Commercial Reservoir Simulator Research Articles

Estimation of CO2 storage capacities in saline aquifers using material balance

Estimating CO2 storage capacities is crucial in developing carbon capture and storage projects. Material balance equation (MBE) methods, widely employed for oil and gas reserve estimation, offer a direct approach to estimating CO2 storage capacities. However, previous MBE methods rely on an original fluid in-place volume calculated using volumetric methods to estimate CO2 storage capacities, lacking validation for accuracy. It is essential to accurately estimate the original fluid in-place volume, representing the pore volume, as it substantially influences CO2 storage capacity. This study presents a refined MBE method that ensures accurate estimates of CO2 storage capacities by validating the original fluid in-place volumes in saline aquifers. The accuracy of this method was evaluated by comparing it with a commercial reservoir simulator for a synthetic aquifer example and the Sleipner L9 model. In the synthetic aquifer example, the relative error in CO2 storage capacity estimation with the proposed MBE method was only 2.09%, even when short-term (1-year) injection data were utilized. The proposed MBE method demonstrates consistent accuracy in estimating CO2 storage capacities under different aquifer properties, operating conditions, and MBE-related conditions. The proposed MBE method also accurately estimated the CO2 storage capacity in the Sleipner L9 model, achieving a relative error of 3.47%.

Optimization of Discontinuous Polymer Flooding Processes for Offshore Oilfields Using a Novel PSO–ICA Algorithm

Recently, discontinuous polymer flooding has been proposed and successfully applied in some offshore oilfields. The performance of discontinuous polymer flooding depends on various operational parameters, such as injection timing, polymer concentrations, and crosslinker concentrations of four types of chemical slugs. Because the number of the operational parameters are large and they are nonlinearly related, the traditional reservoir numerical simulation might not simultaneously obtain the optimal results of these operational parameters. In this study, to simulate the discontinuous polymer flooding processes, a simulation model was built using a commercial reservoir simulator (CMG STARS), in which the mechanisms of the four types of chemical slugs were considered, such as polymer viscosification, adsorption, and degradation. Then, a PSO–ICA algorithm was developed by using the PSO algorithm to improve the exploration ability of the ICA algorithm. The codes were written with MATLAB and linked to CMG STARS to perform optimization processes. Finally, the PSO–ICA algorithm was compared with the ICA and PSO algorithms on benchmark functions to verify its reliability and applied to optimize a discontinuous polymer flooding process in a typical offshore oilfield in Bohai Bay, China. The results showed that the developed PSO–ICA algorithm had lower iteration numbers, higher optimization accuracy, and faster convergence rate than these of PSO and ICA, indicating that it was an effective method for optimizing the operational parameters of discontinuous polymer flooding processes. Compared to the continuous polymer flooding, the discontinuous polymer flooding had a higher oil production rate, a lower water cut, and a lower residual oil saturation. The net present value of the optimal scheme of discontinuous polymer flooding reached 7.49 × 108 $, which is an increase of 6% over that of the scheme of continuous polymer flooding. More research including selecting more reasonable parameters of the PSO–ICA algorithm to increase its optimization accuracy and convergence rate, comparing with other available optimization algorithms, and verifying the performance of the optimal scheme of discontinuous polymer flooding in the practical offshore oilfield will be required in the future.

Evaluating the Impact of Biochemical Reactions on H2 Storage in Depleted Gas Fields

