Day 2 Thu, April 11, 2019最新文献

{"title":"New and Improved Physical Property Models for Chemical Flooding Simulators","authors":"H. Lashgari, G. Pope, M. Balhoff, Mohsen Tagavifar","doi":"10.2118/193930-MS","DOIUrl":"https://doi.org/10.2118/193930-MS","url":null,"abstract":"\u0000 Significant advances have been made in chemical enhanced oil recovery (EOR) in recent years including the development of hybrid methods that combine surfactants, polymers, alkali, co-solvents, gas and heat in novel ways. New and improved chemical and physical property models have been developed to more accurately simulate these processes at the field scale. We present improved models for relative permeability, capillary pressure, the effect of polymer viscoelasticity on residual oil saturation, the effect of pH on surfactant adsorption, polymer partitioning between aqueous and microemulsion phases, and the effect of co-solvent on microemulsion viscosity. Several simulations are presented to demonstrate how the models can be used to match experimental data.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125608101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Modeling of Fluid-Rock Interactions During Low-Salinity-Brine-CO2 Flooding in Carbonate Reservoirs","authors":"Adedapo Noah Awolayo, H. Sarma, L. Nghiem","doi":"10.2118/193815-MS","DOIUrl":"https://doi.org/10.2118/193815-MS","url":null,"abstract":"\u0000 Fluid-rock interactions can modify certain reservoir properties, notably porosity, permeability, wettability, and capillary pressure, and they may significantly influence fluid transport, well injectivity, and oil recovery. The profound influence of low-salinity-brine flooding is primarily based on wettability alteration, while that of CO2 flooding is based on oil swelling, viscosity reduction, and interfacial tension reduction. Low saline brine, when combined with CO2, leads to higher CO2 solubility and diffusion, and increased brine acidity. The low-salinity-brine-CO2 injection further contributes to the synergy of mechanisms underlying the two processes to improve oil recovery.\u0000 A reactive transport model, which uses surface complexation reactions (SCR) to describe the equilibrium between the rock surface sites and ion species in the brine solution coupled with transport equation, was developed to predict a set of low-salinity-brine-CO2 flooding experiments conducted on carbonate rocks. While conducting batch simulations of the model, it was shown that the thermodynamic parameters reported in the literature for SCRs in a rock–brine system are not suited to natural carbonate rocks. The same thermodynamic parameters could not fit the model to experimental zeta potential data with pulverized and intact carbonate cores at varying potential determining ion concentrations. The model was further utilized to predict the effluent compositions of potential determining ions in single-phase flooding experiments on natural carbonate cores. The failure of thermodynamic parameters in the prediction of reactive transport single-phase experiments, implies that zeta potential is not enough to optimize such parameters for the reactive transport model.\u0000 The reactive–transport model parameters were fitted to the single-phase experiments and a temperature-dependent relationship was generated for the thermodynamic parameters. Then, the optimized model was used in investigating the equilibrium between rock, oil and brine in a set of low-salinity-brine-CO2 flooding experiment. The model showed an incremental recovery of 28% over the formation water flooding, similar to the reported recovery from the experiment. The simulation results show that the incremental recovery can be associated with increased CO2 solubility leading to the formation of in-situ carbonated water to reduce interfacial tension and alter wettability. The performance of low-salinity-brine-CO2 flooding in terms of oil production, relative injectivity, and CO2 storage was evaluated on a field case study using field-specific injection parameters. The results demonstrate that the water injected, and injection scheme has a substantial influence on injectivity and oil production. The injectivity was significantly greater for the water-alternating-gas injection, mainly because the rock surface has an increased contact time with CO2-saturated brine. Meanwhile, carbonated water injection shows greater injec","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123170080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Markov Chain Monte Carlo Uncertainty Quantification with a Least-Squares Support Vector Regression Proxy","authors":"Emilio P. S. Sousa, A. Reynolds","doi":"10.2118/193918-MS","DOIUrl":"https://doi.org/10.2118/193918-MS","url":null,"abstract":"\u0000 Important decisions in the oil industry rely on reservoir simulation predictions. Unfortunately, most of the information available to build the necessary reservoir simulation models are uncertain, and one must quantify how this uncertainty propagates to the reservoir predictions. Recently, ensemble methods based on the Kalman filter have become very popular due to its relatively