Coarse-scale Simulation Research Articles

A 3D Structure Mapping-Based Efficient Topology Optimization Framework

Abstract It is well known that the computational cost of classic topology optimization (TO) methods increases rapidly with the size of the design problem because of the high-dimensional numerical simulation required at each iteration. Recently, the technical route of replacing the TO process with artificial neural network (ANN) models has gained popularity. These ANN models, once trained, can rapidly produce an optimized design solution for a given design specification. However, the complex mapping relationship between design specifications and corresponding optimized structures presents challenges in the construction of neural networks with good generalizability. In this paper, we propose a new design framework that uses deep learning techniques to accelerate the TO process. Specifically, we present an efficient topology optimization (ETO) framework in which structure update at each iteration is conducted on a coarse scale and a structure mapping neural network (SMapNN) is constructed to map the updated coarse structure to its corresponding fine structure. As such, fine-scale numerical simulations are replaced by coarse-scale simulations, thereby greatly reducing the computational cost. In addition, fragmentation and padding strategies are used to improve the trainability and adaptability of SMapNN, leading to a better generalizability. The efficiency and accuracy of the proposed ETO framework are verified using both benchmark and complex design tasks. It has been shown that with the SMapNN, TO designs of millions of elements can be completed within a few minutes on a personal computer.

Machine learning assisted relative permeability upscaling for uncertainty quantification

Traditional two-phase relative permeability upscaling entails the computation of upscaled relative permeability functions for each coarse block (or interface). The procedure can be extremely time-consuming especially for cases with multiple (hundreds of) geological realizations as commonly used in subsurface uncertainty quantification or optimization. In this paper, we develop a machine learning assisted relative permeability upscaling method, in which the flow-based two-phase upscaling is performed for only a small portion of the coarse blocks (or interfaces), while the upscaled relative permeability functions for the rest of the coarse blocks (or interfaces) are quickly computed by machine learning algorithms. The upscaling procedure was tested for generic (left to right) flow problems using 2D models for scenarios involving multiple realizations. Both Gaussian and channelized models with standard boundary conditions and effective flux boundary conditions (EFBCs) are considered. Numerical results have shown that the coarse-scale simulation results using the newly developed machine learning assisted upscaling are of similar accuracy to the coarse results using full numerical upscaling at both ensemble and realization-by-realization levels. Because the full flow-based upscaling is only performed for a small fraction of the models, significant speedups are achieved.

Assessment of the spatio-temporal variability of the added value on precipitation of convection-permitting simulation over the Iberian Peninsula using the RegIPSL regional earth system model

In this study, we have assessed the added value on the spatio-temporal distribution of the precipitation of convection-permitting simulation (3 km) compared to the parent coarse-scale parameterized convection simulation (20 km) with the high-resolution observational datasets i.e., SPREAD (5 km) and IBERIA01 (10 km) over the Iberian Peninsula (IP) in all four seasons during 2000–2009. Both simulations are evaluation runs based on ERA-Interim reanalysis and performed with the RegIPSL regional earth system model in the frame of the European Climate Prediction system (EUCP) H2020 project and COordinated Regional climate Downscaling Experiment (CORDEX). We have not found significant improvement in the convection-permitting simulation compared to the parent coarse-scale simulation for the seasonal mean precipitation of the IP except the spatial variation over mountainous peaks. The kilometer-scale simulation significantly underestimates the observed seasonal mean precipitation over the western parts of the IP compared to the coarse-scale simulation, which may be attributed to a change of local dynamics in the kilometer-scale simulation with a weakening and southward shift of the moisture-laden westerly winds approaching from the Atlantic Ocean over the IP (i.e., a decline in atmospheric moisture transport from the Atlantic Ocean towards the IP). However, the added value of kilometer-scale simulation over the driving coarse-scale simulation is obtained for various indices; in the representation of the spatio-temporal distribution of the wet-day precipitation frequency and intensity, and the extreme/heavy precipitation events for each season at both resolutions i.e., downscaled and upscaled. It has also been noted that the spatio-temporal distribution of precipitation for all metrics used varies between the two observational datasets for all seasons.

