Compositional Reservoir Simulation Research Articles

History Matching Heterogeneous Coreflood of CO2/Brine by Use of Compositional Reservoir Simulator and Geostatistical Approach

Summary Numerical modeling and simulation are essential tools for developing a better understanding of the geologic characteristics of aquifers and providing technical support for future carbon dioxide (CO2) storage projects. Modeling CO2 sequestration in underground aquifers requires the implementation of models of multiphase flow and CO2 and brine phase behavior. Capillary pressure and relative permeability need to be consistent with permeability/porosity variations of the rock. It is, therefore, crucial to gain confidence in the numerical models by validating the models and results by use of laboratory and field pilot results. A published CO2/brine laboratory coreflood was selected for our simulation study. The experimental results include subcore porosity and CO2-saturation distributions by means of a computed tomography (CT) scanner along with a CO2-saturation histogram. Data used in this paper are all based on those provided by Krause et al. (2011), with the exception of the CT porosity data. We generated a heterogeneous distribution for the porosity but honoring the mean value provided by Krause et al. (2011). We also generated the permeability distribution with the mean value for the whole core given by Krause et al. (2011). All the other data, such as the core dimensions, injection rate, outlet pressure, temperature, relative permeability, and capillary pressure, are the same as those in Krause et al. (2011). High-resolution coreflood simulations of brine displacement with supercritical CO2 are presented with the compositional reservoir simulator IPARS (Wheeler and Wheeler 1990). A 3D synthetic core model was constructed with permeability and porosity distributions generated by use of the geostatistical software FFTSIM [Jennings et al. (2000)], with cell sizes of 1.27×1.27×6.35 mm. The core was initially saturated with brine. Fluid properties were calibrated with the equation-of-state (EOS) compositional model to match the measured data provided by Krause et al. (2011). We used their measured capillary pressure and relative permeability curves. However, we scaled capillary pressure on the basis of the Leverett J-function (Leverett 1941) for permeability, porosity, and interfacial tension (IFT) in every simulation grid cell. Saturation images provide insight into the role of heterogeneity of CO2 distribution in which a slight variation in porosity gives rise to large variations in CO2-saturation distribution in the core. High-resolution numerical results indicated that accurate representation of capillary pressure at small scales was critical. Residual brine saturation and the subsequent shift in the relative permeability curves showed a significant impact on final CO2 distribution in the core.

A Compositional Reservoir Simulation and Experimental Investigation of Asphaltene Onset Pressure

Asphaltene precipitation is one of the most common problems in many reservoirs and may lead to many safeties operational issues which affects on oil recovery; therefore, identifying start of asphaltene activation is known as a key parameter to control production efficiency. This study includes predicting onset pressure with Multiphase Flash test and compare its result with experimental data generated by Asphaltene Static Apparatus. Safety pressure was obtained by performing Multiphase Flash test for each component. In order to prevent adsorption, mechanical entrapment, and blockage, reservoir pressure must be higher than this safety pressure. SARA test is widely used to identify the fraction of crude oil that affect the asphaltene stability. IP143 standard test was used to measure precipitated asphaltene. Natural depletion test was designed at four steps, including 4400, 3000, 1550, and 1020 Psia and reservoir temperature is 205°F. It was seen that with decreasing pressure from reservoir pressure to saturation pressure asphaltene precipitation from PVT cell was increased and at pressures below saturation pressure with pressure reduction, asphaltene precipitations was decreased. Also it was concluded that above saturation pressure solubility model is dominant and below saturation pressure colloidal model is dominant. The results of IP143 show that initial content of asphaltene are 12.8%. SARA test result shows this kind of fluid located at unstable asphaltene precipitation region. Comparison of safety pressure between Multiphase Flash test and experimental data are investigated and discussed. Onset pressure of 18000 Pisa was obtained from Multiphase Flash test, which is in good agreement with experimental result.