Summary Underground hydrogen storage (UHS) is an emerging technology to store energy, produced by renewable sources, in subsurface porous formations. UHS efficiency in depleted gas reservoirs can be affected by H2 biochemical degradation due to interactions with rock, brine, and gas. In the reservoir, subsurface microorganisms can metabolize H2 with possible hydrogen losses, H2S production, clogging, and formation damage. In this work, we investigate the impact of hydrogen losses due to microbial activities on UHS operations in depleted gas reservoirs lying in sandstone formations. We developed a workflow to exploit the chemical reactive transport functionalities of a commercial reservoir simulator, to model biochemical processes occurring in UHS. Kinetic chemical reaction formulation was used to replicate a Monod’s type microorganism growth, using PHREEQC to tune reaction parameters by matching a 0D process in an ideal reactor. Then, we applied the methodology to evaluate the impact of biotic reactions on UHS operations in depleted gas fields. Eventually, various sensitivities were carried out considering injection/production cycle lengths, cushion gas volumes, and microbial model parameters. Benchmark against PHREEQC demonstrated that, by properly tuning the kinetic reaction model coefficients, we are capable of adequately reproducing Monod-like growth and competition of different microbial community species. Field-scale results showed that hydrogen losses due to biochemistry are limited, even though this may depend on the availability of reactants in the specific environment: In this work, we focus on gas reservoirs where the molar fraction of the key nutrient, CO2, is small (≤2%) and the formation is a typical sandstone. Operational parameters (e.g., storage cycle length) have an impact on the biochemical dynamics and, then, on the hydrogen degradation and generation of undesired byproducts. Similar considerations hold for the model microbial growth kinetic parameters: In this study, they were established using available literature data for calibration, but we envisage tuning them using experimental results on specific reservoirs. The current model setup does not account for rock-fluid geochemical interactions, which may result in mineral precipitation/dissolution affecting the concentration of substrates available for biotic reactions. Nonetheless, it can provide an estimate of hydrogen consumption during storage in depleted gas reservoirs due to microbial activities. This study is among the first attempts to evaluate the impact of hydrogen losses by the presence of in-situ microbial populations during hydrogen storage in a realistic depleted gas field. The assessment was performed by implementing a novel workflow to encapsulate biochemical reactions and bacterial dynamic growth in commercial reservoir simulators, which may be applied to estimate the efficiency and associated risks of future UHS projects.

Numerical Reservoir Simulation of Supercritical Multi-Source and Multi-Component Steam Injection for Offshore Heavy Oil Development

We present the workflow for numerical reservoir simulation of supercritical multi-source and multi-component steam injection for offshore heavy oil development. We have developed unique techniques in a commercial reservoir simulator to implement the thermal properties of supercritical multi-source and multi-component steam, the pyrolysis chemical reactions, the temperature-dependent relative permeability, and the process of partially dissolving the sandstone rock to enhance the matrix permeability in a commercial reservoir simulator. Simulations are conducted on the type pattern reservoir model, which represents one of the heavy oil fields in CNOOC’s Bohai Bay oil field. Simulation input parameters are calibrated based on laboratory experiments conducted for supercritical multi-source and multi-component steam injection. Simulation results have shown clear improvements in injecting supercritical multi-source and multi-component steam in offshore heavy oil reservoirs compared to the normal steam injection process using subcritical steam. This serves as a workflow for implementing a numerical simulation of the novel supercritical multi-source and multi-component steam injection recovery process.

Development of a heat-penetration criterion to characterize steam chamber growth and propagation dynamics during a steam-assisted gravity drainage (SAGD) process

Steam chamber growth and propagation dynamics dictates the success of a steam-assisted gravity drainage (SAGD) process, which is an enhanced oil recovery (EOR) method for effectively and efficiently exploiting the enormous heavy oil and bitumen reserves. To set the baseline, this work is focused on the SAGD process with pure steam. After numerous trials and errors, a heat-penetration criterion has been developed and integrated into a reservoir simulator for the first time to characterize steam chamber growth and propagation dynamics conditioned to the production profiles during a steam-assisted gravity drainage process. More specifically, based on the heat transfer principle, a pore-scale heat-penetration criterion has been developed with respect to both heat conduction and convection for calibrating the temperature distributions ahead of the steam chamber interface (SCI). Subsequently, such a heat-penetration criterion has been upscaled to a grid-scale for its integration with a commercial reservoir simulator (i.e., CMG STARS), allowing us to pragmatically and accurately quantify the dynamic temperature field during a steam-assisted gravity drainage process. Furthermore, three sets of multiphase relative permeabilities are employed to evaluate comprehensive impacts of the thermally sensitive co/counter-current flows. Finally, such a heat-penetration criterion has been verified by reproducing the experimentally measured steam chamber during a steam-assisted gravity drainage process from a large-scale 3D physical model. With such an integrated method, good agreements between the measured and simulated data including production profiles and temperature distributions have been found, whereas the dynamics of production profiles and corresponding steam chamber propagations cannot be well reproduced solely with a commercial reservoir simulator. Sensitivity analysis has been performed to examine the effects of thermal-sensitive properties, steam chamber configuration, boundary, multiphase flow behaviour (e.g., co-current and counter-current flow), and gridblock dimension. In practice, the newly developed heat-penetration criterion can be seamlessly integrated with a commercial reservoir simulator. Such an integrated method can significantly reduce the model complexities and uncertainties, providing a simple and convenient way to dynamically and accurately characterize the steam chamber growth and propagation dynamics within a unified, consistent, and efficient framework.