Treatment of model error in subsurface flow history matching using a data-space method

Subsurface flow models are inherently imperfect, and the associated model error can lead to unreliable flow predictions. In this work, we introduce treatments for model error in a data-space inversion (DSI) framework. DSI is a history matching procedure that constructs posterior flow predictions (for quantities of interest) directly, within a Bayesian setting, using a large set of prior-model flow simulation results and observed data. The model error considered in this work derives from the use of upscaled/coarsened surrogate models for the prior-model flow simulations required by DSI. Our error treatment entails the simulation of a set of corresponding pairs of fine and upscaled models. These results are used to construct a principal component analysis (PCA) representation of error. A linear regression approach is introduced to capture the coupled nature of the coarse-scale simulation output and model error. To construct posterior DSI predictions, a joint inversion on coarse-scale simulation data and model error is performed using an ensemble smoother with multiple data assimilation. Both the prior simulation data and error terms are parameterized using PCA-based procedures. Results are presented for two-phase flow in 3D channelized geomodels. The corrected prior and DSI posterior results are compared to reference results generated from fine-scale simulations. Comparisons are presented for flow statistics, Mahalanobis distance, and relative error for multiple (synthetic) true models. The coupled model error treatment is shown to provide highly accurate prior results and posterior predictions that agree closely with reference results. Significant improvement relative to uncorrected coarse models is observed. The treatments developed here can be used to represent error from many different sources in a variety of subsurface flow settings.

Improving coarse-scale simulation models with a dual-porosity dual-permeability upscaling technique and a near-well approach

In the management of an oil field, the creation of a highly detailed fine-scale model to properly represent the key reservoir heterogeneities is important. This model better represents the main reservoir features but remains a significant challenge for dynamic reservoir simulations with impractically high computational costs, even for single numerical simulation runs. Therefore, the scaling up of a highly detailed model is a crucial step in reservoir engineering studies to enable field-scale simulation runs within a time-frame compatible with the needs of the industry. Upscaling must be carefully performed because it can introduce significant errors and bias to the numerical models due to truncation errors and the smoothing of sub-grid heterogeneities.This work focuses on improving the representation of small-scale heterogeneities in coarse models. It expands the dual-porosity and dual-permeability upscaling technique proposed (Rios et al., 2020) to be applied to 3D highly heterogeneous systems. Specific near-well treatment is considered to improve the overall flow distribution in the wells; with this procedure, the coarse model better represents the production and injection profiles throughout the wells. Therefore, the final coarse-scale model can represent the main reservoir heterogeneities and possible preferential pathways, while improving the definition of the wells’ productivity and injectivity indices.The proposed upscaling technique is applied to different reservoir simulation models with immiscible water flooding in 3D evaluations. Our approach was validated by two case studies: SPE-10 and a field-case heterogeneous reservoir with karst structures. The focus is to demonstrate that the division of the porous media into primary and secondary systems improves capillary and gravity representation of coarse models in 3D applications, while the near-well upscaling makes the flow distribution in the wells in agreement with the fine-scale solution. The resulting coarse-scale dual-permeability models are much faster than the reference fine models (between 0.025% and 1.5% of the fine-scale simulation time), more accurate than the traditional flow-based upscaling results, and can better reproduce the fine-scale responses in different upscale ratios (from 25 up to 1 700).

Upscaling Technique for Highly Heterogeneous Reservoirs Based on Flow and Storage Capacity and the Lorenz Coefficient

