Day 1 Tue, March 28, 2023最新文献

{"title":"Resolving Ambiguity in 2008-2015 Irving-Dallas Seismicity by Coupling Geomechanical Models at Fort Worth Basin and Barnett Reservoir Scales","authors":"Abdulrahman Bubshait, B. Jha","doi":"10.2118/212170-ms","DOIUrl":"https://doi.org/10.2118/212170-ms","url":null,"abstract":"\u0000 The activation mechanism of Irving-Dallas events is not well understood as it is shrouded in ambiguity due to many earthquakes located relatively far (>15 km) from production and injection wells. This requires a modeling approach that can quantify spatiotemporal propagation of production- and injection-induced stresses from wells to the faults while resolving fault geometry, stratigraphy, and well activity. However, constructing one such detailed model for the entire basin is computationally prohibitive due to the millions of grid cells needed to discretize the basin at that resolution. Based on our analysis of the data on well activity and fault position, we employed a novel two-model approach that exploits the disparity in scales between the basin-scale injection analysis and the well-scale fault reactivation analysis. We construct a coarse-scale model of Ellenburger injection in the Fort Worth basin and a fine-scale flow-geomechanics model of the Dallas-Irving region containing the faults that hosted the seismicity and the production/injection wells in the region. We use the coarse model to provide time-dependent pressure boundary conditions to the fine-scale model. We analyze the spatiotemporal evolution of pressure fields at both basin and reservoir scales. Analysis of the results provides evidence for interaction between Barnett's production and Ellenburger's injection as well as pressure diffusion from Ellenburger into the basement along the through-going faults. It allows us to test the hypothesis of injection-induced reactivation as the causative mechanism for the Irving seismic events. Almost all injection-induced seismicity studies in the literature show how injection near a fault (well-to-fault distance < 10 km) can induce seismicity. We provide evidence of far-field injection-induced seismicity (well-to-fault distance > 80 km) by coupling basin-scale and reservoir-scale models and a multi-physics approach.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122118","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":"Role of Inelasticity in Production-Induced Subsidence and Fault Reactivation in the Groningen Field","authors":"Xiaoxi Zhao, B. Jha","doi":"10.2118/212234-ms","DOIUrl":"https://doi.org/10.2118/212234-ms","url":null,"abstract":"\u0000 Long-term production of gas from the Groningen field has led to subsidence and seismicity in the region. Most of the prior Groningen modeling studies assumed elastic deformation of the reservoir due to the challenges in modeling poroplasticity in a reservoir with hundreds of faults and decades of production history. Here we quantify the role of inelastic deformation in production-induced subsidence and seismicity in the field via 3D high-resolution multiphysics modeling which couples multiphase flow and elastoplastic deformation in a complex geologic system made of claystone overburden, carboniferous underburden, and the gas-bearing sandstone reservoir compartmentalized with 100+ faults. We drive the model with four decades of historical production, spanning the period of induced seismicity, and two decades of future production under gas injection-enhanced recovery. We calibrate the model using the available pressure and subsidence data and analyze compartmentalized depletion and deformation due to spatially varying production and fault distribution. We analyze stress and strain in the caprock-reservoir depth interval to elucidate the role of inelasticity. We use the evolution in shear and normal tractions on seismogenic faults that hosted 1991-2012 seismicity to quantify the evolution in Coulomb stress and geomechanical stability of the faults.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526095","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":"Formation Fracturing by High-Energy Impulsive Mechanical Loading","authors":"Wenzhi Cao, R. Younis","doi":"10.2118/212174-ms","DOIUrl":"https://doi.org/10.2118/212174-ms","url":null,"abstract":"\u0000 Recent technological advances to trigger high-energy seismic waves from within the wellbore have spurred interest in their application to induce fracturing. While a considerable body of recent experiments at the bench scale (on the order of 1 cubic foot) show promise, there remains considerable uncertainty in how the process scales. This work characterizes the scaling relationships between the extent and intensity of fracturing stimulation and stress-wave characteristics. Our approach leverages direct numerical simulation of the elastodynamic equations accounting for nonlinear fracture mechanics. We apply a hybrid Finite-Discrete Element Method (FDEM) where cohesive (elasto-plastic) laws hold mesh elements together until complete failure. Beyond failure, elements act as deformable free bodies that can interact via contact constraints. An infinite domain is modeled with a spherical inclusion within which an impulsive load is imposed. The dynamic load models a rise time to a peak pressure, followed by a decay period, and all occurring within micro- to milliseconds. The model is validated with experimental observations at the bench scale after mesh-refinement verification. Finally, the model is used to explore the dimensionless parameter space by varying loading characteristics (rise time, peak pressure, and impulse) to reveal the stimulated damaged bulk volume and the crack intensity within it. At the bench scale, the model reproduces a nearly linear trend between damage radius and peak stress. Beyond that, however, the model predicts that this scaling slows considerably to a fractional power law between the damaged radius and the peak stress. This limitation is coincident with a geometric increase in the intensity of damage within the stimulated volume.