In-depth water diversion is a chemical Enhanced Oil Recovery (EOR) method that has been gaining acceptance recently for several reasons. One of them is the fact that sodium silicate, used in this method, is one of the few green chemicals used in EOR. In addition, this chemical has shown the ability to generate thermally activated plugs far away from the wellbore and improve oil recovery due to the better sweep, as validated in several simulation studies and field pilots. In this work, we will apply this technique to extend the lifetime of geothermal doublets in simulations of low-enthalpy geothermal projects. The simulation model consists of a thermal-compositional reactive formulation that was implemented in Stanford’s Automatic Differentiation General Purpose Research Simulator (ADGPRS) based on a fully implicit approach. The motivation for selecting this method is the strong coupling between chemical and flow variables linking the drastic changes in permeability induced by the reaction. The implementation of the silicate reaction assumes the oligomerization reaction proposed in Icopini et al. (2005) with kinetic rate suggested in Hiorth et al. (2016). This model describes the accumulation of solid silicate through a solid phase deposition and the resulting permeability changes due to pore blockage following a correlation described in Hiorth et al. (2016). We start with validation of the proposed model with an EOR case and obtain a close match to previous simulations as reported in Trujillo (2017). In this application, the model shows a successful generation of a plug around the middle of the reservoir, increased oil production rates after water breakthrough and an overall increase in cumulative oil production over 7%. Next, we apply the same model to a geothermal application where the generation of a plug helps to increase the time when the cold water is breaking through to the production well, thus extending the geothermal doublet lifetime. The plug placement has proven to be sensitive to different parameters such as the silicate concentration in the injected solution, the volume of the pre-flush batch and the total volumes of silicate solution injected. In addition, several numerical parameters, such as spatial and temporal resolution, can affect the accuracy of simulation results. In our study, we perform a sensitivity study to address these factors in typical hydrocarbon production and low-enthalpy geothermal projects.@en

We investigate the potential for improved recovery of subsurface energy resources (hydrocarbons or heat) through in-depth diversion technology. A number of pilot studies in the North Sea have demonstrated in recent years that sodium silicate can be used to block preferential flow paths and divert water to previously unswept areas of a reservoir. Accompanying simulation studies based on an explicit weak coupling of a reservoir flow simulator and an external chemical module have attempted to replicate the observed behaviour. Since the development of silicate gels and the accompanying permeability reduction is essentially a coupled flow-chemical process, we first will present a fully implicit compositional-reactive flow and transport implementation and investigate the impact of the grid and time-stepping resolution on simulation performance in 2D subsurface reservoirs mimicking petroleum and geothermal applications. We proceed to investigate the sensitivity of the recovery to design parameters of the in-depth diversion strategy. Since adjoint gradients are not typically available for these parameters and uncertainties associated with an application of in-depth divergence are large, we use an ensemble-based methodology to perform an optimization study. This study aims to find optimal strategies for combined waterflooding and design of in-depth diversion under geological uncertainty. It is demonstrated that in-depth diversion can significantly extend the life-time of hydrocarbon or geothermal fields when the timing of injection and the size of the sodium silicate batch is optimized. Finally, we discuss methods that help to address an issue of computational cost associated with the high resolution required for accurate simulation of the coupled process.@en