Algebraic multiscale grid coarsening using unsupervised machine learning for subsurface flow simulation

Subsurface flow simulation is vital for many geoscience applications, including geoenergy extraction and gas (energy) storage. Reservoirs are often highly heterogeneous and naturally fractured. Therefore, scalable simulation strategies are crucial to enable efficient and reliable operational strategies. One of these scalable methods, which has also been recently deployed in commercial reservoir simulators, is algebraic multiscale (AMS) solvers. AMS, like all multilevel schemes, is found to be highly sensitive to the types (geometries and size) of coarse grids and local basis functions. Commercial simulators benefit from a graph-based partitioner; e.g., METIS to generate the multiscale coarse grids. METIS minimizes the amount of interfaces between coarse partitions, while keeping them of similar size which may not be the requirement to create a coarse grid. In this work, we employ a novel approach to generate the multiscale coarse grids, using unsupervised learning methods which is based on optimizing different parameter. We specifically use the Louvain algorithm and Multi-level Markov clustering. The Louvain algorithm optimizes modularity, a measure of the strength of network division while Markov clustering simulates random walks between the cells to find clusters. It is found that the AMS performance is improved when compared with the existing METIS-based partitioner on several field-scale test cases. This development has the potential to enable reservoir engineers to run ensembles of thousands of detailed models at a much faster rate.

Guided Deep Learning Manifold Linearization of Porous Media Flow Equations

Summary Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation execution. We achieve this by introducing two novel components: (1) augmention of the state-space to yield a bilinear system and (2) an autoencoder based on a deep neural network to linearize physics reservoir equations in a reduced manifold using a Koopman operator. Recognizing that reservoir simulators execute expensive Newton-Raphson iterations after each timestep to solve the nonlinearities of the physical model, we propose “lifting” the physics to a more amenable manifold where the model behaves close to a linear system, similar to the Koopman theory, thus avoiding the iteration step. We use autoencoder deep neural networks with specific loss functions and structure to transform the nonlinear equation and frame it as a bilinear system with constant matrices over time. In such a way, it forces the states (pressures and saturations) to evolve in time by simple matrix multiplications in the lifted manifold. We also adopt a “guided” training approach, which is performed in three steps: (1) We initially train the autoencoder, (2) then we use a “conventional” model order reduction (MOR) as an initializer for the final (3) full training, when we use reservoir knowledge to improve and to lead the results to physically meaningful output. Many simulation studies exhibit extremely nonlinear and multiscale behavior, which can be difficult to model and control. Koopman operators can be shown to represent any dynamical system through linear dynamics. We applied this new framework to a 2D two-phase (oil and water) reservoir subject to a waterflooding plan with three wells (one injector and two producers) with speedups around 100 times faster and accuracy in the order of 1% to 3% on the pressure and saturation predictions. It is worthwhile noting that this method is a nonintrusive data-driven method because it does not need access to the reservoir simulation internal structure; thus, it is easily applied to commercial reservoir simulators and is also extendable to other studies. In addition, an extra benefit of this framework is to enable the plethora of well-developed tools for MOR of linear systems. To the authors’ knowledge, this is the first work that uses the Koopman operator for linearizing the system with controls. As with any MOR method, this can be directly applied to a well-control optimization problem and well-placement studies with low computational cost in the prediction step and good accuracy.