Summary Field-scale representation of highly heterogeneous reservoirs remains a challenge in numerical reservoir simulation. In such reservoirs, detailed geological models are important to properly represent key heterogeneities. However, high computational costs and long simulation run times make these detailed models unfeasible to use in dynamic evaluations. Therefore, the scaling up of geological models is a key step in reservoir-engineering studies to reduce computational time. Scaling up must be carefully performed to maintain integrity; both truncation errors and the smoothing of subgrid heterogeneities can cause significant errors. This work evaluates the latter—the effect of averaging small-scale heterogeneities in the upscaling process—and proposes a new upscaling technique to overcome the associated limitations. The technique is dependent on splitting the porous media into two levels guided by flow- and storage-capacity analysis and the Lorenz coefficient (LC), both calculated with static properties (permeability and porosity) from a fine-scale reference model. This technique allows the adaptation of a fine highly heterogeneous geological model to a coarse-scale simulation model in a dual-porosity/dual-permeability (DP/DP) approach and represents the main reservoir heterogeneities and possible preferential paths. The new upscaling technique is applied to different reservoir-simulation models with water injection and immiscible gas injection as recovery methods. In deterministic and probabilistic studies, we show that the resulting coarse-scale dual-permeability models are more accurate and can better reproduce the fine-scale results in different upscaling ratios (URs), without using any simulation results of the reference fine-scale simulation models, as some of the current alternative upscaling methods do.

Inference of Global Reservoir Connectivity from Static Pressure Data with Fast Coarse-Scale Simulation Models

Characterization of field-scale reservoir connectivity is critical for production optimization and field development planning. The information content of the data corresponding to different physical processes, coupled with the discrepancy in production data and geologic model resolutions, complicate the estimation of global field connectivity. Due to its diffusive nature, pressure varies smoothly in the reservoir, a characteristic that can be adequately captured using coarse-scale description of the connectivity in the reservoir model. On the other hand, flowrate and watercut information are relatively more localized and require higher-resolution models to adequately describe their variations. Hence, pressure and flowrate data carry information pertinent to their respective scales. In this paper, a fast workflow is presented for estimating large-scale reservoir connectivity from static reservoir pressure measurements. For this purpose, a coarse-scale grid system with Delaunay triangulation is developed to represent the global reservoir connectivity. Flow-based upscaling is applied to create the initial coarse-scale static simulation models from geological or simulation models. The ensemble Kalman filter is then applied to calibrate the initial models against static pressure data and to identify field-scale connectivity parameters that control the global pressure distribution, such as aquifer strength, continuity/discontinuity in reservoir properties, and fault transmissibilities. This approach enables fast characterization of field-scale reservoir connectivity from pressure data with a coarser description of the reservoir connectivity, which offers a parameterization level that is commensurate with the resolution and information content of the static pressure measurements. The proposed calibration approach can serve as preconditioning to generate initial reservoir models with consistent global connectivity patterns for full-scale history matching. To illustrate the feasibility and performance of the method, examples are drawn from real field cases in which static pressure data are integrated to infer global reservoir connectivity maps. In this paper, the resulting connectivity maps are used to obtain facies probability maps to generate initial facies models for full-scale history matching.

An Efficient Robust Optimization Workflow using Multiscale Simulation and Stochastic Gradients

We present an efficient workflow that combines multiscale (MS) forward simulation and stochastic gradient computation - MS-StoSAG - for the optimization of well controls applied to waterflooding under geological uncertainty. A two-stage iterative Multiscale Finite Volume (i-MSFV), a mass conservative reservoir simulation strategy, is employed as the forward simulation strategy. MS methods provide the ability to accurately capture fine scale heterogeneities, and thus the fine-scale physics of the problem, while solving for the primary variables in a more computationally efficient coarse-scale simulation grid. In the workflow, the construction of the basis fuctions is performed at an offline stage and they are not reconstructed/updated throughout the optimization process. Instead, inaccuracies due to outdated basis functions are addressed by the i-MSFV smoothing stage. The Stochastic Simplex Approximate Gradient (StoSAG) method, a stochastic gradient technique is employed to compute the gradient of the objective function using forward simulation responses. Our experiments illustrate that i-MSFV simulations provide accurate forward simulation responses for the gradient computation, with the advantage of speeding up the workflow due to faster simulations. Speed-ups up to a factor of five on the forward simulation, the most computationally expensive step of the optimization workflow, were achieved for the examples considered in the paper. Additionally, we investigate the impact of MS parameters such as coarsening ratio and heterogeneity contrast on the optimization process. The combination of speed and accuracy of MS forward simulation with the flexibility of the StoSAG technique allows for a flexible and efficient optimization workflow suitable for large-scale problems.

