bluuebunny/crossref_metadata_2025_split huggingface dataset:
System-AMG for Fully Coupled Reservoir Simulation with Geomechanics
Authors: Gries Sebastian , Metsch Bram , Terekhov Kirill M. , Tomin Pavel 
Published: March 2019

Abstract
The consideration of geomechanical effects is becoming more and more important in reservoir simulations. Ensuring stable simulation processes often enough requires handling the entire process with all types of physical unknowns fully implicitly. However, the resulting fully coupled linear systems pose challenges for linear solvers. The number of approaches that can efficiently handle a fully coupled system is extremely limited.
System-AMG has demonstrated its efficiency for isothermal and thermal reservoir simulations. At the same time, AMG is known to be a robust and highly efficient linear solver for mere linear elasticity problems. This paper will discuss the combination of the advantages that AMG approaches have for both types of physics. This results in a robust and efficient solution scheme for the fully coupled linear system. The Automatic Differentiation General Purpose Research Simulator (AD-GPRS) is used to produce the Jacobians that are guaranteed to be exact.
In a single-phase case, the overall Jacobian matrix takes the form of a constrained linear elasticity system where the flow unknowns serve as a Lagrangian multiplier. In other words, a saddle point system needs to be solved, where the flow and the mechanics problem might come at very different scales. A natural relaxation method for this kind of systems is given by Uzawa smoothing schemes which provide a way to overcome the difficulties that other smoothers may encounter.
This approach appears intuitive for single-phase problems, where Gauss-Seidel can be applied in an inexact Uzawa scheme. However, in the multiphase case, incomplete factorization smoothers are required for the flow and transport part. We will discuss the incorporation in an inexact Uzawa scheme, where different realizations are possible, with different advantages and disadvantages. Finally, we propose an adaptive mechanism along with the outer Krylov solver to detect the best-suited realization for a given linear system. In the multiphase case, also the matrix preprocessing, for instance, by Dynamic Row Summing, needs to be considered. However, the process now also needs to reflect the requirements of the Uzawa scheme to be applicable.
We demonstrate the performance for widely used test cases as well as for real-world problems of practical interest.
Validation of a Non-Uniform Coarsening and Upscaling Framework
Authors: Guion Alexandre Nicolas , Skaflestad Bård , Lie Knut-Andreas , Wu Xiao-Hui 
Published: March 2019

Abstract
We present a novel framework for generating reduced-order models that combines agglomeration of cells from existing high-fidelity reservoir models and flow-based upscaling. The framework employs a hierarchical grid-coarsening strategy that enables accurate preservation of geological structures from the underlying model. One can also use flow information to distinguish regions of high or low flow, and use this division, or other geological or user-defined quantities, to select and adapt the model resolution differently throughout the reservoir. Altogether, the framework provides a wide variety of coarsening strategies that allow the user to adapt the reduced model to important geology and explore and identify the features that most impact flow patterns and well communication. By preserving these features, while aggressively coarsening others, the user can develop reduced models that closely match an underlying high-fidelity model. Various types of simple flow diagnostics based on time-of-flight and volumetric well communication are used to predict the accuracy of the resulting reduced models.
In this paper, we systematically apply this framework to the Great White Field, but also present results from other real or synthetic models, to demonstrate the asymptotic scaling of accuracy metrics with coarsening levels. Our aim is to identify and illustrate best practices when designing and improving coarsening strategies that can guide future applications of the framework to other reservoir models. We also discuss practical limitations when applying the framework to new simulation models where flow regimes or geologic features may differ.
Parameterization of Element Balance Formulation in Reactive Compositional Flow and Transport
Authors: Kala Keshav , Voskov Denis , Huzefa Ammiwala Mohammad 
Published: March 2019