easy implementation and computational efficiency. However, ensemble methods based on the Kalman filter are developed based on an assumption of a linear relationship between reservoir parameters and reservoir simulation predictions as well as the assumption that the reservoir parameters follows a Gaussian distribution, and these assumptions do not hold for most practical applications. When these assumptions do not hold, ensemble methods only provide a rough approximation of the posterior probability density functions (pdf 's) for model parameters and predictions of future reservoir performance. However, in cases where the posterior pdf for the reservoir model parameters conditioned to dynamic observed data can be constructed from Bayes’ theorem, uncertainty quantification can be accomplished by sampling the posterior pdf. The Markov chain Monte Carlos (MCMC) method provides the means to sample the posterior pdf, although with an extremely high computational cost because, for each new state proposed in the Markov chain, the evaluation of the acceptance probability requires one reservoir simulation run. The primary objective of this work is to obtain a reliable least-squares support vector regression (LS-SVR) proxy to replace the reservoir simulator as the forward model when MCMC is used for sampling the posterior pdf of reservoir model parameters in order to characterize the uncertainty in reservoir parameters and future reservoir performance predictions using a practically feasible number of reservoir simulation runs. Application of LS-SVR to history-matching is also investigated.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124538949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Embedded Discrete Fracture Modeling Workflow for Simulating Complex Processes in Field-Scale Fractured Reservoirs","authors":"M. Hui, G. Dufour, S. Vitel, Pierre Muron, R. Tavakoli, M. Rousset, A. Rey, Bradley T. Mallison","doi":"10.2118/193827-MS","DOIUrl":"https://doi.org/10.2118/193827-MS","url":null,"abstract":"\u0000 Traditionally, fractured reservoir simulations use Dual-Porosity, Dual-Permeability (DPDK) models that can idealize fractures and misrepresent connectivity. The Embedded Discrete Fracture Modeling (EDFM) approach improves flow predictions by integrating a realistic fracture network grid within a structured matrix grid. However, small fracture cells with high conductivity that pose a challenge for simulators can arise and ad hoc strategies to remove them can alter connectivity or fail for field-scale cases. We present a new gridding algorithm that controls the geometry and topology of the fracture network while enforcing a lower bound on the fracture cell sizes. It honors connectivity and systematically removes cells below a chosen fidelity factor. Furthermore, we implemented a flexible grid coarsening framework based on aggregation and flow-based transmissibility upscaling to convert EDFMs to various coarse representations for simulation speedup. Here, we consider pseudo-DPDK (pDPDK) models to evaluate potential DPDK inaccuracies and the impact of strictly honoring EDFM connectivity via Connected Component within Matrix (CCM) models. We combine these components into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications.\u0000 We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models. The coarse simulation results show biases consistent with the underlying assumptions (e.g., pDPDK can over-connect fractures). The preservation of fracture connectivity via the CCM aggregation strategy provides better accuracy relative to the fine EDFM forecast while maintaining computational speedup. We then demonstrate the robustness of the proposed EDFM workflow for practical studies through application to an improved oil recovery (IOR) study for a fractured carbonate reservoir. Our automatable workflow enables quick screening of many possibilities since the generation of full-field grids (comprising almost a million cells) and their preprocessing for simulation completes in a few minutes per model. The EDFM simulations, which account for complicated multiphase physics, can be generally performed within hours while coarse simulations are about a few times faster. The comparison of ensemble fine and coarse simulation results shows that on average, a DPDK representation can lead to high upscaling errors in well oil and water production as well as breakthrough time while the use of a more advanced strategy like CCM provides greater accuracy. Finally, we illustrate the use of the Ensemble Smoother with Multiple Data Assimilation (ESMDA) approach to account for field measured data and provide an ensemble of history-matched models with calibrated properties.