Analytical solution for chemical fluid injection into linear heterogeneous porous media based on the method of characteristics

Injection of Newtonian fluids in porous media to displace linearly another Newtonian fluid is described by the Buckley-Leverett (BL) fractional flow theory derived for incompressible, constant viscosity fluids at constant injection rate in a homogeneous and isotropic reservoir. Injection of non-Newtonian fluids can be addressed by modifying the fractional flow equation by considering the dependence of injected fluid viscosity with velocity, changes of residual oil saturation and chemical adsorption on rock. In this work, the BL fractional flow model is extended to describe injection of non-Newtonian fluids into a 1D linear heterogeneous formation accounting for fluid adsorption, permeability reduction, inaccessible pore volume, and presence of a denuded water bank developed over time ahead of non-Newtonian zone. Results from derived analytical solutions demonstrate fluids saturation and pressure drop across the formation, fluids production rate and cumulative volumes to be in close agreement with the ones from a commercial reservoir simulator.

Measurement of Effective Hydrogen-Methane Gas Diffusion Coefficients in Reservoir Rocks

Summary If hydrogen is stored in depleted gas fields, the remaining hydrocarbon gas can be used as cushion gas. The composition of the backproduced gas depends on the magnitude of mixing between the hydrocarbon gas and the hydrogen injected. One important parameter that contributes to this process of mixing is molecular diffusion. Although diffusion models are incorporated in the latest commercial reservoir simulators, effective diffusion coefficients for specific rock types, pressures, temperatures, and gas compositions are not available in the literature. Thus, laboratory measurements were performed to improve storage performance predictions for an underground hydrogen storage (UHS) project in Austria. An experimental setup was developed that enables measurements of effective multicomponent gas diffusion coefficients. Gas concentrations are detected using infrared light spectroscopy, which eliminates the necessity of gas sampling. To test the accuracy of the apparatus, binary diffusion coefficients were determined using different gases and at multiple pressures and temperatures. Effective diffusion coefficients were then determined for different rock types. Experiments were performed multiple times for quality control and to test reproducibility. The measured binary diffusion coefficients without porous media show a very good agreement with the published literature data and available correlations based on the kinetic gas theory (Chapman-Enskog, Fuller-Schettler-Giddings). Measurements of effective diffusion coefficients were performed for three different rock types that represent various facies in a UHS project in Austria. A correlation between static rock properties and effective diffusion coefficients was established and used as input to improve the numerical model of the UHS. This input is crucial for the simulation of backproduced gas composition and properties which are essential parameters for storage economics. In addition, the results show the impact of pressure on effective diffusion coefficients, which impacts UHS performance.

Sequentially Coupled Thermal-Hydraulic-Mechanical Simulation for Geomechanical Assessments of Caprock Integrity in SAGD

Oil sand reservoirs and caprock undergo deformations triggered by pore pressure increases and thermal induced stresses during the steam-assisted-gravity-drainage (SAGD) processes. Geomechanical assessments are mandated by energy regulators to evaluate the caprock integrity and ensure the safe SAGD operations. Commercial reservoir simulation packages started to incorporate geomechanical effects when predicting flow response; however, these geomechanical modules are not able to correctly model the plastic deformations caused by thermal-hydraulic-mechanical (THM) interactions, which has a first order effect on predicting steam chamber propagation and evaluating caprock integrity.
 An integrated coupled THM modeling methodology is proposed here to improve the modeling of reservoir deformations and caprock integrity in a heterogeneous oil sand reservoir with interbedded shale barriers. The pressure and temperature front are found to propagate at different speed and that dominate the elastic and plastic deformations caused by changes of shear and mean effective stress. Therefore, four stages are divided in the SAGD process that can be interpretations of changes in stress paths including buildup of pore pressure, generation and dissipation of thermal induced stresses. The response surfaces of minimum factor of safety (FOS) are introduced and computed to provide a conservative estimate for caprock integrity during SAGD of a heterogeneous reservoir with multiple layers of caprocks in Athabasca oil sands. 