Abstract
A novel formulation for modeling nonlinear reactive-compositional transport comprising of complex phase behaviors with chemical and thermodynamic interactions is presented. The precipitation/dissolution of minerals during reactive flow in subsurface reservoirs is modeled in the newly designed simulation framework. This framework uses molar formulation with a consistent reduction of governing mass balance equations from component to element mole fractions. The thermodynamic phase behaviour is extended by including the chemical equilibrium reactions in the multiphase thermodynamic flash. This allows for a general treatment of chemical and thermodynamic equilibrium in a fully couple and implicit manner. The governing component conservation equations are reduced to element conservation equations using the Equilibrium Rate Annihilation matrix. The element composition of the mixture serves as an input for these computations whereas the output is fractions of components in each phase, including solids. To solve the resulting nonlinear element based governing equations, we use the Adaptive Operator-Based Linearization (OBL) approach where the governing equations are formulated in terms of space and state-dependent parameters. The proposed framework is utilized for modeling of several challenging flow and transport problems with dissolution and precipitation reactions. This is the first time when a multiphase multicomponent flash using element fractions as an input is coupled with an element balance compositional formulation and validated for multidimensional problems of practical interest. In addition, an efficient parametrization using adaptive OBL approach improves both robustness and performance of complex reactive-compositional flow and transport.
Insights into Development and Application of a Reservoir Simulator: A Study on Agile Processes to Enhance Simulator Testing, Benchmarking, and Deployment
Authors: Kumar Sarwesh , Talpallikar Milind , Valbuena Ernesto , Nguyen Phan , Velusamy Baskar 
Published: March 2019

Abstract
Increased complexity in reservoir models, advanced understanding of fluid flow physics, and improved computational capabilities continuously push E&P companies to improve their reservoir simulation tools and associated workflows. This paper describes a robust framework to handle two critical steps in the development of the integrated simulation workflows: simulator testing and release process to end-users companywide. Selection of benchmark models, coverage of functionalities and stress testing of the simulator, are also discussed.
Ideally, the simulation results should not change with new versions of the simulation software, but this is hardly the case. With the addition of new features, bug fixes and updates, some of the existing functionalities could behave differently - sometimes an improvement over previous results and at other times, an unintentional side-effect leading to errors. The simulator testing process starts with the automated regression test covering a wide range of features and functionalities, which is evaluated through comprehensive benchmark criteria and metrics for success/failure. Regression tests are followed by different levels of investigation to identify and resolve potential problems within each individual software component, the integrated workflows, or the simulation model itself. The web-based continuous integration tool (to collect code changes and create new builds) along with the automation helped to reduce the turnaround time for identification of issues with any code changes. This also helped to streamline and prioritize the scheduling and investigation of the regression tests based on the criticality of features under development and perform focused testing for user-preferred workflows.
Due to the increase in operational requirements, reservoir complexities, and the need to share the models with the partners, several types of simulation workflows are in practice within the company, all of which require proper testing before each new release version. Previously, sheer volume and monotonicity of the regression test process caused frequent incidences of human error; therefore, automation has not only reduced those errors but also made quality time available for in-depth analysis of the issues shortlisted by the automated process.
The paper details the testing and release framework using several real field simulation models, where the automated process resulted in timely resolution of issues, seamless transition to newer version of software/workflows, and reliable results from simulation tools. This framework also facilitated the collaboration and coordination among the cross-functional teams and provided updates on the release status and software changes to ensure a smooth and successful release.
Data-Driven Discovery of Unconventional Shale Reservoir Dynamics
Authors: Klie Hector , Florez Horacio 
Published: March 2019

Abstract
The need to deliver well-informed decisions within stringent production cycles in unconventional plays is motivating the quest for practical models that can assimilate increasing volumes of data and satisfactorily account for observed production trends. The present work introduces the extended Dynamic Mode Decomposition (EDMD) as a suitable data-driven framework for learning the reservoir dynamics entailed by flow/fracture interactions in unconventional shales. The proposed EDMD approach builds on the approximation of infinite dimensional linear operators combined with the power of deep learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single and two-phase coupled flow/geomechanics simulations and a real production dataset from Vaca Muerta unconventional shale formation in Argentina. Given relatively moderate data requirements, we show that it is possible to attain a high level of predictability from hidden field state variables and well production data. As the main conclusion of this work, EDMD stands as a promising data- driven choice for efficiently reconstructing flow/fracture dynamics that are either partially or entirely unknown, or that are too complex to formulate using known simulation tools on unconventional plays.
Coupled Numerical Simulation of Thermal-Reactive Flow and Geomechanics with Solid Mass Conversion
Authors: Alpak Faruk O. , Vink Jeroen C. 
Published: March 2019

