Chemical Flood Simulation of Laboratory Corefloods for the Mangala Field: Generating Parameters for Field-Scale Simulation

Abstract

Detailed laboratory experiments were carried out to evaluate the potential for various chemical flood processes (polymer, alkali-polymer, and alkali-surfactant-polymer) in Mangala, a large oilfield in India containing waxy crude with viscosity of 7–20cp. Experiments included fluid-fluid and fluid-rock interaction studies, followed by a series of linear and radial corefloods. Simulation of the corefloods was carried out using STARS, the advanced compositional simulator available from CMG. The main coreflood simulation objective was to shed light on the various process mechanisms and to generate chemical flood parameters for field-scale simulation forecasts. Primary matching parameters included performance of the base waterflood, injection pressures to define shear-thinning polymer behavior, chemical flood performance to define interpolations between appropriate relative permeability curves based on capillary numbers, and produced chemical concentrations to define adsorption parameters. Sensitivity studies on the matching parameters examined the impacts of heterogeneity within the large-diameter radial cores, chemical adsorption, and capillary pressure and gravity effects. The coreflood simulations provided significant insights into the chemical flood process mechanisms and eliminated uncertainties such as the effect of gravity in radial coreflood experiments; although some issues including the role of capillary end-effects have not as yet been completely resolved. The modeling showed that in situ saturation monitoring (scanning) of the cores is critical for future planned corefloods. The study also showed some of the current limitations in modeling these complex processes. Chemical flood parameters fine-tuned during the coreflood simulations have been used in field-scale simulations to evaluate expected performance and to aid in design of a field-scale pilot project. Extensive field-scale simulations have been used to design an appropriate chemical flood implementation strategy. These works indicate potential field-scale incremental recoveries over waterflooding of ~7% STOIIP for a polymer flood and ~15% STOIIP for an alkaline-surfactant-polymer flood.

Effect of initial water flooding on the performance of polymer flooding for heavy oil production

“…More complex models have been developed to match experimental observations such as dependency of Irreducible Oil Saturation (S or ) on the polymer concentration and Capillary Number (N ca ) (Chen et al, 2008(Chen et al, , 2011Huh and Pope, 2008;Pandey et al, 2008). However, the number of variables necessary to calibrate these models before implementing them in full field simulations is challenging.…”

History matching of experimental polymer flooding for enhanced viscous oil recovery

“…This process consists of adjusting the model input data until obtaining the minimum difference between the results of the simulated model and histories of the laboratory data. Various published works showed different levels of success in the application of the history match of polymer core flooding [1,13,20,21,25,[33][34][35]. The parameters of adjusted models can then be used for production forecasts in field-scale models [33].…”

“…Various published works showed different levels of success in the application of the history match of polymer core flooding [1,13,20,21,25,[33][34][35]. The parameters of adjusted models can then be used for production forecasts in field-scale models [33].…”

“…Pandey et al [33] history matched oil production, injection pressure and produced chemicals in the polymer, surfactant polymer (SP) and alkali surfactant polymer (ASP) radial core floods. However, to achieve a good match in the polymer flooding experiments, the authors changed the relative permeability curves during polymer flooding and used a higher adsorption value than the one measured experimentally.…”

“…Several recent works focused on the determination of properties through history matching a simulation model to experimental data, e.g., polymer adsorption [21,33] or relative permeability [13,20]. However, history matching an experiment through sensitivity analysis of a property that heavily influences the flow behavior may mask combined effects of other properties or hide imperfections of inaccurate modeling of a phenomenon.…”