This paper investigates the reduction in gas mobility during the EOR (enhanced oil recovery) process of gas injection due to the presence of foam, thereby increasing sweep efficiency. The presented work is focused on developing a systematic approach to tune the CO2 foam parameters based on two separate core flooding experiments, the former conducted at variable foam qualities while the latter at a fixed foam quality. The paper discusses the experimental data required for the modeling of mobility control using CO2-foam for a high temperature, high salinity layered carbonate reservoir. An empirical foam model is used for parametric matching of laboratory data, and foam parameters are calculated and tuned. The key objective of the model is not only to match the measured apparent foam viscosity for varying foam qualities but also be able to capture the pressure drop measured for various experimental runs. The tuned foam model can be applied to field scale and design the injection strategy to maximize the oil recovery.
Polymer flooding is one of the most commonly used techniques to improve oil recovery; however its application is dependent on the technical and economic feasibility along with the knowledge of the risks involved. The presented work is focused on quantifying the uncertainties affecting the mobility of injected fluid in polymer flooding along with a sensitivity analysis of influential parameters. Initially, a coreflooding experiment on carbonate core sample is performed using partially hydrolyzed polyacrylamide, SAV 10 under high temperature high salinity conditions. The coreflood apparatus is aided with linear X-ray in order to record real time saturations for the entire length of core sample in addition to the pressure and production data. The experimental data are then history matched using commercial software to generate relative permeability curves and to optimize polymer slug size and initiation time. The optimized model is then used as a reference and a coredflood is conducted on the optimized conditions i.e. slug size and initiation time. The recovery obtained from the experimental run is compared with the simulation results. Polymer viscosity, adsorption on the rock surface and mechanical degradation are some of the other parameters included in the study. The optimum polymer flooding scenario established in this study is injection of 0.1 PV of polymer after 0.3 PV water injection. Encouraging results are obtained at the optimized conditions resulting in an overall recovery factor of 84% and early injection of polymer also helped to delay the breakthrough time. The small slug size resulted in low adsorption and residual residual factor for the optimized case is found to be 1.73.
Foam is one of the most cost-effective means of solving the drawbacks associated with the process of gas injection. The presented work is focused on probing the impact of oil saturation on CO2 foam generation and its stability in the presence of high temperature and high salinity conditions. In this work, initially a texture-implicit local equilibrium model is used for parametric matching of a core flooding experiment conducted in the absence of oil. Once the foam parameters are calculated and tuned, the designed model is later up-scaled and studied with the inclusion of oil. The key objective is to study the effect of miscibility on the foam displacement front and the degenerating effect instilled by the residual oil saturation on the stability of foam. Different field scale models are designed and compared to validate the observed effect and hypothesis. The results of this work suggests that during CO2 foam flooding the oil saturation has a profound effect on oil recovery especially when CO2 is in an immiscible state with oil when compared with the miscible state.
Summary Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.