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114995243","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":"Troll Reservoir Simulation Development, From Well Long-Term Tests to Full FMU Simulations","authors":"E. Reiso, Kjetil Bjørke, T. Ek, R. Nybø, Jan C. Rivenæs","doi":"10.2118/212163-ms","DOIUrl":"https://doi.org/10.2118/212163-ms","url":null,"abstract":"\u0000 Reservoir simulation studies of the Troll field, from the start with single realization full field simulation models and well simulation models in 1991 until today's complex and large ensemble models, have given important input to the Troll field development, reservoir management and well planning. The main focus in this paper is on Troll Oil simulation model. The effects of hardware and software technology developments are discussed. This includes challenges due to field size, geology, communications, thin oil zone, horizontal wells, gridding, numerics, CPU etc. Troll reservoir simulation has always pushed the limits of the hardware and the software, and this has initiated new solutions in modelling and simulators.\u0000 The following topics are addressed:\u0000 General information about the Troll field Troll Oil – how did it start Model size, grid resolution and hardware capacity – pushing the limits Grid construction Well modelling Geological reservoir model From Reference Model to Multiple Realizations","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244390","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":"An Enriched Galerkin Discretization Scheme for Two Phase Flow on Non-Orthogonal Grids","authors":"M. Jammoul, F. Alpak, M. Wheeler","doi":"10.2118/212238-ms","DOIUrl":"https://doi.org/10.2118/212238-ms","url":null,"abstract":"\u0000 The representation of faults and fractures using cut-cell meshes often results in irregular non-orthogonal grids. Simple finite volume approaches fail to handle complex meshes because they are highly prone to grid orientation effects and only converges for K-orthogonal grids. Wide stencil approaches and higher order methods are computationally expensive and impractical to adopt in commercial reservoir simulators. In this work, we implement an Enriched Galerkin (EG) discretization for the flow and transport problems on non-orthogonal grids. The EG approximation space combines continuous and discontinuous Galerkin methods. The resulting solution lies in a richer space than the the two-point flux approximation (TPFA) method and allows a better flux approximation. It also resolves the inconsistencies that are usually associated with TPFA scheme. The method is tested for various non-orthogonal mesh configurations arising from different fault alignments. The performance of the scheme is also tested for reservoirs with strong anisotropy as well as reservoirs with heterogeneous material properties.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113988820","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 Quasi-Newton Method for Well Location Optimization Under Uncertainty","authors":"Esmail Eltahan, F. Alpak, K. Sepehrnoori","doi":"10.2118/212212-ms","DOIUrl":"https://doi.org/10.2118/212212-ms","url":null,"abstract":"\u0000 Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Conventional gradient-based methods are known to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the objective function may be too expensive to compute for realistic applications in the absence of the implementation of the adjoint method. In this paper, we develop a quasi-Newton method based on the stochastic simplex approximate gradient (StoSAG), which requires only objective-function values.\u0000 We have implemented the BFGS quasi-Newton updating algorithm together with line-search and trust-region optimization strategies. We have developed a novel approach to enhance the accuracy of StoSAG gradients by modifying their formulations to enable exploiting the objective-function structure. The objective function is treated as a summation of element functions, each representing the contribution from an individual well at distinct time steps. Instead of working with a single value for the gradient, we treat it as a sum of sub-gradients. We then utilize problem-specific prior knowledge to form a matrix W that acts on the sub-gradients. The entries of W vary from 0 to 1 and are proportional to the interference effects the neighbouring wells have on each other. We define those entries (or weights) based on the radii of investigation around the wells. The BFGS-StoSAG variants are demonstrated on a realistic synthetic case with 26 wells while varying the average reservoir permeability.