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Summary Modeling the dynamic fluid behavior of low-salinity waterflooding (LSWF) at the reservoir scale is a challenge that requires a coarse-grid simulation to enable prediction in a feasible time scale. However, evidence shows that using low-resolution models will result in a considerable mismatch compared with an equivalent fine-scale model with the potential of strong, numerically induced pulses and other dispersion-related effects. This work examines two new upscaling methods that have been applied to improve the accuracy of predictions in a heterogeneous reservoir where viscous crossflow takes place. We apply two approaches to upscaling to bring the flow prediction closer to being exact. In the first method, we shift the effective-salinity range for the coarse model using algorithms that we have developed to correct for numerical dispersion and associated effects. The second upscaling method uses appropriately derived pseudorelative permeability curves. The shape of these new curves is designed using a modified fractional-flow analysis of LSWF that captures the relationship between dispersion and the waterfront velocities. This second approach removes the need for explicit simulation of salinity transport to model oil displacement. We applied these approaches in layered models and for permeability distributed as a correlated random field. Upscaling by shifting the effective-salinity range of the coarse-grid model gave a good match to the fine-scale scenario, while considerable mismatch was observed for upscaling of the absolute permeability alone. For highly coarsened models, this method of upscaling reduced the appearance of numerically induced pulses. On the other hand, upscaling by using a single (pseudo)relative permeability produced more robust results with a very promising match to the fine-scale scenario. These methods of upscaling showed promising results when they were used to scale up fully communicating and noncommunicating layers as well as models with randomly correlated permeability. Unlike documented methods in the literature, these newly derived methods take into account the substantial effects of numerical dispersion and effective concentration on fluid dynamics using mathematical tools. The methods could be applied for other models where the phase mobilities change as a result of an injected solute, such as surfactant flooding and alkaline flooding. Usually these models use two sets of relative permeability and switch from one to another as a function of the concentration of the solute.
Al Kalbani, Mandhr (Heriot–Watt University) | Al Shabibi, Hatem (Heriot–Watt University) | Ishkov, Oleg (Heriot–Watt University) | Silva, Duarte (Heriot–Watt University) | Mackay, Eric (Heriot–Watt University) | Baraka-Lokmane, Salima (Total) | Pedenaud, Pierre (Total)
Summary Injection of low-sulfate seawater (LSSW) instead of untreated full-sulfate seawater (FSSW) is widely used to mitigate barium sulfate scaling risk at the production wells. LSSW injection may no longer be required when the barium concentrations in the produced water drop below a certain threshold. Such a trigger value could be estimated from the barium sulfate precipitation tendency. Relaxation of requirements for the sulfate reduction plant (SRP) can significantly reduce operational costs. This study investigates the impact of several parameters on the timing and degree of relaxation of the output sulfate concentration by the SRP. Finally, the optimal switching strategy is proposed for a field case. The strategy for switching from LSSW to FSSW (e.g., time and method; direct or gradual increase in the sulfate concentration) was initially investigated using generic 2D areal and vertical models. The sensitivity study included the impact of reservoir heterogeneity and the initial barium and sulfate ion concentrations. Findings were later applied on a full-field reservoir simulation model followed by a mineral scale prediction software to investigate the specific switching strategy for a field that has multiple wells and significantly more complex heterogeneity. The results show that barium concentrations in the formation brine affect the choice of switching time more than the output sulfate concentration produced by the SRP. The degree of heterogeneity around the producers also has a significant impact on the switching time. Another parameter is the contrast in the permeability between layers; higher contrast allows a longer period of coproduction of the scaling ions and thus delays the switching time. In the field case, switching to FSSW at early times allows higher consumption of barium ions because of its in-situ precipitation. Barium is no longer a limiting ion, and so a higher degree of deep reservoir precipitation reduces the requirement for prolonged LSSW injection. Another strategy is a gradual relaxation of LSSW output, which allows even earlier buildup of the injected sulfate concentration compared with the direct FSSW switch. The study investigates the reservoir parameters that affect sulfate relaxation of LSSW injection for a field. After the proposed workflow, the optimal relaxation strategy can be designed for other field cases.