Effect of Reservoir Heterogeneity on Primary Recovery and CO2 Huff ‘n’ Puff Recovery in Shale-Oil Reservoirs

Summary An equation-of-state (EOS) -based compositional reservoir simulator, UT-COMP, is used to simulate both primary recovery and carbon dioxide (CO2) huff ‘n’ puff recovery in a shale matrix typical of the Bakken formation, to investigate the effect of reservoir heterogeneity on hydrocarbon recovery. Nonaqueous components are carefully lumped into seven pseudocomponents. Permeability fields with various heterogeneity and correlation lengths are generated. UT-COMP is able to solve the compositional model, despite the permeability difference between the fracture and matrix being six orders of magnitude. The effects of both primary recovery and CO2 huff ‘n’ puff recovery depend significantly on reservoir heterogeneity. In primary recovery, the recovery factor can be fit by a two-parameter exponential formula; higher heterogeneity reduces the rate coefficient in the formula. Permeability fields with identical or similar heterogeneity have similar rate coefficients, even if the correlation lengths are different, which implies that the recovery depends primarily on heterogeneity and is insensitive to correlation length. Multiple-cycle CO2 huff ‘n’ puff processes are simulated in both homogeneous and heterogeneous reservoirs. Recovery rate in the production stage rises to a peak value much higher than that in the primary recovery, and then declines dramatically. The peak recovery rate decreases with increasing huff ‘n’ puff cycles, resulting from depleted reservoir pressure and hydrocarbons. The final recovery factor in the huff ‘n’ puff recovery is lower than that in the primary recovery, because the incremental recovery in the production stage is unable to compensate the loss in the injection and shut-in stages. Use of a longer shut-in time does not help increase the recovery rate in the production stage, because CO2 migration into the shale matrix is very limited because of the low matrix permeability. Reservoir heterogeneity leads to a faster decline of recovery rate in the production stage.

Mixed precision parallel preconditioner and linear solver for compositional reservoir simulation

Reservoir simulation role in value creation and strategic management decisions cannot be over emphasized. Simulation of complex challenging reservoirs with millions of grid blocks especially in compositional mode is very time-consuming even with fast modern computers. On the other hand, high price of cluster supercomputers prevents them for being commonly used for fast simulation of such reservoirs. In recent years, the development of many-core processors like cell processors, DSPs, and graphical processing units (GPUs) has provided a very cost-effective hardware platform for fast computational operations. However, programming for such processors is much more difficult than conventional CPUs, and new parallel algorithm design and special parallel implementation methods are needed. Using the computational power of CPUs, GPUs, and/or any other processing unit, Open Computing Language (OpenCL) provides a framework for programming for heterogeneous platforms. In this paper, OpenCL is used to employ the computational power of a GPU to build a preconditioner and solve the linear system arising from compositional formulation of multiphase flow in porous media. The proposed parallel preconditioner is proved to be quite effective, even in heterogeneous porous media. Using data-parallel modules on GPU, the preconditioner/solver runtime reduced at least 1 order of magnitude compared to their serial implementation on CPU.

An integrated approach for rapid phase behavior calculations in compositional modeling

Abstract The solution of the hydrocarbon phase equilibria problem through the use of an Equation of State (EoS) requires substantial computational power due to the complexity and iterative nature of the calculations particularly when the reservoir fluid needs to be described with a significant number of components. This high CPU time demand becomes particularly acute during the execution of compositional reservoir simulations as the phase equilibria problem needs to be solved repeatedly for each discretization block and at each iteration of the non-linear solver. Solving the hydrocarbon phase equilibrium problem is often reported to consume more than 50% of the simulation׳s total CPU time, thus making the speeding up of these calculations a challenge of great interest. In this work, an integrated approach is presented which utilizes classification and regression models to provide direct answers to both the phase stability and phase split problems during compositional modeling. The models are generated offline, in an automated way, once the selected EoS model is tuned and finalized and prior to setting up and running the reservoir or process simulator. First, classifiers are trained, as previously presented in the literature, utilizing the fluid׳s tuned EoS model, which label the fluid׳s state as stable or unstable for given feed composition, pressure and temperature. Second, regression models are generated so as to provide the prevailing reduced variables values, thus emulating the unknown closed-form solution of the Equation-of-State based reduced flash formulation. An unsupervised training technique is proposed to optimize the usage of the regression model׳s learning capacity. Once the models are generated an unlimited number of compositional modeling calculations can be made during which the models developed beforehand are simply called to determine directly both the stability state of the feed and the phase split reduced variables values, if the mixture is found unstable, which, in turn, will lead to the equilibrium phases properties and molar ratios. By providing the reduced variables values instead of the full equilibrium coefficients set, the regression model expression is kept simple, thus very fast to evaluate compared to the conventional iterative algorithms. The method is applicable to any type of simulation models requiring a great number of phase behavior calculations. The proposed approach is presented in two-phase equilibria formulation although it can be directly extended to multi-phase equilibria applications. Examples demonstrate the accuracy of the calculations and the very significant performance improvement achieved with respect to currently used methods.

