially favorable hysteretic changes to phase relative permeabilities. In such cases, typically, reservoir simulation models already exist and have been calibrated, often modifying saturation functions during the history matching stage. However, to utilize such models in forecasting iWAG performance, additional parameters may be required. These can be acquired by simulation of WAG coreflood experiments. While in many published cases, the parameter values obtained from matching experimental results are used without modification, this may not be advisable since the parameters are only valid at the core scale at which they were obtained. This paper discusses the challenge of systematically upscaling WAG parameters obtained at core scale to an existing full field model. In this work, we use a multi-stage upscaling process from core scale to full field scale. The first stage uses a core scale model to match ‘representative’ core flood experiments and obtain WAG parameters. The second uses a well-to-well high-resolution 1D section of the full field model populated using gridblocks of core size to generate ‘reference’ WAG performance using the unaltered WAG parameters obtained from core. The third stage uses a similar 1D model but populated using gridblocks at full field model resolution to match the results from the reference model while adjusting the WAG parameters as little as possible. Finally, a model using the full field model resolution as well as the full field relative permeability functions which, it is assumed, have been tuned to match the history and account for dispersion is used to match the reference model results and obtain final upscaled WAG parameters. The upscaled WAG parameters obtained at the end of this multi-stage process can be used at the field scale. This process allows clear quantification of the uncertainty associated with the upscaling process. Simulations at the third stage showed that once the full field to core scale grid size ratio exceeded a certain point (2500:1), there was a marked increase in the difference between upscaled and reference model results. It was found that if WAG parameters were changed in the full field model resolution model in order to match recovery results in the reference model, Land's parameter could change by up to 10% and relative permeability reduction factor could increase by up to 30% although it is expected that this will vary from case to case. It is therefore recommended to identify and use full field model resolutions to as close to the threshold as possible. The practice of using the core scale iWAG parameters in the full field model directly could under-estimate actual recovery, and overestimate injectivity. When considering the WAG mechanism alone, the value of the recovery underestimate increasing with pore volumes injected and, in our case, by up to 7% after injecting 1 pore volume of fluid. This multi-stage simulation approach helps identify the adjustments required and uncertainties associated with simulating iWAG flooding in reservoir models. This approach utilizes options widely present in commercially available finite difference simulators, addresses the challenge of utilizing existing pseudo functions and provides a practical methodology through which iWAG performance forecasting can be improved.
EOR design and risk mitigation often involves the use of laboratory results to further characterize frontal movement and interaction of the EOR agents within the reservoir; laboratory tests and protocols have been designed to address such uncertainties and constitute now a fundamental part of any EOR deployment. While the need for laboratory tests is recognized, specifics of the tests and uncertainty of the laboratory tests themselves are not widely known, or fully understood, which has the potential of inadequate usage of the laboratory results on the EOR performance estimations. This paper aims to provide guidance on use of the laboratory results in numerical models by identifying and pairing specific laboratory measurements to numerical requirements; that is recognizing what parameters are directly measured and which are estimated indirectly from either laboratory and field measurements. This paper addresses the requirements for chemical, miscible and WAG processes; and highlights the caveats of translation of the measurements onto numerical models as well as the inherent measurement uncertainties. A comprehensive numerical investigation is used to illustrate the effect and scalability of laboratory measurements for different displacement scenarios, accounting for finite-difference grid size, viscous-gravity forces, concentrations and fluid interaction. These results are used to further refine the remaining uncertainties on the displacement characterization and provide a solid start for the design of any field measurements including EOR pilot operations.
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