Enhanced history matching process by incorporation of saturation logs as model selection criteria

This paper proposes a methodology for an alternative history matching process enhanced by the incorporation of a simplified binary interpretation of reservoir saturation logs (RST) as objective function. Incorporating fluids saturation logs during the history matching phase unlocks the possibility to adjust or select models that better represent the near wellbore waterfront movement, which is particularly important for uncertainty mitigation during future well interference assessments in water driven reservoirs. For the purposes of this study, a semi-synthetic open-source reservoir model was used as base case to evaluate the proposed methodology. The reservoir model represents a water driven, highly heterogenous sandstone reservoir from Namorado field in Brazil. To effectively compare the proposed methodology against the conventional methods, a commercial reservoir simulator was used in combination with a state-of-the-art benchmarking workflow based on the Big Loop™ approach. A well-known group of binary metrics were evaluated to be used as the objective function, and the Matthew correlation coefficient (MCC) has been proved to offer the best results when using binary data from water saturation logs. History matching results obtained with the proposed methodology allowed the selection of a more reliable group of reservoir models, especially for cases with high heterogeneity. The methodology also offers additional information and understanding of sweep behaviour behind the well casing at specific production zones, thus revealing full model potential to define new wells and reservoir development opportunities.

The Uncertain Bright Future of Underground Hydrogen Storage

The most obvious obstacles to a big ramp-up in global hydrogen production are well known. They include technological breakthroughs to bring down production costs along with new sources of demand from the power and transportation sectors. Less obvious is that a small army of reservoir engineers, geologists, and other subsurface experts will be needed to understand where and how tomorrow’s hydrogen hubs will store their clean-burning fuel. Bulk storage on the surface is considered by many experts to be simply out of the question. That means large hydrogen projects will need a subsurface component, and some think depleted oil and gas fields—with an emphasis on the latter—may fit the bill. Saline aquifers are being eyed for the role too. But as this all suggests, no one has ever attempted to use these formations for hydrogen storage. Just four shallow salt formations, three salt domes in Texas and one salt field in the UK, represent the totality of the world’s hydrogen underground storage (HUS) capacity. Research is underway to expand HUS in salt formations but that will not solve for the fact that they are not a geologic option for many locations where big industrial players are hoping to produce hydrogen. This includes most of Europe and most of the US outside of its Gulf Coast states. By contrast, deeper sedimentary structures of various flavors are in no short supply but lack any material field experience that might help jumpstart the de-risking of storing several Bcf of hydrogen. The upstream industry’s extensive experience in operating what are the closest analogues—natural gas storage and carbon capture and storage (CCS)—will help that process but there are new challenges when it comes to injecting the universe’s smallest molecule into porous media. Topping the list is hydrogen’s strong propensity to migrate inside a reservoir (laterally and vertically) along with the potential for troublesome chemical and biological reactions. Hydrogen may also be clean burning but it offers only about a third of the energy density as methane, which means it needs roughly three times the storage volume to deliver the same energy output to a gas-fired power plant. Among those working to bring clarity to such issues is Mojdeh Delshad, a reservoir engineer and professor at The University of Texas at Austin. Her latest research involved using commercial reservoir simulators to model what would happen if selected gas fields and saline aquifers in the US used for CCS or natural gas storage were instead used to store hydrogen. “We wanted to know about the challenges of hydrogen, which because of its properties—very low density, very low viscosity—is going to move in the reservoir much more quickly than CO2 and methane. And that’s exactly what we found, which means we’re going to have to do something differently with hydrogen storage in order to capture and produce what is injected,” said Delshad.

Carbon Dioxide Sequestration in Low Porosity and Permeability Deep Saline Aquifer: Numerical Simulation Method