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127473100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced Degrees of Freedom Gaussian Mixture Model Fitting for Large Scale History Matching Problems","authors":"G. Gao, Hao Jiang, Chaohui Chen, J. Vink, Y. E. Khamra, J. Ita, F. Saaf","doi":"10.2118/193916-ms","DOIUrl":"https://doi.org/10.2118/193916-ms","url":null,"abstract":"\u0000 Gaussian-mixture-model (GMM) fitting has been proved a robust method to generate high quality, independent conditional samples of the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees-of-freedom (DOF) for all unknown GMM parameters may become huge for large-scale history-matching problems. A new formulation of GMM fitting with reduced number of DOF is proposed in this paper, to save memory-usage and reduce computational cost. Its performance is compared with other methods of GMM.\u0000 The GMM fitting method can significantly improve the accuracy of the GMM approximation by adding more Gaussian components. In the full-rank GMM fitting formulation, both memory-usage and computational cost are proportional to the number of Gaussian components. In the reduced DOF GMM fitting formulation, the covariance matrix of the newly added Gaussian component is efficiently parameterized, using products of a low number of vectors and their transposes, whereas the other Gaussian components are simply modified by multipliers. Thus, memory usage and computational cost increase only modestly as the number of Gaussian components increases.\u0000 Unknown GMM parameters, including the parameterized covariance matrix and mixture weighting factor for each Gaussian component, are first determined by minimizing the error that measures the distance between the GMM approximation and the actual posterior PDF. Then, performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method is found to perform more efficiently than the full-rank GMM fitting formulation, e.g., it further reduces the memory usage and computational cost by a factor of 5 to 10, while it achieves comparable accuracy. Although it is less efficient than the L-GMM approximation based on local linearization, it achieves much higher accuracy, e.g., it manages to further reduce the error by a factor of 20 to 600.\u0000 Finally, the new method together with the parallelized acceptance-rejection (AR) algorithm is applied to a history matching problem. It is found to reduce the computational cost (i.e., the number of simulations required to generate an accepted conditional realization on average) by a factor of 200 when compared with the Markov chain Monte Carlo (MCMC) method, while the quality of accepted GMM samples is comparable to the MCMC samples. Uncertainty of reservoir model parameters and production forecasts can be properly quantified with accepted GMM samples by conditioning to production data.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Algebraic Smoothing Aggregation Two Level Preconditioner to Multiphysical Fluid Flow Simulations in Porous Media","authors":"Shihao Wang, A. Lukyanov, Yu-Shu Wu","doi":"10.2118/193870-MS","DOIUrl":"https://doi.org/10.2118/193870-MS","url":null,"abstract":"\u0000 Traditionally, preconditioners are used to damp slowly varying error modes in the linear solver stage. State-of-the-art multilevel preconditioners use a sequence of aggressive restriction, coarse-grid correction and prolongation operators to handle low-frequency modes on the coarse grid. High-frequency errors are then resolved by employing a smoother on fine grid. In this paper, the algebraic smoothing aggregation two level preconditioner is implemented to solve different coupled problems.\u0000 The proposed method generalizes the existing MsRSB and smoothing aggregation AMG methods. This method does not require any coarse partitioning and, hence, can be applied to general unstructured topology of the fine scale. Inspired by smoothing aggregation algebraic multigrid solver, the algebraic smoothing aggregation preconditioner constructs basis functions which allow mapping of some high-frequency modes from fine scale to low-frequency modes on the coarse scale. These basis functions are also used to reconstruct unknown primary variables at the fine scale using their approximations at the coarse level.\u0000 The proposed preconditioner has been adopted to challenging multiphysical problems, including fully coupled simulation of filtration and geomechanics processes including non-isothermal fluid flow problems. The preconditioner provides a reasonably good approximation to the coupled physical processes and speeds up the convergence. Compared to traditional ILU0+GMRES linear solvers, our preconditioner with GMRES solver reduces the number of iterations by about 3 times. In addition, the proposed method obeys a good theoretical scalability essential for parallel simulations.