New Modification on Production Data of Gas Condensate Reservoirs for Rate Transient Analysis

Techniques of production data analysis for single-phase oil and gas reservoirs have advanced significantly over the past few years. These techniques range from traditional (i.e., Arps, 1945; Fetkovich, 1980) to modern methods that account for the variation of operating conditions at the wellbore. However, the application of these later methods to gas condensate reservoirs is a challenge. The authors aimed to extend the applicability of modern production data analysis (single-phase flow) to analyze the production data of a gas condensate reservoir (two-phase flow). A single-phase production model consists of (a) a material balance equation, (b) the solution of diffusivity equation for gas flow subjected to constant rate and constant pressure boundary conditions, and (c) the presentation of a material-balance-time function. Using a compositional reservoir simulator, long-term production data have been generated over a wide range of the gas condensate reservoir parameters. The simulation result is compared with the prediction from the single-phase gas reservoir model, which uses a modern production analysis method. The error level is evaluated and a correlation for treatment of the single-phase analysis method is developed. Finally the results show that the methodology developed can be successfully applied for analysis of production data of a gas condensate reservoir.

Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow

An adjoint formulation for the gradient-based optimization of oil–gas compositional reservoir simulation problems is presented. The method is implemented within an automatic differentiation-based compositional flow simulator (Stanford’s Automatic Differentiation-based General Purpose Research Simulator, AD-GPRS). The development of adjoint procedures for general compositional problems is much more challenging than for oil–water problems due to the increased complexity of the code and the underlying physics. The treatment of nonlinear constraints, an example of which is a maximum gas rate specification in injection or production wells, when the control variables are well bottom-hole pressures, poses a particular challenge. Two approaches for handling these constraints are presented—a formal treatment within the optimizer and a simpler heuristic treatment in the forward model. The relationship between discrete and continuous adjoint formulations is also elucidated. Results for four example cases of increasing complexity are presented. Improvements in the objective function (cumulative oil produced) relative to reference solutions range from 4.2 to 11.6 %. The heuristic treatment of nonlinear constraints is shown to offer a cost-effective means for obtaining feasible solutions, which are, in some cases, better than those obtained using the formal constraint handling procedure.

Fully compositional and thermal reservoir simulation

Fully compositional and thermal reservoir simulation capabilities are important in oil exploration and production. There are significant resources in existing wells and in heavy oil, oil sands, and deep-water reservoirs. This article has two main goals: (1) to clearly identify chemical engineering sub-problems within reservoir simulation that the PSE community can potentially make contributions to and (2) to describe a new computational framework for fully compositional and thermal reservoir simulation based on a combination of the Automatic Differentiation-General Purpose Research Simulator (AD-GPRS) and the multiphase equilibrium flash library (GFLASH). Numerical results for several chemical engineering sub-problems and reservoir simulations for two EOR applications are presented. Reservoir simulation results clearly show that the Solvent Thermal Resources Innovation Process (STRIP) outperforms conventional steam injection using two important metrics – sweep efficiency and oil recovery.