A Novel Framework for Integration of Random-Walk Particle-Tracking Simulation in Subsurface Multi-phase Flow Modeling

Coarse-scale models are generally preferred in the numerical simulation of multi-phase flow due to computational constraints. However, capturing the effects of fine-scale heterogeneity on flow and isolating the impacts of numerical (artificial) dispersion, which increases with scale, are not trivial. In this paper, a particle-tracking method is devised and integrated in a scale-up workflow to estimate the conditional probability distributions of multi-phase flow functions, which can be considered as inputs in coarse-scale simulations with existing commercial packages. First, a novel particle-tracking method is developed to solve the saturation transport equation. The transport calculation is coupled with a velocity update, following the implicit pressure, explicit saturation framework, to solve the governing equations of two-phase immiscible flow. Each phase particle is advanced in a deterministic convection step according to the phase velocity, as well as in a stochastic dispersion step based on the random Brownian motion. A kernel-based formulation is proposed for computation of fluid saturation in accordance with the phase particle distribution. A novel aspect is that this method employs the kernel approach to construct saturation from phase particle distribution, which is an important improvement to the conventional box method that necessitates a large number of particles per grid cell for consistent saturation interpolation. The model is validated against various analytical solutions. Finally, the validated model is integrated in a statistical scale-up procedure to calibrate effective, or “pseudo,” multi-phase flow functions (e.g., relative permeability functions). The proposed scale-up framework does not impose any length scale requirement regarding the distribution of sub-grid heterogeneities.

Assimilating spatially dense data for subsurface applications—balancing information and degrees of freedom

The degrees of freedom (DOF) in standard ensemble-based data assimilation is limited by the ensemble size. Successful assimilation of a data set with large information content (IC) therefore requires that the DOF is sufficiently large. A too small number of DOF with respect to the IC may result in ensemble collapse, or at least in unwarranted uncertainty reduction in the estimation results. In this situation, one has two options to restore a proper balance between the DOF and the IC: to increase the DOF or to decrease the IC. Spatially dense data sets typically have a large IC. Within subsurface applications, inverted time-lapse seismic data used for reservoir history matching is an example of a spatially dense data set. Such data are considered to have great potential due to their large IC, but they also contain errors that are challenging to characterize properly. The computational cost of running the forward simulations for reservoir history matching with any kind of data is large for field cases, such that a moderately large ensemble size is standard. Realization of the potential in seismic data for ensemble-based reservoir history matching is therefore not straightforward, not only because of the unknown character of the associated data errors, but also due to the imbalance between a large IC and a too small number of DOF. Distance-based localization is often applied to increase the DOF but is example specific and involves cumbersome implementation work. We consider methods to obtain a proper balance between the IC and the DOF when assimilating inverted seismic data for reservoir history matching. To decrease the IC, we consider three ways to reduce the influence of the data space; subspace pseudo inversion, data coarsening, and a novel way of performing front extraction. To increase the DOF, we consider coarse-scale simulation, which allows for an increase in the DOF by increasing the ensemble size without increasing the total computational cost. We also consider a combination of decreasing the IC and increasing the DOF by proposing a novel method consisting of a combination of data coarsening and coarse-scale simulation. The methods were compared on one small and one moderately large example with seismic bulk-velocity fields at four assimilation times as data. The size of the examples allows for calculation of a reference solution obtained with standard ensemble-based data assimilation methodology and an unrealistically large ensemble size. With the reference solution as the yardstick with which the quality of other methods are measured, we find that the novel method combining data coarsening and coarse-scale simulations gave the best results. With very restricted computational resources available, this was the only method that gave satisfactory results.