\u0000 We first show that the BFGS algorithm delivers promising performance as in many cases it results in the most rapid improvement for the objective-function values (especially in early iterations). Further testing results confirm that the trust-region protocol is more effective than the line-search protocol for accelerating convergence with BFGS. Although the objective function is not always continuously differentiable with respect to well locations, the StoSAG variants overcome this challenge owing to their smoothing properties of approximate gradients. Moreover, we show that using our gradient correction procedures on the well-location optimization problem results in drastic acceleration in convergence indicating enhancement in the StoSAG gradient approximation quality.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115655151","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 Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-Based Proxy Modelling","authors":"George Hadjisotiriou, Kiarash Mansour Pour, D. Voskov","doi":"10.2118/212217-ms","DOIUrl":"https://doi.org/10.2118/212217-ms","url":null,"abstract":"\u0000 In this study, we utilize deep neural networks to approximate operators of a nonlinear partial differential equation (PDE), within the Operator-Based Linearization (OBL) simulation framework, and discover the physical space for a physics-based proxy model with reduced degrees of freedom. In our methodology, observations from a high-fidelity model are utilized within a supervised learning scheme to directly train the PDE operators and improve the predictive accuracy of a proxy model. The governing operators of a pseudo-binary gas vaporization problem are trained with a transfer learning scheme. In this two-stage methodology, labeled data from an analytical physics-based approximation of the operator space are used to train the network at the first stage. In the second stage, a Lebesgue integration of the shocks in space and time is used in the loss function by the inclusion of a fully implicit PDE solver directly in the neural network's loss function. The Lebesgue integral is used as a regularization function and allows the neural network to discover the operator space for which the difference in shock estimation is minimal. Our Physics-Informed Machine Learning (PIML) methodology is demonstrated for an isothermal, compressible, two-phase multicomponent gas-injection problem. Traditionally, neural networks are used to discover hidden parameters within the nonlinear operator of a PDE. In our approach, the neural network is trained to match the shocks of the full-compositional model in a 1D homogeneous model. This training allows us to significantly improve the prediction of the reduced-order proxy model for multi-dimensional highly heterogeneous reservoirs. With a relatively small amount of training, the neural network can learn the operator space and decrease the error of the phase-state classification of the compositional transport problem. Furthermore, the accuracy of the breakthrough time prediction is increased therefore improving the usability of the proxy model for more complex cases with more nonlinear physics.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117063383","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":"Efficient Adaptation and Calibration of Ad joint-Based Reduced-Order Coarse-Grid Network Models","authors":"S. Krogstad, Ø. Klemetsdal, Knut-Andreas Lie","doi":"10.2118/212207-ms","DOIUrl":"https://doi.org/10.2118/212207-ms","url":null,"abstract":"\u0000 Network models have proved to be an efficient tool for building data-driven proxy models that match observed production data or reduced-order models that match simulated data. A particularly versatile approach is to construct the network topology so that it mimics the intercell connection in a volumetric grid. That is, one first builds a network of \"reservoir nodes\" to which wells can be subsequently connected. The network model is realized inside a fully differentiable simulator. To train the model, we use a standard mismatch minimization formulation, optimized by a Gauss-Newton method with mismatch Jacobians obtained by solving adjoint equations with multiple right-hand sides. One can also use a quasi-Newton method, but Gauss-Newton is significantly more efficient as long as the number of wells is not too high. A practical challenge in setting up such network models is to determine the granularity of the network. Herein, we demonstrate how this can be mitigated by using a dynamic graph adaption algorithm to find a good granularity that improves predictability both inside and slightly outside the range of the training data.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122462096","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":"An Integrated Modeling Framework for Simulating Complex Transient Flow in Fractured Reservoirs with 3D High-Quality Grids","authors":"Hui Liu, X. Liao, Knut-Andreas Lie, Ø. Klemetsdal, K. Bao, Xiaoliang Zhao, A. Johansson, X. Raynaud","doi":"10.2118/212171-ms","DOIUrl":"https://doi.org/10.2118/212171-ms","url":null,"abstract":"\u0000 Modeling near-well transient flow with complex 3D fracture networks poses several challenges: the multiscale nature (millimeters to kilometers), long and deviating well trajectories, intricate fracture networks with fracture-fracture and fracture-well intersections, and high level of reservoir heterogeneities. We address these difficulties by proposing a comprehensive methodology for meshing, discretizing, and simulating transient flow in complex 3D fracture networks based on discrete fracture-matrix models.