Multiparadigm Parallel Acceleration for Reservoir Simulation

Summary With the advent of the multicore central-processing unit (CPU), today's commodity PC clusters are effectively a collection of interconnected parallel computers, each with multiple multicore CPUs and large shared random access memory (RAM), connected together by means of high-speed networks. Each computer, referred to as a compute node, is a powerful parallel computer on its own. Each compute node can be equipped further with acceleration devices such as the general-purpose graphical processing unit (GPGPU) to further speed up computational-intensive portions of the simulator. Reservoir-simulation methods that can exploit this heterogeneous hardware system can be used to solve very-large-scale reservoir-simulation models and run significantly faster than conventional simulators. Because typical PC clusters are essentially distributed share-memory computers, this suggests that the use of the mixed-paradigm parallelism (distributed-shared memory), such as message-passing interface and open multiprocessing (MPI-OMP), should work well for computational efficiency and memory use. In this work, we compare and contrast the single-paradigm programming models, MPI or OMP, with the mixed paradigm, MPI-OMP, programming model for a class of solver method that is suited for the different modes of parallelism. The results showed that the distributed memory (MPI-only) model has superior multicompute-node scalability, whereas the shared memory (OMP-only) model has superior parallel performance on a single compute node. The mixed MPI-OMP model and OMP-only model are more memory-efficient for the multicore architecture than the MPI-only model because they require less or no halo-cell storage for the subdomains. To exploit the fine-grain shared memory parallelism available on the GPGPU architecture, algorithms should be suited to the single-instruction multiple-data (SIMD) parallelism, and any recursive operations are serialized. In addition, solver methods and data store need to be reworked to coalesce memory access and to avoid shared memory-bank conflicts. Wherever possible, the cost of data transfer through the peripheral component interconnect express (PCIe) bus between the CPU and GPGPU needs to be hidden by means of asynchronous communication. We applied multiparadigm parallelism to accelerate compositional reservoir simulation on a GPGPU-equipped PC cluster. On a dual-CPU-dual-GPGPU compute node, the parallelized solver running on the dual-GPGPU Fermi M2090Q achieved up to 19 times speedup over the serial CPU (1-core) results and up to 3.7 times speedup over the parallel dual-CPU X5675 results in a mixed MPI + OMP paradigm for a 1.728-million-cell compositional model. Parallel performance shows a strong dependency on the subdomain sizes. Parallel CPU solve has a higher performance for smaller domain partitions, whereas GPGPU solve requires large partitions for each chip for good parallel performance. This is related to improved cache efficiency on the CPU for small subdomains and the loading requirement for massive parallelism on the GPGPU. Therefore, for a given model, the multinode parallel performance decreases for the GPGPU relative to the CPU as the model is further subdivided into smaller subdomains to be solved on more compute nodes. To illustrate this, a modified SPE5 (Killough and Kossack 1987) model with various grid dimensions was run to generate comparative results. Parallel performances for three field compositional models of various sizes and dimensions are included to further elucidate and contrast CPU-GPGPU single-node and multiple-node performances. A PC cluster with the Tesla M2070Q GPGPU and the 6-core Xeon X5675 Westmere was used to produce the majority of the reported results. Another PC cluster with the Tesla M2090Q GPGPU was available for some cases, and the results are reported for the modified SPE5 (Killough and Kossack 1987) problems for comparison.

New correlation to predict CO<SUB align="right">2 gas saturation and gas front velocity

Compositional reservoir simulation sensitivity runs were performed to develop correlations that can be used to predict CO2 gas saturation and gas front velocity in oil reservoirs. These correlations are accurate (with average error of less than 5%) and are easy to use. The correlations can also be applied to a diverse range of reservoir types with different fluid properties, producing under different flow regimes. Eclipse 300 was used to build the simulation models. The models were later tuned to investigate different CO2 and flue gas injection scenarios. Parameters like reservoir shape, injected gas and original fluid in place properties, injection and production rates and pressures among others have been used to develop these correlations. More than 400 different simulation sensitivity runs have been performed. This study showed that the gas saturation in different locations of a reservoir can be predicted accurately. Also, it has been observed that every point of the gas front is moving at a constant velocity and was time independent. Velocity, however, was variable along the gas front.

Tie-Simplex-Based Phase-Behavior Modeling in an IMPEC Reservoir Simulator

Summary Recently, tie-simplex-based phase-behavior modeling in reservoir simulators has been applied and investigated as a potential method for improving the computational speed of equation-of-state (EOS) -based reservoir simulators. We implemented compositional-space adaptive tabulation (CSAT), the most promising tie-simplex-based method, in UTCOMP, the University of Texas' in-house IMPEC compositional reservoir simulator, to investigate its computational efficiency compared with the phase-behavior algorithm in UTCOMP. The results show that applying CSAT only to skip stability analysis does improve computational time, but only when a significant portion of the gridblocks are in the single-phase region and no other technique for avoiding stability analysis is used. However, in most cases, there is little or no computational advantage to use of CSAT when the simple option in UTCOMP is used where stability analysis is skipped for blocks surrounded by single-phase regions. We explore in detail the performance of CSAT, which depends significantly on the specific gas flood modeled, and the number of tie-lines generated during adaptive tabulation. The results shed light on applicability of CSAT in the IMPEC-type compositional reservoir simulators and show that the advantages of CSAT in this type of simulator are not as great as are reported in the literature for fully implicit or adaptive implicit formulations.