Summary Numerical fidelity is required when using simulations to predict enhanced-oil-recovery (EOR) processes. In this paper, we investigate the conditions that lead to numerical errors when simulating low-salinity (LS) waterflooding (LSWF). We also examine how to achieve more accurate simulation results by scaling up the flow behavior in an effective manner. An implicit finite-difference numerical solver was used to simulate LSWF. The accuracy of the numerical solution has been examined as a function of changing the length of the grid cell and the timestep. Previously we have shown that numerical dispersion induces a physical retardation such that the LS front slows down while the formation water front speeds up. We also report for the first time that pulses can be generated as numerical artifacts in coarsely gridded simulations of LSWF. These effects reflect the interaction of dispersion, the effective-salinity range, and the use of upstream weighting during calculation, and can corrupt predictions of flow behavior. The effect of the size of the timestep was analyzed with respect to the Courant condition, traditionally related to explicit numerical schemes and also numerical stability conditions. We also investigated some of the nonlinear elements of the simulation model, such as the differences between the concentrations of connate water salinity and the injected brine, effective-salinity-concentration range, and the net mobility change on fluids through changing the salinity. We report that to avoid pulses it is necessary, but not sufficient, to meet the Courant condition relating timestep size to cell size. We have also developed two approaches that can be used to scale up simulations of LSWF and tackle the numerical problems. The first method is dependent on a mathematical relationship between the fractional flow, effective-salinity range, and the Péclet number and treats the effective-salinity range as a pseudofunction. The second method establishes an unconventional proxy method equivalent to pseudorelative permeabilities. A single table of pseudorelative permeability data can be used for a waterflood instead of two tables, as is usual for LSWF. This is a novel approach that removes the need for relative permeability interpolation during the simulation. Overall, by avoiding numerical errors, we help engineers to more efficiently and accurately assess the potential for improving oil recovery using LSWF and thus optimize field development. We also avoid the numerical pulses inherent in the traditional LSWF model.
Abstract We investigate the effect of heterogeneous petrophysical properties on Low Salinity Water Flooding (LSWF). We considered reservoir scale models, where the geological properties were obtained from a giant Middle East carbonate reservoir. The results are compared against a typical sandstone model. We simulated low salinity induced wettability changes in field scale models in which the petrophysical properties were randomly distributed with spatial correlation. We examined a wide range of geological realisations which mimic complex geological structures. Sandstone was simulated using a log-linear porosity-permeability relation with fairly good correlation. A carbonate reservoir from the Middle East was simulated where a much less correlated porosity permeability relationship was obtained. The salinity of formation water was set to typically observed values for the sandstone and carbonate cases. A number of simulations were then carried out to assess the flow behaviour. We have found that the general trend of permeability-porosity correlation has a key role that could mitigate or aggravate the impact of spatial distributions of petrophysical properties. We considered models with a log-linear permeability-porosity correlation, as generally observed for sandstone reservoirs. These are likely to be directly affected by the spatial distribution more than models with a power permeability-porosity correlation, which is often reported for flow units of carbonate reservoirs. The scatter of data in the permeability-porosity correlations had a relatively small impact on the flow performance. On the other hand, the effect of heterogeneity decreases with the width of the effective salinity range. Thus, uncertainty in carbonate reservoirs arises due to the ambiguity of spatial distribution of permeability and porosity would be less affects the LSWF predictability than in sandstone case. Overall, the incremental oil recovery due to LSWF was higher in the carbonate models than the sandstone cases. We observe from uncertainty analysis that the formation waterfront was less fingered than the low salinity waterfront and the salinity concentration. The dispersivity of salinity front and the water cut can be estimated for models with various degrees of heterogeneity. The outcome of the study is a better understanding of the implications of heterogeneity on LSWF. In some cases the behaviour can appear like a waterflood in very heterogeneous cases. It is important to assess the reservoir effectively to determine the best business decision.
Abstract Geothermal energy refers to the heat stored in the subsurface that can be extracted by producing the hot fluids (water and/or steam) in contact with the hot formation. A major issue that may restrict the extraction of geothermal energy is precipitation of mineral scales which can occur within the reservoir, inside the wellbore, or surface facilities. The objective of this paper is to find the most efficient scale treatment strategy to prevent mineral scaling. Continuous injection of chemical scale inhibitor (SI) downhole in the production well, is the most common method to prevent mineral scale in geothermal plants. This method although effective does not protect the near-wellbore area, where the highest pressure drop is expected. To address this issue, two methods will be studied, bullheading the production well with SI, commonly known as squeeze treatment, and injecting SI in the injection well. Optimum designs for both methods were identified considering different levels of SI adsorption, and also permeability variation in fractured and non-fractured formations. As expected, the volume of SI required in continuous injection in producer was lower than the other two methods. However, in cases where the highest risk of precipitation is in the near-wellbore area or it is below the continuous injection point, it is necessary to apply one of the suggested methods. While the squeeze treatment protects only the formation around the producer well, treatments deployed in injector wells will protect the whole system and this extra protection may offset the extra volume of chemical necessary. The application of SI in injector well was studied in both continuous and batch mode with different injection frequencies. It was shown in continuous injection that even though less SI volume is used, the SI breakthrough time in producer can be so long that a series of squeeze treatments might be required to protect the well. The simulation results showed that in high adsorption formations, squeeze treatment is more efficient than deploying SI in the injector well. However, in cases of low adsorption and fractured reservoirs, the scenario commonly found in geothermal plants, SI injection at the injector is more optimal. Two different scale treatment methodologies were studied in geothermal wells, including squeeze treatment in producer and SI injection in the injector and the results were compared with the continuous SI injection in producer, which is the most current treatment in geothermal wells. It was illustrated in fractured geothermal reservoirs with relatively low levels of adsorption, that SI injection in the injector is the most optimum treatment that can effectively protect the whole plant from scaling.