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjoint-Based Adaptive Convergence Control of the Iterative Finite Volume Multiscale Method","authors":"W. D. Zeeuw, R. J. Moraes, A. Heemink, J. Jansen","doi":"10.2118/193812-MS","DOIUrl":"https://doi.org/10.2118/193812-MS","url":null,"abstract":"\u0000 We propose a novel adaptive, adjoint-based, iterative multiscale finite volume (i-MSFV) method. The method aims to reduce the computational cost of the smoothing stage of the original i-MSFV method by selectively choosing fine-scale sub-domains (or sub-set of primary variables) to solve for. The selection of fine-scale primary variables is obtained from a goal-oriented adjoint model. An adjoint-based indicator is utilized as a criterion to select the primary variables having the largest errors. The Lagrange multipliers from the adjoint model can be interpreted as sensitivities of the objective function value with respect to deviations from the constraints. In case of adjoining the porous media flow equations with Lagrange multipliers, this implies that the multipliers are the sensitivities of the objective function with respect to the residuals of the flow equations, i.e., to the residual error that remains after approximately solving linear equations with the aid of an iterative solver. This allow us to recognize at which locations the solution contains more errors. More specifically, we propose a modification to the i-MSFV method to adaptively reduce the size of the fine-scale system that must be smoothed. The aim is to make the fine-scale smoothing stage less computationally demanding. To that end, we introduce a goal-oriented, adjoint-based fine-scale system reduction criterion. We demonstrate the performance of our method via single-phase, incompressible flow simulation models with challenging geological settings and using a history-matching like misfit objective function as the goal. The performance of the newly introduced method is compared to the original i-MSFV method. We investigate the adaptivity versus accuracy of the method and demonstrate how the solution accuracy varies by varying the number of unknowns selected to be smoothed. It is shown that the method can provide accurate solutions at reduced computational cost. The proof-of-concept applications indicate that the method deserves further investigations.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114278943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Fuzzy Timestep Selector for a Fully Implicit Reservoir Simulator","authors":"P. Crumpton","doi":"10.2118/193809-MS","DOIUrl":"https://doi.org/10.2118/193809-MS","url":null,"abstract":"\u0000 The objective of this work is to avoid wasteful timestep cuts of the reservoir simulator by developing a timestep-selector that controls the linear and non-linear iterations as well as the physical quantities. Using a Fuzzy logic framework, a non-linear timestep selector has been developed that reduces run time, and increases robustness for challenging nonlinear simulations.\u0000 From a linear analysis standpoint a fully implicit reservoir simulator has no stability limit on the size the timestep. However, in practice the non-linearity prevents arbitrary timestep size being chosen. Without any theory to guide us the timestep choice it is left to heuristics, usually based on physical engineering constraints such as the previous time steps, maximum pressure and saturation changes. This can be very effective, but can lead to many timestep cuts, and sometimes lead to failure of the simulator. This is especially common for highly non-linear dual-porosity, dual-permeability reservoirs which are very common in the Middle East. Here a Fuzzy logic framework is used to construct a non-linear timestep selector which takes many inputs (linear and non-linear convergence data as well as pressure and saturation changes) and breaks down the complexity. Firstly fuzzification of the inputs into fuzzy sets (e.g. High medium and low) then applications of rules (e.g. if linear high then timestep is low) and de-fuzzification into a crisp timestep to be used for the next iteration. This process provides us with a powerful framework to construct various strategies for controlling the timestep. In contrast, traditional timestep controllers use crisp logic, this is difficult to blend multiple conflicting inputs to a timestep selector.\u0000 To demonstrate the effectiveness of this approach results are presented on a suite of cases, covering a wide range of models including compositional and dual-porosity cases. For some cases a dramatic 3x improvement is observed, however, what is more important, is on average the new timestep selector significantly improves performance, especially for the slow challenging cases; by reducing the time steps wasted due to timestep cuts. Perhaps what is most impressive is that the fuzzy controller did achieve the goals of the fuzzy rules to keep the non-linear and linear iterations under control, which had the benefit of reducing total failures of the simulator.\u0000 A fuzzy logic framework is applied to timestep selection of a fully implicit reservoir simulator. A combination of convergence data as well as physical quantities are used as inputs which has led to a robust and extendable timestep selector.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130720308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Geomechanics-Coupled Embedded Discrete Fracture Model and its Application in Geothermal Reservoir Simulation","authors":"Xiangyu Yu, P. Winterfeld, Shihao Wang, Cong Wang, Lei Wang, Yu-Shu Wu","doi":"10.2118/193931-MS","DOIUrl":"https://doi.org/10.2118/193931-MS","url":null,"abstract":"\u0000 Geomechanics plays an essential role in fluid/heat flow by affecting hydraulic parameters. This influence could be amplified when fractures exist in the system because fracture aperture is highly sensitive to stresses. Coupled fluid/heat flow and geomechanics model is considerably important in simulating thermal-hydrologic-mechanical process, such as geothermal reservoir development. At the same time, due to the rock matrix shrinkage or expansion, thermal stress exerted on fracture surface remolds the aperture significantly and should be incorporated in modeling heat related process.