Investigating Improved Oil Recovery in Heavy Oil Reservoirs

Primary production mechanisms do not recover an appreciable fraction of the hydrocarbon initially in place (HIIP). Practical knowledge has shown that, at the point when the natural energy in a heavy oil reservoir is nearly or altogether depleted, the recovery factor does not exceed about 20%. Some heavy oil reservoirs do not produce at all by natural drive mechanisms. This often necessitates adopting a production improvement strategy to augment recovery. Prior to implementing an improved oil recovery method (either secondary or tertiary) in the field, it is very important to investigate its potential for success. Reservoir simulation is a part of a continuous learning process used to gain insight into the feasibility and applicability of improved oil recovery methods. In this project, GEM compositional reservoir simulator has been used to study the efficiencies of different improved oil recovery strategies, ranging from waterflooding to solvent injection. The drainage volume investigated is a hypothetical box-shaped heavy oil reservoir composed of three distinct permeable layers.

Investigation of Several Interpolation Functions for Unstructured Meshes in Conjunction with Compositional Reservoir Simulation

One of the key parameters in numerical simulation of fluid flow problems is the interpolation function used for adjusting the numerical value from the position where it is evaluated to the interface of the control volumes; for instance, if the finite-volume method is employed. In this article, we present an investigation of several interpolation functions in conjunction with the element based finite-volume method (EbFVM) using unstructured triangular meshes. We investigate the mass weighted upwind (MWU) and a modified version of this method, the upwind scheme, a streamline based upwind scheme, and a limited second order upwind scheme. These interpolation functions are implemented in a compositional simulator using vertex cell unstructured grids. The accuracy of these interpolation functions are evaluated for several case studies. For some of them, we also compare the results with the available analytical solutions.

Integrated-Asset-Modeling Approach for Reservoir Management on North Slope

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 158497, ’Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope,’ by R.D. Roadifer, SPE, ConocoPhillips Alaska; R. Sauve, Schlumberger; R. Torrens, SPE, Schlumberger Middle East; H.W. Mead, SPE, N.P. Pysz, SPE, and D.O. Uldrich, SPE, ConocoPhillips Alaska; and T. Eiben, ConocoPhillips Canada, prepared for the 2012 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8-10 October. The paper has not been peer reviewed. An integrated-asset-model (IAM) approach has been implemented for the Alpine field and eight associated satellite fields on the western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir- and facilities-management options under existing and future operating constraints. The technology used for managing the fields consists of fullfield compositional reservoir-simulation models for each reservoir integrated with a pipeline-surface-network model and a process facility model. Developing an IAM Reservoir-Management Needs. As with the construction of any reservoir or surface model, the first step in developing an IAM is to define with as much clarity as possible the objectives in undertaking such an endeavor. A detailed list of all possible objectives for creating an IAM covering all possible development and operational situations that might occur would be quite extensive. As is often the case, however, simply identifying the “big rocks” will allow the finer details of those leveraging aspects to be identified and planned for in the development of the IAM. The Alpine field anchors the westernmost oil-production and -processing facility on Alaska’s North Slope (Fig. 1). Discovered in 1994, the Alpine field is in the Colville River delta, 6 miles south of the Arctic Ocean and approximately 70 miles west of the Trans-Alaska Pipeline. The Alpine field began production in November 2000 and continues development today. Subsequently, satellite fields, including the Fiord-Nechelik, Fiord-Kuparuk, Nanuq-Kuparuk, Nanuq-Nanuq, Qannik, and Alpine-Kuparuk, have been brought on line and continue to be developed. Additionally, several fields in the National Petroleum Reserve have the potential to be developed. The common theme across all these developments is that they are or will be produced through the Alpine Central Facility (ACF). The ACF is a single-train processing facility. The only significant fluid that leaves the ACF is the sales oil. All other gas not used for fuel or lift must either be blended for injection as miscible injectant (enriched lean-gas injectant) or be injected as lean gas into two black-start gas-injection wells. All produced water must be reinjected and, for pipeline-integrity reasons, must be segregated from imported makeup seawater used for injection.