Al-Rudaini, Ali (Heriot-Watt University) | Geiger, Sebastian (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Maier, Christine (Heriot-Watt University) | Pola, Jackson (Heriot-Watt University)
Summary 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 chemicalcomponent-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. Introduction Nearly more than one-half of the world's remaining oil reserves (Burchette 2012) and less than one-half of the world's remaining gas reserves are in carbonate reservoirs (Montaron 2008; Burchette 2012). Fractures are more common in carbonate reservoirs than in clastic reservoirs (Ahr 2008), and much oil can be left behind in the rock matrix, which is typically oil-wet (OW) and mixed-wet (MW) (Treiber and Owens 1972), causing low recovery factors. The scope of this paper is to investigate the applicability of different DP models for predicting the transport of chemical components from fractures into a matrix block by means of spontaneous imbibition (SI). The effects of matrix permeability and wettability conditions are also investigated.
Scale Inhibitor Squeeze treatments are some of the most common techniques to prevent oilfield mineral scale deposition in oil producers. A squeeze treatment design's effectiveness and lifespan is determined by the scale inhibitor (SI) retention, which can be described using a pseudo-isotherm adsorption, commonly derived from coreflooding experiments, although in some certain circumstances a new isotherm will need to be re-derived to match the field return concentration profile, once the treatment is deployed and samples are collected to measure SI return concentration. This new isotherm is used to design the next treatment. The objective of this manuscript is to quantify the uncertainty, which depends of the number of samples analyzed. In any inverse problem, there might not be a unique solution, which is in our context a pseudo-isotherm matching the return concentration profile. As a consequence, there will be a certain level of uncertainty predicting the next squeeze treatment lifetime. Solving this inverse problem in Bayesian formulation, incorporating the prior information, and the likelihood involving the return concentration profile, it is possible to quantify the posterior distribution, and therefore calculate the uncertainty range, commonly known as P90/P50/P10, based on the Randomized Maximum Likelihood (RML) approach. The P90/P50/P10 was calculated as a function of the number of samples available, differentiating from the early production and late production.
The results suggest that there is a correlation between the P90/P50/P10 interval and the number of samples, i.e. the differences between the P10 and P90 in terms of the forecast squeeze lifetime was wider the smaller number of samples. The methodology proposed may be used to determine the number of samples required to reduce the level of uncertainty predicting the lifetime of the next squeeze treatment. Although taking more samples may increase the cost per barrel for a treatment, the ability to predict accurately treatment lifetime will be more cost effective in the long term, as production might not be affected.
Al Kalbani, Munther (Heriot-Watt University) | Al Shabibi, Hatem (Heriot-Watt University) | Ishkov, Oleg (Heriot-Watt University) | Silva, Duarte (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Baraka-Lokmane, Salima (Total) | Pedenaud, Pierre (Total)
Injection of Low Sulphate Seawater (LSSW) instead of untreated Full Sulphate Seawater (FSSW) is widely used to mitigate barium sulphate (BaSO4) scaling risk at production wells. LSSW injection may no longer be required when the barium (Ba2+) concentrations in the produced water drop below a certain threshold. Such a trigger value could be estimated from the BaSO4 precipitation tendency. Relaxation of requirements for the Sulphate Reduction Plant (SRP) can significantly reduce operational costs. This study investigates the impact of several parameters on the timing and degree of relaxation of the output sulphate (SO42-) concentration by the SRP. Finally, the optimal switching strategy is proposed for a field case.
