We demonstrate how geological heterogeneity impacts the effectiveness of surfactant-based enhanced oil recovery (EOR) at larger (inter-well and sector) scales when upscaling small (core) scale heterogeneity and physicochemical processes. We used two experimental datasets of surfactant-based EOR where spontaneous imbibition and viscous displacement, respectively dominate recovery. We built 3D core-scale simulation models to match the data and parameterize surfactant models. The results were deployed in high-resolution models that preserve the complexity and heterogeneity of carbonate formations in the inter-well and sector scale. These larger-scale models were based on two outcrop analogues from France and Morroco, respectively, which capture the reservoir architectures inherent to the productive carbonate reservoir systems in the Middle East. We then assessed and quantified the error in production forecast that arises due to upscaling, upgridding, and simplification of geological heterogeneity. Simulation results showed a broad range of recovery predictions. The variability arises from the choice of surfactant model parameterization (i.e., spontaneous imbibition vs viscous displacement) and the way the heterogeneity in the inter-well and sector models was upscaled and simplified. We found that the parameterization of surfactant models has a significant impact on recovery predictions. Oil recovery at the larger scale was observed to be higher when using the parametrization derived from viscous displacement experiments compared to parameterization from spontaneous imbibition experiments. This observation clearly demonstrated how core-scale processes impact recovery predictions at the larger scales. Also, the variability in recovery prediction due to the choice of surfactant model was as large as the variability arising from upscaling and upgridding. Upscaled and upgridded models overestimated recovery because of the simplified geology. Grid coarsening exacerbated this effect because of the increased numerical dispersion. These results emphasize the need to use correctly configured surfactant models, appropriate grid resolution that minimizes numerical dispersion, and properly upscaled reservoir models to accurately forecast surfactant floods. Our findings present new insights into how the uncertainty in production forecasts during surfactant flooding depends on the way surfactant models are parameterized, how the reservoir geology is upscaled, and how numerical dispersion is impacted by grid coarsening.

We propose a workflow to optimize the configuration of multiple-interacting-continua (MINC) models and overcome the limitations of the classical dual-porosity (DP) model when simulating chemical-component-transport processes during two-phase flow. Our new approach captures the evolution of the saturation and concentration fronts inside the matrix, which is key to design more effective chemical enhanced-oil-recovery (CEOR) projects in naturally fractured reservoirs. Our workflow is intuitive and derived from the simple concept that fine-scale single-porosity (SP) models capture fracture/matrix interaction accurately; it can hence be easily applied in any reservoir simulator with MINC capabilities. Results from the fine-scale SP model are translated into an equivalent MINC model that yields more accurate results compared with a classical DP model for oil recovery by spontaneous imbibition; for example, in a water-wet (WW) case, the root-mean-square error (RMSE) improves from 0.123 to 0.034. In general, improved simulation results can be obtained when selecting five or fewer shells in the MINC model. However, the actual number of shells is case specific. The largest improvement in accuracy is observed for cases where the matrix permeability is low and fracture/matrix transfer remains in a transient state for a prolonged time. The novelty of our approach is the simplicity of defining shells for a MINC model such that the chemical-component-transport process in naturally fractured reservoirs can be predicted more accurately, especially in cases where the matrix has low permeability. Hence, the improved MINC model is particularly suitable to model chemical-component transport, key to many CEOR processes, in (tight) fractured carbonates.

We investigate how efficiently oil can be recovered from a carbonate rock during surfactant based enhanced oil recovery (EOR) at the core-scale, particularly when chemical processes change wettability, and analyse how geological heterogeneities, observed at the next larger scale (centimetre to decimetre) impacts the effectiveness of surfactant-based EOR at the inter-well scale. To quantify how heterogeneity across scales impacts surfactant flooding, we combine laboratory experiments with simulation studies at the core- and inter-well scale. We first analysed a series of surfactant imbibition experiments at different surfactant concentrations (from 0 to 3 wt. %) using reservoir cores from the Wakamuk field, a carbonate reservoir in Indonesia. We then built a 3D simulation model of the laboratory experiment and matched the experimental data to identify the key physical mechanisms (e.g., reduction in interfacial tension (IFT) and wettability alteration) that lead to increased oil recovery. Next, we parametrised the surfactant models using assisted history-matching methods to calibrate the relative permeability and capillary pressure curves as a function of surfactant concentration. These models were then deployed in high-resolution simulations at the inter-well scale. These simulations captured the small-scale geological heterogeneities that are typical for a carbonate reservoir system, e.g., the Shuaiba formation in the Middle East, but are not resolved in field-scale models. Our core-scale simulations demonstrate a change from co- to counter-current flow in the laboratory experiments and indicate that the resulting increase in oil recovery is due to a combination of IFT reduction, wettability alteration from oil- to water-wet, and capillary pressure restoration; these processes need to be captured adequately at the inter-well scale model. The increase in surfactant concentration above the critical micelle concentration (CMC) (i.e., from 1 to 3 wt. %) triggered the capillary pressure restoration and dominated recovery at the early-time. The changes in relative permeability and capillary curves during the surfactant floods were best modelled using a concentration-based interpolation. There is uncertainty when calibrating surfactant models using laboratory experiments. A key question hence is if geological heterogeneity at the inter-well scale masks these uncertainties. Results from our high-resolution simulations show that large-scale heterogeneity impacts recovery predictions, but it is the coarsening of the grid, not the upscaling of permeability, that dominates the error in field-scale recovery predictions during surfactant based EOR. Indeed, the error arising from numerical dispersion during grid coarsening can be as large as the error arising when selecting an inaccurately configured surfactant model due to the lack of quality experimental data. Hence appropriate grid refinement, possibly using adaptive grid refinement, needs to be considered when setting up a surfactant based EOR simulation, along with the appropriate configuration of the surfactant model itself.