A 3D compositional miscible gas flooding simulator with dispersion using Element-based Finite-Volume method

Physical dispersion is one of the most important key parameters in compositional reservoir simulation. It is a phenomenon of mixing with mass transfer occurring when gradients in composition arise or exist. This process tends to homogenize the composition of the phases. When the dispersion term is included in the material balance equations, we obtain a full tensor structure. However, most of the reservoir simulators neglect such an important physical term. In this work, we investigate the governing partial differential equations for modeling miscible flooding by adding dispersion to the material balance equations. The equations are solved by the Element-based Finite-Volume method (EbFVM) in conjunction with unstructured meshes. Results of several compositional reservoir simulation case studies are presented to demonstrate the application of the method.

An Integrated Model for History Matching and Predicting Reservoir Performance of Gas/Condensate Wells

Summary The depletion performance of gas/condensate reservoirs is highly influenced by changes in fluid composition below the dewpoint. The long-term prediction of condensate/gas reservoir behavior is therefore difficult because of the complexity of both composition variation and two-phase-flow effects. In this paper, an integrated model was developed to simulate gas-condensate reservoir/well behavior. The model couples the compositional material balance or the generalized material-balance equations for reservoir behavior, the two-phase pseudo integral pressure for near-wellbore behavior, and outflow correlations for wellbore behavior. An optimization algorithm was also used with the integrated model so it can be used in history-matching mode to estimate original gas in place (OGIP), original oil in place (OOIP), and productivity-index (PI) parameters for gas/condensate wells. The model also can be used to predict the production performance for variable tubinghead pressure (THP) and variable production rate. The model runs fast and requires minimal input. The developed model was validated by use of different simulation cases generated with a commercial compositional reservoir simulator for a variety of reservoir and well conditions. The results show a good agreement between the simulation cases and the integrated model. After validating the integrated model against the simulated cases, the model was used to analyze production data for a rich-gas/condensate field (initial condensate/gas ratio of 180 bbl/ MMscf). THP data for four wells were used along with basic reservoir and production data to obtain original fluids in place and PIs of the wells. The estimated parameters were then used to forecast the gas and condensate production above and below the dewpoint. The model is also capable of predicting reservoir pressure, bottomhole flowing pressure, and THP and can account for completion changes when they occur.

Robust and efficient Trust-Region based stability analysis and multiphase flash calculations

Multiphase flash calculations and phase stability analysis are central in compositional reservoir and chemical process simulators. For instance, in some simulations, a huge amount of phase equilibrium calculations is required (the most important part of the computational effort). Moreover, a single failure may cause significant error propagations leading to false solutions. Thus, it is imperative that calculation algorithms are efficient and highly robust. The most difficult regions in mixture phase envelopes are in the vicinity of singularities: critical points for flash calculations and the stability test limit locus for stability analysis. For these conditions, all algorithms have difficulties to converge. Traditionally, a number of successive substitution iterations (SSI) are performed before switching to the second-order Newton method (many SSI iteration may be required before switch very close to singularities). The Trust-Region method has the advantage of performing a Newton step whenever the Hessian is definite positive; otherwise, the Trust-Region corrects the Hessian matrix by adding a diagonal element to make it positive definite, thus a descent direction is guaranteed. The Trust-Region limits the solution within a trust-radius, which is updated automatically at each iteration level, depending on the quality of the quadratic approximation. If the function is convex, the trust-radius enables larger changes in iteration variables, otherwise restricted steps are used to ensure a progress towards the solution. The proposed Trust-Region algorithm, as well as a hybrid methodology that combines SSI, Newton and Trust-Region steps, are tested for multiphase flash calculations and stability analysis on a variety of mixtures involving hydrocarbon components, carbon dioxide and hydrogen sulfide, exhibiting complicated phase envelopes. The proposed method compares favorably to the widely used SSI-Newton methods with various independent variables. The more difficult a test point is, the more spectacular the algorithm acts from both efficiency and reliability perspectives.