Screening wells by multi-scale grids for multi-stage Markov Chain Monte Carlo simulation

For improved prediction of subsurface flows and representation of the uncertainties of geostatistical properties, we use the framework of Bayesian statistical interface in combination with the Markov Chain Monte Carlo (MCMC) method which needs many fine-scale simulations. Hence it is essential to apply cheap screening stages, such as coarse-scale simulation to remove irrelevant proposals of the generated Markov chain, reduce fine-scale computational cost and increase the acceptance rate of MCMC. We propose a screening step, that is examination of subsurface characteristics around injection/production wells, aiming at accurate breakthrough capturing as well as above mentioned efficiency goals. However this short time simulation needs fine-scale structure of the geological model around wells and running a fine-scale model is not as cheap as necessary for screening steps. On the other hand applying it on a coarse-scale model declines important data around wells and causes inaccurate results, particularly accurate breakthrough capturing which is important for prediction applications. Therefore we propose a multi-scale grid which preserves the fine-scale model around wells (as well as high permeable regions and fractures) and coarsens rest of the field and keeps efficiency and accuracy for the screening well stage and coarse-scale simulation, as well. A discrete wavelet transform is used as a powerful tool to generate the desired unstructured multi-scale grid efficiently. Finally an accepted proposal on coarse-scale models (screening well stage and coarse-scale simulation) will be assessed by fine-scale simulation. Accepted proposals are saved for prediction. Numerical results admit increment in acceptance rate, improvement in breakthrough capturing and significant reduction in computational cost by avoiding many forward simulations.

Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling

Numerical techniques for subsurface flow and transport modeling are often limited by computational limitations including fine mesh and small time steps to control artificial dispersion. Particle-tracking simulation offers a robust alternative for modeling solute transport in subsurface formations. However, the modeling scale usually differs substantially from the rock measurement scale, and the scale-up of measurements have to be made accounting for the pattern of spatial heterogeneity exhibited at different scales. Therefore, it is important to construct accurate coarse-scale simulations that are capable of capturing the uncertainties in reservoir and transport attributes due to scale-up. A statistical scale-up procedure developed in our previous work is extended by considering the effects of unresolved (residual) heterogeneity below the resolution of the finest modeling scale in 3D. First, a scale-up procedure based on the concept of volume variance is employed to construct realizations of permeability and porosity at the (coarse) transport modeling scale, at which flow or transport simulation is performed. Next, to compute various effective transport parameters, a series of realizations exhibiting detailed heterogeneities at the fine scale, whose domain size is the same as the transport modeling scale, are generated. These realizations are subjected to a hybrid particle-tracking simulation. Probabilistic transition time is considered, borrowing the idea from the continuous time random walk (CTRW) technique to account for any sub-scale heterogeneity at the fine scale level. The approach is validated against analytical solutions and general CTRW formulation. Finally, coarse-scale transport variables (i.e., dispersivities and parameterization of transition time distribution) are calibrated by minimizing the mismatch in effluent history with the equivalent averaged models. Construction of conditional probability distributions of effective parameters is facilitated by integrating the results over the entire suite of realizations. The proposed method is flexible, as it does not invoke any explicit assumption regarding the multivariate distribution of the heterogeneity. In contrast to other hierarchical CTRW formulation for modeling multi-scale heterogeneities, the proposed approach does not impose any length scale requirement regarding sub-grid heterogeneities. In fact, it aims to capture the uncertainty in effective reservoir and transport properties due to the presence of heterogeneity at the intermediate scale, which is larger than the finest resolution of heterogeneity but smaller than the representative elementary volume, but it is often comparable to the transport modeling scale.