Abstract
An extension of the novel fully coupled thermo-hydro-mechanical Open-System Geomechanics (OSG) model is developed for the analysis of coupled flow and oil-shale geomechanics. The model is cast within the framework of Biot's elasticity and classical thermoelasticity. A new term in the equations due to pyrolysis is included in the present model to capture the removal of mass from the reactive solid phase due to the kerogen conversion process modeled through a simplified chemical reaction model. The proposed novel formulation approach and its numerical implementation differs from traditional methods and offers a step improvement in the geomechanical modeling of thermally-reactive porous media, such as, oil shales.
The numerical implementation of the OSG model, a first in the literature, is developed within the framework of a proprietary coupled thermal-reactive flow and geomechanics simulator, which was extensively validated in a previous publication. In this paper, we compare the thermal-reactive OSG model against experimental measurements. Numerical results from the validation test shows that the model captures the fundamental physical behavior of oil-shale geomechanics realistically and correctly. Parametric analyses of the OSG model indicate that the chemical conversion term is the critical term that dictates the magnitude of the compaction in the solid equation and a further investigation illustrates the importance of the mobility term in the pore pressure build-up. It is also noticed that the initiation and rate of compaction of the oil-shale sample are governed by the chemical activation energy and reaction-rate constant.
Modeling the Effect of Reaction Kinetics and Dispersion during Low-Salinity Waterflooding
Authors: Magzymov Daulet , Purswani Prakash , Karpyn Zuleima T. , Johns Russell T. 
Published: March 2019

Abstract
The objective of this paper is to model low-salinity waterflooding by honoring physico-chemical complexity, namely, the effects of reaction kinetics and dispersion. Recent literature provides evidence for the potential of low-salinity water injection to improve oil recovery through wettability alteration through a complex network of reactions. However, there is lack of consensus with respect to the exact chemical species that are responsible for the alteration process. Therefore, in this study, we develop a a simplified binary multiphase reactive transport model that honors the general surface reaction for wettability alteration, but at the same time includes effects of reaction kinetics and dispersion in the governing equations.
We lump the reactants, such as sodium, calcium, and petroleum acids, into two characteristic components based on their contribution to oil or water wetness. The wettability alteration process is modelled as a competition between these primary characteristic components to occupy the rock surface as described by reaction kinetics.
The simulation results show a significant impact of reaction kinetics on the rate of wettability alteration compared to assuming instantaneous equilibrium. In the limiting case of a very slow reaction rate (Da ~ 0), low-salinity injection does not alter the wettability. Particularly, no effect on ultimate oil recovery is observed, regardless of the injected salinity level. For the case of an instantaneous reaction the ultimate oil recovery is sensitive to the injected fluid salinity. Moreover, during fast reactions (Da ~ 10-4), the wettability alteration front moves slower than the injected fluid front caused by excess salt in the solution that desorbs from the rock surface. The delay in wettability alteration is crucial to consider for an appropriate slug size design of low-salinity injection. Lastly, we observe that dispersion does not affect the ultimate oil recovery during wettability alteration as compared to reaction kinetics.
Modeling of Cosolvents in a Fully-Implicit Surfactant Flood Simulator Using the Three-Level Framework
Authors: Han Choongyong , Shi Xundan , Chang Yih-Bor , Wolfsteiner Christian , Guyaguler Baris 
Published: March 2019

Abstract
Cosolvents are commonly injected along with surfactants for successful enhanced oil recovery as they help control aqueous stability, salinity gradient, and microemulsion phase viscosity. Therefore, modeling capability for numerical simulation of cosolvent injection is essential in helping design optimal surfactant floods. Also, the numerical implementation in the simulator should be fully implicit, fully coupled, and highly-scalable to enable full-field models and the higher resolutions often required by chemical flood simulations.
We propose a novel numerical approach to model cosolvents in a fully implicit, fully coupled, parallel, four-phase surfactant flood simulator using the three-level (phase/pseudocomponent/pure component) framework. Three pseudoalcohol components are introduced to the framework for efficient modeling of surfactant phase behavior with alcohols that are partitioned to pseudooil, pseudowater, and pseudosurfactant, respectively. They consist of pure alcohol components which are partitioned to the same pseudocomponent and are distributed to phases as required by the phase behavior equations. New nonlinear solution variables of concentrations are proposed to model transport of pure alcohols, their partitioning into pseudcomponents, and distribution of the pseudoalcohols to phases, along with corresponding equations. The physical properties critical for surfactant flood simulation such as interfacial tension, phase relative permeability, viscosity, and mass density are extended to consider the effect of alcohols.
It is shown that the new numerical approach significantly simplifies implementation of the cosolvent simulation functionality. This is because time consuming and error prone conversion between variables and derivatives, and local iterative solve for the concentrations, are not needed. This simplification enables us to significantly reduce implementation efforts, even within the fully implicit, fully coupled framework. The implementation is validated with various test cases against a widely referenced chemical flood simulator. A large-scale surfactant/polymer flood case with cosolvent injection is successfully simulated with all the important physical processes modeled, with the simulator exhibiting good performance.
Large field scale, four-phase chemical flood simulations with surfactant phase behavior with cosolvents are now practically achievable with the novel numerical approach using the three-level framework without compromising comprehensive physics.
A Comprehensive Adaptive Forecasting Framework for Optimum Field Development Planning
Authors: Salehi Amir , Hetz Gill , Olalotiti Feyisayo , Sorek Nadav , Darabi Hamed , Castineira David 
Published: March 2019