Development of an Efficient Embedded Discrete Fracture Model for 3D Compositional Reservoir Simulation in Fractured Reservoirs

Summary Many naturally fractured reservoirs around the world have depleted significantly, and improved-oil-recovery (IOR) processes are necessary for further development. Hence, the modeling of fractured reservoirs has received increased attention recently. Accurate modeling and simulation of naturally fractured reservoirs (NFRs) is still challenging because of permeability anisotropies and contrasts. Nonphysical abstractions inherent in conventional dual-porosity and dual-permeability models make them inadequate for solving different fluid-flow problems in fractured reservoirs. Also, recent technologies for discrete fracture modeling may suffer from large simulation run times, and the industry has not used such approaches widely, even though they give more-accurate representations of fractured reservoirs than dual-continuum models. We developed an embedded discrete fracture model (DFM) for an in-house compositional reservoir simulator that borrows the dual-medium concept from conventional dual-continuum models and also incorporates the effect of each fracture explicitly. The model is compatible with existing finite-difference reservoir simulators. In contrast to dual-continuum models, fractures have arbitrary orientations and can be oblique or vertical, honoring the complexity of a typical NFR. The accuracy of the embedded DFM is confirmed by comparing the results with the fine-grid, explicit-fracture simulations for a case study including orthogonal fractures and a case with a nonaligned fracture. We also perform a grid-sensitivity study to show the convergence of the method as the grid is refined. Our simulations indicate that to achieve accurate results, the embedded discrete fracture model may only require moderate mesh refinement around the fractures and hence offers a computationally efficient approach. Furthermore, examples of waterflooding, gas injection, and primary depletion are presented to demonstrate the performance and applicability of the developed method for simulating fluid flow in NFRs.

Study on the Application of the Tie-Line-Table-Look-Up-Based Methods to Flash Calculations in Compositional Simulations

Summary Flash calculation can be a time-consuming part in compositional reservoir simulations, and several approaches have been proposed to speed it up. One recent approach is the shadow-region method that reduces the computation time mainly by skipping stability analysis for a large portion of the compositions in the single-phase region. In the two-phase region, a highly efficient Newton-Raphson algorithm can be used with the initial estimates from the previous step. Another approach is the compositional-space adaptive-tabulation (CSAT) approach, which is based on tie-line table look-up (TTL). It saves computation time by replacing rigorous phase-equilibrium calculations with the stored results in a tie-line table whenever the new feed composition is on one of the stored tie-lines within a certain tolerance. In this study, a modified version of CSAT, named the TTL method, has been proposed to investigate if approximation by looking up a tie-line table can save flash-computation time in the two-phase region. The number of tie-lines stored for comparison and the tolerance set for accepting the feed composition are the key parameters in this method because they will influence the simulation speed and the accuracy of simulation results. We also proposed the tie-line distance-based approximation (TDBA) method, an alternative method to TTL, to obtain approximate flash results in the two-phase region. The method uses the distance to a previous tie-line in the same grid-block to determine whether the approximation should be made. Comparison between the shadow-region approach and the approximation approach, including TTL and TDBA, has been made with a slimtube simulator by which the simulation temperature and the simulation pressure are set constant. It is shown that TDBA can significantly improve the speed in the two-phase region. In contrast, TTL, even with a precalculated tie-line table, is not so advantageous compared with an efficient implementation of rigorous flash. Furthermore, we implemented TDBA in a compositional streamline simulator to apply TDBA to scenarios with pressure variation across the reservoir. We also discussed how to extend TDBA to the general situation in which pressures in grid-blocks are updated dynamically.

Coupled Carbon Dioxide Sequestration and Energy Production From Geopressured/Geothermal Aquifers

Summary The current approach to carbon capture and sequestration (CCS) from pulverized-coal-fired power plants is not economically viable without either large subsidies or a very high price on carbon. Current schemes require roughly one-third of a power-plant's energy for carbon dioxide (CO2) capture and pressurization. The production of energy from geopressured aquifers has evolved as a separate, independent technology from the sequestration of CO2 in deep, saline aquifers. A game-changing new idea is described here that combines the two technologies and adds another—the dissolution of CO2 into extracted brine that is then reinjected. A systematic investigation covering a range of conditions was performed to explore the best strategy for the coupled process of CO2 sequestration and energy production. Geological models of geopressured/geothermal aquifers were developed with available data from studies of Gulf Coast aquifers. These geological models were used to perform compositional reservoir simulations of realistic processes with coupled aquifer and wellbore models.