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

SPE Journal ◽  
2020 ◽  
Vol 25 (04) ◽  
pp. 1981-1999 ◽  
Author(s):  
Victor S. Rios ◽  
Luiz O. S. Santos ◽  
Denis J. Schiozer

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.

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.


2005 ◽  
Vol 8 (03) ◽  
pp. 189-195 ◽  
Author(s):  
Mun-Hong Hui ◽  
Dengen Zhou ◽  
Xian-Huan Wen ◽  
Louis J. Durlofsky

Summary To better design and manage miscible gas injection, a fast and accurate coarse-scale miscible simulation capability is required. In this paper, we present a new technique for the upscaling of first-contact miscible displacements. The method comprises two components: effective flux boundary conditions (EFBCs) and the extended Todd and Longstaff with upscaled relative permeabilities (ETLU) formulation. The former accounts approximately for the effects of the global flow field on the local upscaling problems, while the latter modifies the way that effective fluid properties and upscaled relative permeabilities are computed so that effectively residual oil is properly represented. For a sequence of partially layered, synthetic 2D permeability fields, the technique is shown to be successful in reproducing reference fine-scale solutions. The method is also shown to outperform other upscaling techniques over a wide range of coarsening factors. The upscaling procedure is then applied to a 3D simulation of a miscible gas-injection field study. A near-well upscaling technique is also incorporated into the methodology. We show that the new approach provides coarse-scale simulation results that match the reference solutions closely. In addition, the technique is shown to be very efficient computationally. Introduction In many oil fields with significant amounts of associated gas, miscible gas injection is a potentially attractive recovery method because it can yield high local displacement efficiencies and may also offer a solution for gas handling. For an accurate estimation of the displacement efficiency, complex phenomenalike viscous fingering need to be modeled properly. There are two broad categories of approaches to modeling miscible displacements: fully compositional (FC) and limited compositional (LC). For multicontact miscible processes, FC simulations are generally required. However, fine-scale FC simulations of miscible processes are prohibitively time-consuming. While compositional streamline techniques may eventually address many of the computational difficulties, several issues (e.g., gravity, compressibility, and streamline updating) have yet to be fully resolved. When first-contact miscibility is applicable, the LC formulation may be preferable because of its computational efficiency. The LC formulation allows the simulator to model miscibility within a black-oil framework and empirically accounts for viscous fingering by modifying the fluid properties of the pseudophases. However, because fine-scale LC simulations are still computationally demanding, there remains a clear need for a robust miscible upscaling technique. In this work, we present a novel upscaling technique for the fast and accurate coarse-scale simulation of first-contact miscible displacements. Our method is an LC approach that has two components: the use of EFBCs for the calculation of upscaled (pseudo-) relative permeabilities and the ETLU formulation. EFBCs incorporate some approximate global flow information into the local upscaling calculations and appropriately suppress the flux through high-permeability streaks that are not continuous throughout the domain. As a result, EFBCs address the problem of premature breakthrough of injected fluid, which can occur because of the overestimation of flux that results from the use of standard boundary conditions. Our ETLU formulation extends the Todd and Longstaff method by accounting for the fact that, within reservoir-simulation length scales, there exists an amount of oil that is practically immobile and not available for mixing (Sorb). The computation of effective fluid properties and upscaled relative permeabilities, therefore, should not include this Sorb. This concept in fact leads to the improved behavior of the upscaled relative permeabilities. Previous miscible upscaling approaches entailing upscaled relative permeabilities neither included the Sorb concept nor used any specialized boundary conditions such as EFBCs.


Author(s):  
Klaus Rollmann ◽  
Aurea Soriano-Vargas ◽  
Forlan Almeida ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
...  

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