Influence of Binder Porosity on Gdl Gas Phase Transport

In polymer electrolyte fuels (PEFC) carbon fiber based gas diffusion layers (GDL) are used for fine distribution of reactant gases from the flow field channels to the catalyst layer (CL). It is a key component for PEFC as it serves also for water removal from the CL as well as heat and electricity transport between CL and the flow field plates. GDL materials can be classified as woven, felts, papers, where the later type requires a binder to mechanically stabilize the fiber arrangement. While most GDL binders are solid, the binder of SGL GDLs appears porous and it’s influence on gas phase transport is unclear [1]. In SEM surface images of SGL, GDL binder domains of appear as flake like fine-structure with particles of about 100 to 300 nm (see Fig. 1a). Here, we apply multi-dimensional X-ray imaging techniques and multi-scale numerical simulations to clarify possible contributions of the binder porosity to overall GDL gas transport. Absorption contrast X-ray tomographic microscopy measurements at the TOMCAT beamline of the Swiss Light Source (SLS) with voxel sizes of minimum 0.16 µm revealed a high porosity (59 %) of SGL 24 BA binder domains of (Fig. 1b). Computational transport simulations on the segmented binder structures showed, that the binder domains provide isotropic effective diffusivity of 0.30. In order to quantify the contribution of the porous binder to the overall fluid transport in the GDL, the obtained binder diffusivity values were used in multi-scale transport simulations of ternary segmented XTM data of representative GDL samples with 2.2 µm voxel size. If the binder was treated as solid, through-plane effective diffusivity of the GDL of 0.1 was obtained. If we applied a multi-scale simulation approach, that treats the binder domain as porous and uses the transport parameters of the binder domain as input values for the coarse scale simulations of the whole GDL domain, the through-plane effective diffusivity increased to 0.4 (+300 %). These numbers will be verified by X-ray ptychograptic computed tomography measurements of the binder fine structure at the cSAXS beamline of the SLS that proved resolutions of up to 16 nm [2].

Mid-21st century air quality at the urban scale under the influence of changed climate and emissions – case studies for Paris and Stockholm

Abstract. Ozone, PM10 and PM2.5 concentrations over Paris, France and Stockholm, Sweden were modelled at 4 and 1 km horizontal resolutions respectively for the present and 2050 periods employing decade-long simulations. We account for large-scale global climate change (RCP-4.5) and fine-resolution bottom-up emission projections developed by local experts and quantify their impact on future pollutant concentrations. Moreover, we identify biases related to the implementation of regional-scale emission projections by comparing modelled pollutant concentrations between the fine- and coarse-scale simulations over the study areas. We show that over urban areas with major regional contribution (e.g. the city of Stockholm) the bias related to coarse-scale projections may be significant and lead to policy misclassification. Our results stress the need to better understand the mechanism of bias propagation across the modelling scales in order to design more successful local-scale strategies. We find that the impact of climate change is spatially homogeneous in both regions, implying strong regional influence. The climate benefit for ozone (daily mean and maximum) is up to −5 % for Paris and −2 % for Stockholm city. The climate benefit on PM2.5 and PM10 in Paris is between −5 and −10 %, while for Stockholm we estimate mixed trends of up to 3 % depending on season and size class. In Stockholm, emission mitigation leads to concentration reductions up to 15 % for daily mean and maximum ozone and 20 % for PM. Through a sensitivity analysis we show that this response is entirely due to changes in emissions at the regional scale. On the contrary, over the city of Paris (VOC-limited photochemical regime), local mitigation of NOx emissions increases future ozone concentrations due to ozone titration inhibition. This competing trend between the respective roles of emission and climate change, results in an increase in 2050 daily mean ozone by 2.5 % in Paris. Climate and not emission change appears to be the most influential factor for maximum ozone concentration over the city of Paris, which may be particularly interesting from a health impact perspective.

Local–Global Upscaling for Compositional Subsurface Flow Simulation

Compositional flow simulation is required for the modeling of many important subsurface processes including enhanced oil recovery and geological carbon storage. These simulations can be very demanding computationally, which motivates the need for efficient alternatives to detailed fine-scale flow modeling. In this work, a local–global upscaling procedure for oil–gas compositional problems is developed. With this approach, the key upscaling computations are performed over local (rather than global) fine-scale regions. The pressure boundary conditions required for these computations are determined through an approximate global coarse-scale simulation. Gas saturation and component molar fraction boundary conditions are prescribed based on the injected fluid conditions. An ‘attribute-based’ approach, which enables the efficient assignment of coarse-scale relative permeabilities and compositional functions based on quickly computed (static and single-phase flow) features, is introduced. Numerical results are presented for two- and three-dimensional models (containing eight and four components, respectively) with flow driven by wells. These results demonstrate that the local–global upscaling method, with or without the attribute-based property assignment, provides significantly more accurate predictions than standard (single-phase-parameter) upscaling methods, though accuracy is not as high as that achieved using more expensive global compositional upscaling. Speedups of about an order of magnitude, relative to fine-scale simulations, are observed using the attribute-based local–global procedure. The robustness of the upscaled models to variation in well bottom-hole pressures is also assessed. These computations demonstrate that the upscaled models retain accuracy over a range of well settings, which suggests that they will be useful in computationally demanding applications such as well control optimization.