\u0000 Our framework consists of three parts: (i) Given deviating wells and planar or nonplanar fractures and faults, we construct highquality 3D grids conforming to wells, hydraulic fractures, faults, and dominating natural fractures. We ensure sufficient mesh quality near important features using transfinite interpolation near wells and hydraulic fractures, combined with adaptive refinement in regions of interest. (ii) With the generated grid, we discretize the governing equations with a fully implicit finite- volume formulation with an inner-boundary well model and discrete fracture model. (iii) Finally, we analyze the results using suitable visualization tools, both for pressure-transient curves and 3D matrix/fracture data.\u0000 The framework enables high-resolution numerical modeling of transient flow with complex fracture networks in 3D. We demonstrate the capacities through simple validation cases with comparisons against an industry-standard commercial well-testing software but also present highly complex cases with long and deviating well trajectories and highly detailed fracture networks. We present and analyze flow-transient behavior coupling the wellbore, the fracture network, and the matrix. We also present an approach to reliably diagnose complex multiple flow regimes on the pressure-transient curves combined with different-scale spatial pressure distribution. Comparison against the commercial software indicates that our framework does not introduce adverse grid-orientation effects for non-K-orthogonal grids which is able to robustly handle the details for fracture-network heterogeneities in 3D reservoirs.\u0000 Overall, our framework is robust for simulating and analyzing realistic second-level transient effects and short-term well performance with complex fracture networks and heterogeneities. Detailed description of the 3D fracture networks, and accurate simulation of the near-well transient flow behavior can be achieved, which provides confidence to interpret the dynamic flow data at different scales and observe transport mechanisms in unconventional fractured reservoirs with multiple levels of heterogeneity.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129232886","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":"Data-Space Inversion for Rapid Physics-Informed Direct Forecasting in Unconventional Reservoirs","authors":"M. Hui, Kainan Wang, Jincong He, Shusei Tanaka","doi":"10.2118/212230-ms","DOIUrl":"https://doi.org/10.2118/212230-ms","url":null,"abstract":"\u0000 Traditionally, subsurface models are created based on reservoir characterization, then simulated and calibrated via history matching (HM) to honor data, generate forecasts, and quantify uncertainties. However, this approach is time consuming for unconventional projects with aggressive schedules. On the other hand, purely data-driven approaches such as decline curve analysis (DCA) are fast but not reliable for yet-to-be-observed flow regimes, e.g., boundaries or other effects causing late-time changes in productivity decline behaviors. We propose a physics-informed unconventional forecasting (PIUF) framework that combines simulations and data analytics for robust field applications. We apply Data-Space Inversion (DSI) to incorporate physics from a large ensemble of prior simulation models to generate posterior forecasts within a Bayesian paradigm. We also quantify the consistency of simulated physics and observed data by computing the Mahalanobis distance to ensure that the appropriate prior ensemble is employed. In lieu of history-matched models, a statistical relationship between data and forecast is learned; then posterior sampling is applied for data assimilation and direct forecasting in DSI. DSI reduces the dimensions of time-series (and other) data using parameterization like Principal Component Analysis. We implemented DSI within a tool that is connected to a vast database of observations for thousands of unconventional Permian Basin wells and a large ensemble of fracture simulations. We apply it to rapidly generate probabilistic forecasts (e.g., oil production rate, gas oil ratio) for unconventional wells and show that DSI can provide robust long-term forecasts based on early-time data when compared with DCA. We show that DSI yields robust uncertainty quantification with a manageable number of simulations compared with simple machine-learning methods like K-Nearest-Neighbors. We illustrate how data error and volume impact DSI forecasts in meaningful ways. We also introduce a DSI enhancement to generate posterior distributions for model parameters (e.g., hydraulic fracture height) to derive subsurface insights from data and understand key performance drivers. Our cloud-native implementation stores data (observed and simulated) in the cloud while the algorithm is implemented as a microservice that is efficient and elastic for the analysis of many wells. The overall framework is useful for rapid probabilistic forecasting to support development planning and de-risk new areas as an alternative to DCA or HM.","PeriodicalId":225811,"journal":{"name":"Day 1 Tue, March 28, 2023","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115952391","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}