\u0000 In this study, a coupled fluid/heat flow and geomechanics model, TOUGH2-THM, was developed based on the parallel framework of TOUGH2-CSM, with stress tensor components as primary variables. This modification is aiming on computing normal stresses on discrete fracture surface such that fracture related parameters can be fully coupled with geomechanical model. Embedded discrete fracture model was also improved to be compatible with the geomechanical coupling. Both of TOUGH2-THM and modified EDFM were validated for further application.\u0000 A geothermal reservoir simulation is conducted by the newly developed model, demonstrating the capability of this program to perform coupled modeling. It is also concluded that geomechanics and especially temperature alteration induced stress could affect fluid/heat flow in fracture and rock matrix. Thus, production efficiency could be impacted as well. The thermal stress generated by temperature reduction could enhance the fracture permeability in orders of magnitude. Various scenarios of injection temperature were modeled and compared. It can be observed that geothermal reservoir development is negatively influenced by geomechanical (and thermal) effect on fractures. The coupled model is helpful to improve the simulation accuracy.","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"History Matching an Unconventional Reservoir with a Complex Fracture Network","authors":"Zhe Liu, A. Reynolds","doi":"10.2118/193921-MS","DOIUrl":"https://doi.org/10.2118/193921-MS","url":null,"abstract":"\u0000 Multistage hydraulic fracturing of a horizontal well in an unconventional reservoir tends to induce a complex fracture network (CFN) which is challenging to characterize by conventional methods. In this work, we develop a fracture characterization workflow to estimate the geometric configuration and fracture properties of a CFN by assimilating microseismic event data and production data, sequentially.\u0000 A novel stochastic fractal model, that is consistent with rock physics and outcrop observations, is developed in order to generate realizations of the complex fracture network. In the first stage of the two-stage assisted history matching workflow, we estimate the parameters of the stochastic fractal model (fracture intensity, average fracture length, orientation and fracture distribution) by using a genetic algorithm to history match data for the locations of microseismic events. In the second stage, the production data from the shale reservoir are assimilated by the ES-MDA algorithm to estimate the stimulated reservoir volume (SRV) and its average permeability, fracture permeability, aperture and porosity. In the unconventional shale gas reservoir simulator used as the forward model, large-scale fractures are modeled via the embedded discrete fracture model (EDFM) and a dual-porosity, dual-permeability (DP-DK) model is used for modeling the SRV and small scale fractures. The simulator includes Knudsen diffusion and the Langmuir adsorption/desorption model.\u0000 For validation, we consider a synthetic shale gas reservoir with a horizontal well that has been stimulated by multistage hydraulic fracturing. A particular realization of the variables that describe the reservoir model is used to generate observed data for microseismic events and production rates. The parameters to be adjusted to match the observed microseismic events are the expected values of the length, orientation and intensity of the distribution of the natural fractures and the fractal pattern. Results show that we obtain good estimates of the expected value of natural fracture length, orientation, intensity and fracture distribution by history matching observations of locations of microseismic events. These estimates provide an updated stochastic fractal model for the configuration of CFN. The history-matched fractal model is used to generate an ensemble of fracture distributions consistent with microseismic data as candidate fracture configurations when estimating fracture properties by matching production data. We obtain much better history matches, future performance predictions, estimates of stimulated reservoir volume and its average permeability and estimates of fracture permeability, porosity and aperture when we match both microseismic and production data than we only match production data. When both seismic and production data are matched for synthetic cases and parameters are properly scaled, the true values of parameters and reservoir performance predictions are within the","PeriodicalId":246878,"journal":{"name":"Day 2 Thu, April 11, 2019","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132145170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}