Effective Local-Global Upscaling of Fractured Reservoirs under Discrete Fractured Discretization

The subsurface flow in fractured reservoirs is strongly affected by the distribution of fracture networks. Discrete fracture models, which represent all fractures individually by unstructured grid systems, are thus developed and act as a more accurate way for fractured reservoir simulation. However, it is usually not realistic to directly apply discrete fracture models to simulate field scale models for efficiency reasons. There is a need for upscaling techniques to coarsen the high resolution fracture descriptions to sizes that can be accommodated by reservoir simulators. In this paper, we extended the adaptive local-global upscaling technique to construct a transmissibility-based dual-porosity dual-permeability model from discrete fracture characterizations. An underlying unstructured fine-scale grid is firstly generated as a base grid. A global coarse-scale simulation is performed to provide boundary conditions for local regions and local upscaling procedures are carried out in every local region for transmissibility calculations. Iterations are performed until the consistency between the global and local properties is achieved. The procedure is applied to provide dual-porosity dual-permeability (DPDK) parameters including coarse-scale matrix-matrix, fracture-fracture and matrix-fracture flux transmissibilities. The methodology is applied to several cases. The simulation results demonstrate the accuracy, efficiency and robustness of the proposed method.

Designing inflatable structures

We propose an interactive, optimization-in-the-loop tool for designing inflatable structures. Given a target shape, the user draws a network of seams defining desired segment boundaries in 3D. Our method computes optimally-shaped flat panels for the segments, such that the inflated structure is as close as possible to the target while satisfying the desired seam positions. Our approach is underpinned by physics-based pattern optimization, accurate coarse-scale simulation using tension field theory, and a specialized constraint-optimization method. Our system is fast enough to warrant interactive exploration of different seam layouts, including internal connections, and their effects on the inflated shape. We demonstrate the resulting design process on a varied set of simulation examples, some of which we have fabricated, demonstrating excellent agreement with the design intent.

Development and application of near-well multiphase upscaling for forecasting of heavy oil primary production

Abstract Near-well effects can have a strong impact on reservoir flow. Current reservoir modeling practice often uses coarse-scale flow simulation models, which may lead to biased results, compared with fine-scale models. In this work, we extend and apply a recently developed near-well multiphase flow upscaling technique to the coarse-scale simulation of heavy-oil primary production. For heavy oils, oil viscosity is a strong function of pressure when the pressure is below the bubble point. Therefore, the upscaled mobility functions (from near-well multiphase upscaling) depend on both pressure and saturation, which cannot be directly input to general purpose reservoir simulators. This is very different than the upscaled mobility functions for typical black-oil fluids, in which oil viscosity does not vary significantly with pressure. Accordingly, the upscaled mobility functions are often equivalent to upscaled relative permeabilities (as functions of saturation only). In this work, we develop two procedures to derive either the upscaled relative permeability or viscosity functions from the phase mobility functions, thus decoupling the dependency on pressure and saturation. It is found that the upscaled oil viscosity provides more accurate predictions than the upscaled relative permeabilities, especially at early time. This is because that the rapid change of pressure at the early stage of production is captured sufficiently in the upscaled oil viscosity (as a function of pressure). The use of upscaled viscosity function in multiphase upscaling is new, and has not been presented in previous studies. We also introduce a grouping technique to reduce the number of upscaled flow functions in coarse-scale models. This is based on an observation that there is a strong correlation between the upscaled flow functions and the coarse-scale well-block permeabilities. The proposed methods are applied to realistic models from heavy-oil fields. For cases considered, the near-well multiphase flow upscaling considerably improves upon the standard coarse-scale models. The use of upscaled relative permeability and viscosity functions, as well as the grouping of upscaled flow functions, provides practical applicability of the proposed method.

