Understanding crude oil/brine interface chemistry is essential to elucidating the effect of low-salinity waterflooding (LSWF) on enhanced oil recovery (EOR). The acid and base functional groups in crude oil result in an electrostatic interaction with the rock’s surface, thereby affecting wettability conditions. Moreover, the content of carboxyl acid components is a key factor influencing electrostatic interaction during LSWF. In this study, the number of carboxyl groups in four different crude oils with varying acid number (AN) was estimated using a combination of zeta potential experiments and a triple-layer surface complexation model. In addition, the surface complexation modeling parameters for the dissociation of carboxyl groups and the adsorption of calcium and magnesium ions were also determined. The experimentally determined parameters and carboxyl groups sufficiently predicted the crude oil/brine interface at high and low salinities of seawater and formation water. The density of carboxyl groups (expressed in sites/nm2) is logarithmically related to the AN of crude oil, and it is revealed that the effect of AN on the density is lower for high-AN crude oil. Further, for crude oils with high AN, divalent cations exhibit higher adsorption ability than those with low-AN crude oil. The percentage of resin components in crude oil has a linear relationship with the number of carboxyl sites, thus indicating the importance of resin components in crude oil/brine interface chemistry. The study discusses the influence of AN on potential distribution and possible wettability alteration by LSWF in sandstone and carbonate reservoirs.
Reservoir simulation to predict production performance requires two steps: one is history-matching, and the other is uncertainty quantification in forecasting. In the process of history-matching, rock relative permeability curves are often altered to reproduce production data. However, guidelines for changing the shape of the curves have not been clearly established. The aim of this paper is to clarify the possible influence of relative permeabilities on reservoir simulation using the uncertainty envelope.We propose a method for adjusting the shape of relative permeability curves during history-matching at the coarse scale, using the Neighbourhood Approximation algorithm and B-spline parameterisation. After generating multiple history-matched models, we quantify the uncertainty envelope in a Bayesian framework. Our approach aims at encapsulating sub-grid heterogeneity in multi-phase functions directly in the coarse-scale model, and predicting uncertainty. In this sense, the framework differs from conventional procedures which perturb fine-scale features, upscale the models and evaluate each performance. In addition, B-spline parameterisation is flexible allowing the capture of local features in the relative permeability curves. The results of synthetic cases showed that the lack of knowledge of the subgrid permeability and the insufficient production data provoked a substantial amount of uncertainty in reservoir performance forecasting.
Reservoir simulation to predict production performance requirestwo steps: one is history-matching, and the other is uncertainty quantification in forecasting. In the process of history-matching, rock relative permeability curves are often altered to reproduce production data. However, guidelines for changing the shape of the curves have not been clearly established. The aim of this paper is to clarify the possible influence of relative permeabilities on reservoir simulation using the uncertainty envelope. We propose a method for adjusting the shape of relative permeability curves during history-matching at the coarse scale, using the Neighbourhood Approximation algorithm and B-spline parameterisation. After generating multiple history-matched models, we quantify the uncertainty envelope in a Bayesian framework. Our approach aims at encapsulating sub-grid heterogeneity in multi-phase functions directly in the coarse-scale model, and predicting uncertainty. In this sense, the framework differs from conventional procedures which perturb fine-scale features, upscale the models and evaluate each performance. In addition, B-spline parameterisation is flexible allowing the capture of local features in the relative permeability curves. The results of synthetic cases showed that the lack of knowledge of the subgrid permeability and the insuffcient production data provoked a substantial amount of uncertainty in reservoir performance forecasting. Introduction Reservoir simulation is routinely employed in the prediction of reservoir performance under different depletion and operating scenarios. This practical use of reservoir simulation requires two steps: one is history-matching, and the other is uncertainty quantification in forecasting. In the traditional approach, a single history-matched model, conditioned to production data, is obtained, and is used to forecast future production profiles. Since the history-matching is non-unique, the forecast production profiles are uncertain. Recently, in order to take account of the non-uniqueness of the inverse problem, a new methodology for uncertainty quantification has been introduced to the petroleum industry. The Markov Chain Monte Carlo method has been adopted by [1, 2, 3, 4], along with the Neighbourhood Approximation [5, 6], in order to investigate parameter space.
Several mechanisms have been proposed for enhanced oil recovery (EOR) in low salinity waterflooding (LSWF). Coupling of the significant processes affecting crude oil-brine-rock system is necessary to understand the LSWF effect. In this study, mineral thermodynamic equilibrium and surface complexation reactions at crude oil/brine and calcite/brine interfaces were coupled with solute transport to simulate LSWF in carbonate reservoir. The dissolution and precipitation of minerals were considered thorough thermodynamic phase-equilibrium model, and the triple-layer surface complexation model was developed to predict the interface reactions and the associated surface and zeta potentials. These models were coupled with solute transport model to predict ionic profiles and oil recovery during LSWF. In the integrated geochemical model, the crude oil was considered as colloids and the ionic adsorbed/ionized and un-ionized surface groups of oil were transported via advective and dispersive transport. These sub-models were coupled in a geochemical code PHREEQC. The coupled model was first used to predict Ca2+ and Mg2+ profiles in chalk saturated with NaCl without crude oil. The agreement between published experimental data and simulation results validate the proposed model. A nearly equal equilibrium constant in the surface complexation model provides a similar breakthrough composition for Ca2+ and Mg2+ ions. The model was further validated in chalk core aged with the crude oil. Both model and experimental results show an earlier breakthrough composition of sulphate in oil-aged core. The model was then used to predict ionic profiles and oil recovery in two-phase flow experiment. The modelling results reproduces the experimental data on relative concentration of ionic species and pH increase with dilution of injecting water, however additional mechanism should be incorporated in the model for better prediction of oil recovery.
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