The strategy for switching from LSSW to FSSW, e.g. time and method (direct or gradual increase in the SO42- concentration) were initially investigated using generic 2D areal and vertical models. The sensitivity study included the impact of reservoir heterogeneity and initial Ba2+ and SO42- ion concentrations. Findings were later applied on a full field reservoir simulation model followed by a mineral scale prediction software to investigate the specific switching strategy for a field that has multiple wells and significantly more complex heterogeneity.
Results show that Ba2+ concentrations in the formation brine impact the choice of switching time more than the output SO42- concentration produced by the SRP. The degree of heterogeneity around the producers also has a significant impact on the switching time. Another parameter is the contrast in the permeability between layers; higher contrast allows longer period of co-production of the scaling ions and thus delays the switching time. In the field case, switching to FSSW at early times allows higher consumption of Ba2+ ions due to its
The study investigates the reservoir parameters that impact SO42- relaxation of LSSW injection for a field. Following the proposed workflow, the optimal relaxation strategy can be designed for other field cases.
Azari, Vahid (Heriot-Watt University) | Vazquez, Oscar (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Sorbie, Ken (Heriot-Watt University) | Jordan, Myles (Champion X) | Sutherland, Louise (Champion X)
The application of chemical scale inhibitors (SI) in a squeeze treatment is one of the most commonly used techniques to prevent downhole scale formation. This paper presents a sensitivity analysis of the treatment design parameters, to assist with the automated optimization of squeeze treatments in single wells in an offshore field.
Two wells were studied with different constraints on total SI neat volume (VSI) and total injected volume (VT) including main pill and overflush volumes, followed by a field case squeeze optimization to demonstrate the sensitivity to lifetime and the cost function per treated volume of water. A purpose-designed squeeze software model was used to simulate the squeeze treatments and perform the sensitivity analysis. In the course of this optimization procedure, a "Pareto Front" is calculated which represents cases that
It was demonstrated at fixed values of VSI and VT (resulting in almost a fixed total cost for squeeze), the squeeze lifetime can be improved by increasing the scale inhibitor concentration in the main treatment slug; however, the increase in squeeze lifetime is greatly reduced at very high concentrations. Four generic scale inhibitors were used with different adsorption isotherms to validate these calculations. In cases where either VSI or VT is fixed, it is shown that the squeeze life does not monotonically increase by the other parameter and the cost function can be used to determine the optimum design.
Well squeeze optimization was performed and these recommendations were applied in the field. It was shown that a well-executed sensitivity study can prevent misleading results that miss the global optimum. A lesson learned was that the optimal designs entail injecting as much of the inhibitor as possible as early in the squeeze design as possible - provided formation damage effects are avoided. Also, our semi-analytical construction of the Pareto Front greatly helps to simplify and streamline the overall squeeze optimization process.
Rodrigues, Hydra (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Arnold, Daniel (Heriot-Watt University) | Azari, Vahid (Heriot-Watt University) | Vazquez, Oscar (Heriot-Watt University)
How to estimate operational controls so as to optimize economic returns in CO2-WAG projects and reduce calcite scale risk? The reactivity and heterogeneity intrinsic to carbonate reservoirs make CO2-WAG (Water Alternating Gas) injection a big challenge. While miscibility effects greatly increase oil recovered, the presence of CO2 can cause severe flow assurance issues. The aim of this paper is to introduce a simulation-based methodology to optimize the design of CO2-EOR operations, considering economics, mineral scaling risk and environmental impact.
A compositional reservoir model was built to simulate a reactive 3-phase miscible flow in porous media. Aiming at maximizing the Net Present Value (NPV), we optimized the following operational variables: duration of waterflooding period; injection rates; producer bottomhole pressure (BHP); WAG ratio, gas half-cycle duration and number of cycles for different WAG stages (tapered WAG). We then used the forecasted data to quantify calcium carbonate scaling tendency for the scenarios of interest and design scale management strategies (squeeze treatments) with the lowest costs.
The optimal WAG design found through the methodology increased NPV by 55.6% compared to a base-case waterflooding scenario. We also found that performing a waterflood in a carbonate reservoir with high CO2 content will result in more severe calcite scale risk than applying equivalent WAG schemes. A lower production BHP can reduce the potential for precipitation, by allowing the CO2 to evolve from the aqueous solution within the reservoir, before it arrives at the production wellbore. On the other hand, a lower producer BHP can increase water production rates and, therefore, scale risk.
The proposed workflow provides valuable insights on the procedures involved in simulating and optimizing CO2-WAG projects with high calcite scale risk. Its application demonstrated the importance of an integrated analysis that seeks for higher economic returns in a sustainable manner, with reduced production issues caused by CO2 speciation.