Technology Focus: EOR Performance and Modeling (January 2014)

Technology Focus This is the most exciting time for enhanced oil recovery (EOR) in recent memory. At last, almost everyone is talking about increasing recovery factors, and improved oil recovery (IOR) and EOR are being considered natural components of reservoir management. Furthermore, many traditional philosophies are being openly challenged. EOR planning is happening as part of field-development plans. Proven technologies are being adapted in new, smart ways, and new technologies are constantly evolving from research to commercial applications, making EOR projects more economical. These new directions are the result of the following factors: Most fields, including the giant ones, are maturing, and producing liquid hydrocarbons is becoming more difficult in all types of reservoirs (conventional and unconventional). Leaving approximately 70% of the in-place reserves unrecovered has been challenged. There have been developments on many fronts (e.g., advanced reservoir characterization, multiphase-flow physics, smart-well and intelligent completions, recovery research, monitoring and control technologies, EOR chemicals, EOR pilot concepts, and observation-well concepts); the collective effect of all of those is going to make new EOR projects more successful. Increasing recovery factors has been considered a fully integrated multidomain activity (from pore space to separator and everything in between). Severe production decline in tight/light (unconventional) reservoirs is making the industry think about recovery challenges. EOR has been considered during field-development planning in most recent offshore oil developments. We lived through the most active period of EOR—between the mid-1970s and the mid-1980s—and learned about recovery fundamentals then. However, if we look back at that era carefully, most of the wells were single and vertical; reservoir characterization was very elementary, with no digital geological modeling capabilities; and there were no concepts such as monitoring systems or intelligent completions. All of those factors created a significant gap between what was planned in the EOR laboratory and what was achieved in field application. Other than those implemented in extremely viscous oils (heavy-oil EOR operations), projects recovered far less than planned. Overall, such recovery gave a stigma to EOR as inherently difficult and expensive. Most of the factors that made EOR difficult and expensive are gone today. That is why we will see more fields using EOR operations in upcoming years. Broadening the scope of recovery challenge to all domains will make realizations of new reserves more likely, but this change will bring additional challenges. Recommended additional reading at OnePetro: www.onepetro.org. SPE 162701 Geomechanics Considerations in Enhanced Oil Recovery by Tadesse Weldu Teklu, Colorado School of Mines, et al. SPE 166597 BP North Sea Miscible Gas Injection Projects Review by Pinggang Zhang, BP, et al. IPTC 17157 The Development of a Workflow To Improve Predictive Capability of Low-Salinity Response by B.M.J.M. Suijkerbuijk, Shell, et al. SPE 166075 The Upscaling of Discrete Fracture Models for Faster, Coarse-Scale Simulations of IOR and EOR Processes for Fractured Reservoirs by Mun-Hong Hui, Chevron, et al.

Bridging scale simulation of lattice fracture using enriched space‐time Finite Element Method

SUMMARYA multiscale method that couples the space‐time Finite Element Method with molecular dynamics is developed for the simulation of dynamic fracture problems. A coarse scale description based on finite element discretization is established in the entire domain. This representation overlaps with a detailed atomistic description employed in the region immediately surrounding the crack tip with the goal to capture the initiation and propagation of the fracture. On the basis of the crack evolution informed by the atomistic simulation, an enrichment function is introduced to represent the fracture path. The space‐time framework further enables flexible choice of time steps in different regions of interest. Coupling between the fine and coarse scale simulation is achieved with the introduction of a projection operator and bridging scale treatment. The main feature of the work is that the evolution of the crack is adaptively tracked and the enrichment in the coarse scale simulation evolves along with it. As a result, we have a moving atomistic region in the concurrent simulation scheme. The robustness of the method is illustrated through examples involving crack propagation in hexagonal lattices with different orientations and loading conditions. Copyright © 2013 John Wiley & Sons, Ltd.