Abstract The saline aquifer is the most reliable place where anthropogenic carbon dioxide gas storage has shown a promising future. This paper evaluates and predicts the capacities of different carbon dioxide storage trapping mechanisms in storing carbon dioxide gas in low porosity and permeability deep saline aquifers by using commercial reservoir simulator software i.e., Computer modeling group (CMG). Four carbon dioxide storage trapping modeled and simulated were structural or stratigraphic trapping mechanisms, residual trapping mechanisms, solubility trapping mechanisms, and mineral trapping mechanisms. Carbon dioxide gas was injected into a deep saline aquifer for 15 years, followed by 833 years of post-injection. To reflect the real field reality and have a reasonable approximation of the amount of carbon dioxide which can be stored in an aquifer, this paper included water vaporization effects that occur during carbon dioxide injection and water injection operations so as to optimize residual and solubility trapping mechanisms as the most important trapping mechanisms. Furthermore, the effects of different important parameters such as salinity, vertical-to-horizontal permeability ratio, injection rate, bottom hole pressure, and temperature on each carbon dioxide trapping mechanism were analyzed. Results revealed that each carbon dioxide trapping mechanism has a different capacity for storing carbon dioxide and could be either affected linearly or nonlinearly with various parameters. Higher aquifer temperatures are not recommended for carbon dioxide storage because most of the carbon dioxide gas is stored as free gas, which increases the risk of leakage in case of mechanical failure or imbalance. Excess salinity is the only factor that reduces aquifer storage capacity. Furthermore, it was found that an aquifer with a lower vertical-to-horizontal permeability ratio is recommended for carbon dioxide storage because it increases carbon dioxide stored in an immobile phase, which avoids risk leakages. There was an increase of 43.2% and a decrease of 16.84% for minimum and maximum vertical-to-horizontal permeability (kv/kh) ratios, respectively, compared to the base for residual trapping mechanisms. Also, there was a decrease of carbon dioxide dissolved by 19% at maximum kv/kh ratios and an increase of 58% at minimum kv/kh ratios, compared to the base case. Further, there was an increase of carbon dioxide trapped by 96.4% and dissolved by 97% when water was injected at a higher rate compared to the base case (no water injection). Thus, a high injection rate is suggested to enhance residual and solubility trapping mechanisms. It is recommended that the carbon dioxide injection rate and bottom hole pressure be kept at optimal levels to avoid mechanical failure due to aquifer pressures building up, which can increase the risk of leakages and must be monitored and controlled at the surface using pressure gauges or sensor technology.

Temperature-Transient Analysis for Matrix Linear Flow Toward Infinite-Conductivity Hydraulically Fractured Wells

SummaryThis work presents novel analytical temperature-transient solutions for matrix linear flow toward an infinite-conductivity hydraulically fractured well producing under specified constant rate (CR) or constant bottomhole pressure (CBHP) production. The solutions apply to the single-phase flow of a slightly compressible fluid with constant viscosity (e.g., undersaturated oil reservoirs with irreducible water saturation or liquid-dominated geothermal reservoirs). They include the effects of conduction, convection, the Joule-Thomson (J-T) expansion of fluids and adiabatic expansion of the total rock and fluid system, and fluid loss fracture damage and assume constant rock and fluid physical and thermophysical properties with pressure and temperature. They are obtained by using Laplace (for CR) and Boltzmann (for CBHP) transformations. To validate the analytical solutions, an in-house numerical solution is also developed. It solves the mass and thermal energy balance equations coupled simultaneously and accounts for the variation of rock and fluid properties with pressure and temperature. The in-house simulator was validated by using a commercial reservoir simulator. Results indicate that the fracture surface temperature is decreasing with a square root of time for CR production but is constant for CBHP production. The temperature responses for both modes of production are controlled by the adiabatic expansion of the rock and fluid properties and the thermal diffusivity of the rock. The effect of thermal conductivity plays a significant role in both production modes as the matrix permeability decreases. The fracture damage has different signatures on temperature transients depending on the mode of production. The approximate analytical solutions show the information content of temperature-transient data acquired from an infinitely conductive hydraulically fractured well under matrix linear flow. They are simple and can be used to perform matrix linear-flow analysis jointly with pressure and rate transient data to estimate the physical and thermophysical properties of the rock and fluids. We also propose a simple correction to fluid viscosity variation as input for the analytic solutions to accurately estimate the physical and thermophysical parameters in case drawdowns are significant.

An improved multistage preconditioner on GPUs for compositional reservoir simulation

The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on time-step sizes is commonly used in mainstream commercial reservoir simulators. In this paper, we develop an efficient multistage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multicolor Gauss–Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yielding the same convergence behavior as the corresponding sequential version.