We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected. Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator. Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low. The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.

Methane seepage at south Hydrate Ridge (offshore Oregon, United States), one of the best-studied examples of gas venting through gas hydrates, is the seafloor expression of a vigorous fluid flow system at depth. The seeps host chemosynthetic ecosystems and release significant amounts of carbon into the ocean. With new threedimensional seismic data, we image strata and structures beneath the ridge in unprecedented detail to determine the geological processes controlling the style of focused fluid flow. Numerical fluid flow simulations reveal the influence of free gas within a stratigraphic unit known as Horizon A, beneath the base of gas hydrate stability (BGHS). Free gas within Horizon A increases the total mobility of the composite water-gas fluid, resulting in high fluid flux that accumulates at the intersection between Horizon A and the BGHS. This intersection controls the development of fluid overpressure at the BGHS, and together with a well-defined network of faults, reveals the link between the gas hydrate system at depth and methane seepage at the surface.

Multi-phase flow in carbonate reservoirs, which hold about half of the world's remaining oil reserves, is strongly influenced by fractures present in the geological formations. Fractures are often the main flow conduits, leaving most of the oil behind in the low permeability rock matrix, and cause early water breakthrough. An accurate characterization of fracture flow and fluid exchange between fracture and matrix is needed to forecast oil recovery and optimise production in fracture-dominated and fracture-assisted reservoirs. Dual-porosity simulations are traditionally used to model naturally fractured reservoirs. However, the classical dual-porosity models miss some key physics of fracture - matrix fluid exchange: (1) They tend to simplify mass transfer due to spontaneous imbibition since they typically cannot include arbitrary petro-physical properties due to e.g. wettabilities, viscosity ratios, initial water content, etc. (2) They cannot account for the fact that matrix blocks within a single simulation grid cell have various sizes and permeabilities, giving rise to different transfer rates, which should be captured by a distribution of transfer functions in each grid cell. In this paper we present a novel multi-rate dual-porosity model. It is based on an unstructured finite element - finite volume technique, which solves the governing equations for two-phase flow fully implicitly. This allows us to represent complex large-scale geological structures (e.g., non-orthogonal faults and fracture corridors) accurately while small-scale diffuse fractures are modeled with an improved dual-porosity approach. Until recently, calculating the mass transfer between fractures and matrix blocks due to spontaneous imbibition for arbitrary petro-physical and fluid properties exactly was not possible because a general and exact transfer rate for arbitrary petro-physical and fluid properties was not known. In our new dual-porosity model we compute fracture-matrix transfer using the only known analytical and general solution of the Darcy equation including capillarity. This provides us with a generalised transfer function for arbitrary wettability, viscosity ratios, rock types, initial water content and boundary conditions. Each reservoir simulation grid block can contain multiple of these generalised transfer functions to model different matrix permeabilities and/or matrix block sizes present at the sub-grid scale. Using a series of proof-of-concept simulations, we show that the difference in oil recovery using a standard single-rate dual-porosity model and our new multi-rate dual-porosity model cannot be neglected. This demonstrates that our proposed model with the generalised transfer function predicts oil recovery more accurately compared to a classical dual-porosity model.

Commonly, dual-porosity and dual-permeability models are employed to simulate naturally fractured reservoirs (NFR). Recently, dual-porosity models have been extended to include more than one transfer function per simulation grid block. These so-called multi-rate dual-porosity models (MRDP) hence allow for more than one set of rock and fluid parameters to be present within one grid cell, which captures the natural variability in fracture, matrix, and fluid properties more accurately. Discrete fracture and matrix (DFM) based simulators have recently emerged as an alternative in NFR simulation. They use unstructured meshes to model large-scale fractures, fracture corridors, and faults explicitly but cannot represent small-scale fractures because field-simulations would become intractable, even with parallelisation. In this paper we demonstrate a combined MRDP-DFM approach for the first time. We demonstrate how the MRDP-DFM approach can be discretised using unstructured (tetrahedral) meshes. This facilitates the partition of the geological model into regions where (1) large-scale heterogeneities such are fracture corridors are modelled by the DFM method with appropriately upscaled single-porosity properties and (2) the flow through small-scale fractures is modelled using MRDP method with a distribution of transfer function and effective fracture permeability tensors coming from classical Discrete Fracture Network models. We use a suite of proof-of-concept simulations to demonstrate that our MRDP-DFM approach yields more realistic forecasts of oil recovery due to gravity drainage and imbibition in fractured reservoirs because it allows us to represent the multiscale heterogeneities inherent to NFRs more realistically. It hence predicts recovery processes more accurately compared to standard dual-porosity models and more efficiently compared to standard DFM models.