Abstract
An integral aspect of smart reservoir management of oil and gas fields is the process of identifying and performance forecasting of the remaining, feasible, and actionable field development opportunities (FDOs). In the present work, we introduce an adaptive full-physics simulation-based forecasting framework that applies a series of cutting-edge technologies to provide short- and long-term forecasts for both field- and well-level performance. Our workflow can be applied to a comprehensive opportunities inventory including behind-pipe recompletion, infill drilling, and sidetrack opportunities. In our approach, we begin with a model order reduction technique, which involves a parsimonious elimination of redundancies existing in a given geologic model. This involves an adaptive model upscaling strategy that retains fine details in the vicinity of critical geological features by locally varying the resulting model grid resolution. Reduced models, which are validated using streamline-based flow metrics, are passed into an automated sensitivity study and model calibration engine for efficient reconciliation of observed production trends in the field. Here, we apply a recently proposed Ensemble Smoother robust Levenberg- Marquardt (ES-rLM) method to generate plausible model realizations that replicate the reservoir energy. Representative models are further improved in a sensitivity-based local inversion step to match multiphase production data at the well level. An approach alternative to streamlines, which is compliant with a general unstructured grid format, is utilized to directly compute production data sensitivities on the underlying grid in the local inversion module. Finally, calibrated models are directly passed to the optimization and forecasting engine to assess and optimize field opportunities and development scenarios. This framework has been successfully applied to several giant mature assets in the Middle East, North America, and South America. A case study for one of the giant reservoirs in Latin America is presented where hundreds of field development opportunities are initially identified. We then apply our forecasting framework to the various scenarios including all opportunities to deliver the optimum field development plan. We propose a systematic workflow for field-scale modeling and optimization using an adaptive framework. Our approach facilitates a flexible framework to rapidly generate reliable forecasts and quantify associated uncertainties in a robust manner. This advantage in flexibility and robustness is tied to our fast and automated two-stage model calibration module that leads to substantial savings in computational time. This makes it an efficient method for quantifying the uncertainty as demonstrated through improved estimation of the faults’ connectivity, permeability distribution, fluid saturation evolution, and swept volume.
An Interactive Workflow and Data Analytics for Model-Based Production Optimization: A Waterflooding Example
Authors: Fu Jianlin , Libby Lauren 
Published: March 2019

Summary
Maximization of the yield of existing assets becomes more important than ever as the petroleum companies need to win in any business environment. In this context, model-based optimization technology plays an important role in managing efficiently the subsurface flow and can add significant values by maximizing the potential of reservoirs without a large capital investment. Yet, conventional optimization methods did not sufficiently respect expert knowledge and engineering requirements, which severely undermines their business impact in practice. This paper presents a novel interactive workflow that permits injection of expert knowledge into optimization process and ensures the final optimal solution executable. This workflow is unique because it allows to (i) interact with stakeholders, e.g., production engineers and operators, to capture engineering and economic requirements and constraints, (ii) interact with software to identify, screen, and maximize the opportunity, (iii) interact with reservoir to understand the physics for meaningful solutions, and (iv) interact with candidate solutions for the most rigorous one. Data analytics is used in this interactive workflow, boosting the optimization progress to reach the most trustworthy result. An offshore waterflooding example is used to illustrate the workflow proposed. Results show that the optimal solution generated significantly improves, compared to the existing strategy, the estimated short-term and long-term oil recovery (by more than 2% and 6%, respectively). Moreover, the water production volume is largely reduced. The proposed solution is feasible in engineering (meet engineering requirements and engineers’ judgements and expectations), meaningful in physics, optimal (convergence is guaranteed), robust (multiple uncertainties are considered), stable (immune to potential implementation errors), trustworthy (backed by data analytics), and thus executable in practice.