Investigation of impact of low salinity water flooding on oil recovery for sandstone reservoirs – a simulation approach

Low salinity Waterflooding (LSWF) is a proven EOR technique which has emerged in the last few decades. In this, the salinity of the injected water is reduced. Studies have unveiled that the LSWF can improve about 5–38% of oil recovery. This work aims to study the effectiveness of LSWF by modeling Sea waterflooding (SWF) and LSWF using a commercial reservoir simulator and justifying the proposed mechanisms responsible for the incremental oil recovery due to LSWF. A critical parametric study has been undertaken, which led us to conclude that LSWF is a cost-effective, and environment-friendly method that results in additional oil recovery for sandstone reservoirs.

Experimental tests and EDFM method to study gas injection in a fractured granite reservoir

The development of granite reservoirs with high dip fractures has many difficulties, such as a high decline rate, early water breakthrough, and numerous economic losses. Gas injection is usually used to maintain the formation pressure to increase single well productivity, and could be carried out in fractured reservoirs to enhance oil recovery. When injecting associated gas, it meets the environmental protection requirements of the local government to further eliminate the flare, implementing the concept of green and low-carbon development. In this study, both laboratory tests and reservoir simulation have been done to study the feasibility and the benefit of associated gas injection in the research target. For physical stimulation, it mainly includes experiments such as associated gas injection expansion, slim tube, long core displacement, and relative permeability. Through these experiments, the changes in the recovery factor after depletion development and gas displacement are systematically described and the key controls are revealed for improving the recovery ratio of fractured basement reservoirs. For the simulation part, the embedded discrete fracture model processor combining commercial reservoir simulators is fully integrated into the research. A 3D model with complex natural fractures is built to perform the associated gas injection performance of the fractured granite reservoir. Complex dynamic behaviors of natural fractures can be captured, which can maintain the accuracy of DFNs and keep the efficiency offered by structured gridding. Depletion development and gas injection development strategy are optimized in this research. The result shows that oil recovery by using gas injection is increased by 16.8% compared with depletion development by natural energy.

History-Matching and Forecasting Production Rate and Bottomhole Pressure Data Using an Enhanced Physics-Based Data-Driven Simulator

Summary In this study, we present a novel application of our newly developed physics-based data-driven interwell numerical simulator (INSIM) referred to as INSIM-BHP to history match highly variable real-life (oscillatory) oil rate and bottomhole pressure (BHP) data acquired daily in multiperforated wells produced from an oil reservoir with bottomwater drive mechanism. INSIM-BHP provides rapid and accurate computation of well rates and BHPs for history matching, forecasting, and production optimization purposes. It delivers precise BHP calculations under the influence of a limited aquifer drive mechanism. Our new version represents the physics of two-phase oil-water flow more authentically by incorporating a harmonic-mean transmissibility computation protocol and including an arithmetic-mean gravity term in the pressure equation. As the specific data set considered in this study contains a sequence of highly variable oil rate and BHP data, the data density requires INSIM-BHP to take smaller than usual timesteps and places a strain on the ensemble-smoother multiple data assimilation (ES-MDA) history-matching algorithm, which utilizes INSIM-BHP as the forward model. Another new feature of our simulator is the use of time-variant well indices and skin factors within the simulator’s well model to account for the effects of well events on reservoir responses such as scaling, sand production, and matrix acidizing. Another novel modification has been made to the wellhead term calculation to better mimic the physics of flow in the wellbore when the production rate is low, or the well(s) is(are) shut in. We compare the accuracy of the history-matched oil rate and BHP data and forecasted results as well as computational efficiency for history matching and future prediction by INSIM-BHP with those from a high-fidelity commercial reservoir simulator. Results show that INSIM-BHP yields accurate forecasting of wells' oil rates and BHPs on a daily level even under the influence of oscillatory rate schedules and changing operational conditions reflected as skin effects at the wells. Besides, it can help diagnose abnormal BHP measurements within simulation runs. Computational costs incurred by INSIM-BHP and a high-fidelity commercial simulator are evaluated for the real data set investigated in this paper. It has been observed that our physics-based, data-driven simulator is about two orders of magnitude faster than a conventional high-fidelity reservoir simulator for a single forward simulation. The specific field application results demonstrate that INSIM-BHP has great potential to be a rapid approximate capability for history matching and forecasting workflow in the investigated limited-volume aquifer-driven development.

The numerical simulation study on the dynamic variation of residual oil with water drive velocity in water flooding reservoir

The results of core displacement experiments show that increasing the water drive velocity when it is bigger than the limit value can effectively reduce the residual oil saturation and improve the oil displacement efficiency under the same PV. However, the existing commercial simulators (Eclipse, CMG et al.) cannot simulate the effect of water velocity on the relative permeability curve in the process of numerical simulation.In this article, capillary number (Ca), defined as the dimensionless ratio of viscous force to capillary force, is used to characterize the relationship between water drive velocity and residual oil. Second, a new Boltzmann (BG) equation is proposed to match the nonlinear relationship between Ca and residual oil. The BG equation is a continuous function, which is very beneficial to the stability of numerical calculation. Finally, a new reservoir numerical simulator is established which captures the dynamic variation of residual oil saturation with water drive velocity in a water flooding reservoir based on the black oil model. The new simulator was verified by comparing it with the commercial reservoir simulator ECLIPSE and experimental data. The simulation results show that compared with the common model, the model considering the dynamic variation of residual oil saturation with water drive velocity reduced the residual oil saturation near the main flow line after enhanced injection rate. The oil phase flow capacity in the model is enhanced, the water cut is decreased, and the oil recovery rate is higher. The history matching of the S oilfield in Bohai Bay is achieved with the new simulator, and the history matching accuracy is obviously higher than that of Eclipse. The findings of this study can help with a better understanding of the distribution law and flow law of remaining oil in the high water cut stage of the reservoir and have good theory and application value for water flooding offshore oilfields.

Study Evaluates Surfactant EOR From Tight Carbonates

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209418, “Surfactant Enhanced Oil Recovery From Tight Carbonates: Core-Scale Experiments to Reservoir-Scale Modeling,” by Yue Shi, SPE, and Kishore Mohanty, SPE, The University of Texas at Austin. The paper has not been peer reviewed. _ Most carbonate reservoirs are oil-wet/mixed-wet and heterogeneous at multiple scales. The majority of the injected water flows through the high-permeability regions and fractures and bypasses the oil in the matrix because of high negative capillary pressure (Pc). To enhance oil recovery from such reservoirs, the sign of the Pc should be changed by wettability alteration (WA) or the Pc should be reduced by lowering interfacial tension (IFT). In the complete paper, surfactants that can either alter wettability or develop ultralow IFT were identified through laboratory measurements for the target carbonate reservoir. Introduction Contact-angle, IFT, phase-behavior, and imbibition tests were first performed, and surfactants that can either alter wettability or develop ultralow IFT were identified. Then, a laboratory-scale imbibition model was built with commercial reservoir simulation software. A sensitivity study was performed to evaluate the effects of residual oil saturation (Sor), alteration of Pc and relative permeability (Kr), IFT, and matrix scale on oil recovery by surfactant. Next, the laboratory-scale imbibition model was used to history match the experimental data and parameters were tuned for reservoir-scale simulation. Finally, a 3D layered field-scale model was developed to study the performance of the selected surfactants in injection/soak/production (ISP) tests. Materials Eight anionic surfactants (An 1 through An 8) and two quaternary ammonium cationic surfactants (Cat 1 and 2) were used. Silurian dolomite (SD) outcrop cores were used in this study. Fluid samples were obtained from a West Texas dolomite-rich reservoir. The oil has a density of 0.84 g/mL at 25°C and a viscosity of 7.46 cp at reservoir temperature (35°C). It has an acid number of 0.25 mg KOH/g and a base number of 1.0 mg KOH/g. The reservoir has been waterflooded for years, and total dissolved solids of brine has decreased from 112,668 to 40,393 ppm for recent produced water (PW). The authors detail the methodology for aqueous-stability, contact-angle-measurement, IFT-measurement, phase-behavior, and spontaneous-imbibition tests in the complete paper.