bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Design of CO2 Sequestration with Three-Way Coupling of Flow-Geomechanics Simulations and Evolution Strategy
Authors: Lu Xueying , Ganis Benjamin , Wheeler Mary F.
Published: March 2019

Coupled reservoir flow and geomechanics has numerous important applications in the oil & gas industry, such as land subsidence, hydraulic fracturing, fault reaction and hydrocarbon recovery etc. High fidelity numerical schemes and multiphysics models must be coupled in order to simulate these processes and their interactions accurately and efficiently. Specifically, in the applications of CO2 sequestration, the effect of geomechanics on carbon storage estimation is not negligible. However, coupled flow-geomechanics simulations are very computationally expensive and most of the computational time is usually spent for geomechanics calculations. This paper investigates a three-way coupling algorithm that uses an error indicator to determine when displacement must be updated and whether fixed-stress iterative coupling technique is required. Numerical experiments with coupled nonlinear single-phase flow and linear poromechanics shows that the three-way coupling algorithm can speed up 4 times comparing to fixed-stress iterative coupling algorithm. Extensions to coupled compositional flow with poromechanics also shows a speed-up for 5 times for continuous CO2 sequestration applications and 2 times for surfactant-alternating-gas applications (SAG). The substantial speed up makes the three-way coupling algorithm of flow and geomechanics feasible in the large-scale optimizations. Based on the three-way coupling of compositional flow and geomechanics, we experimented two black box optimization algorithms, covariance-matrix adaptation evolution strategy (CMA-ES) and genetic algorithm (GA), for the optimization of well controls during SAG process to maximize CO2 storage volume. CMA-ES outperforms GA in that it is more robust, and it achieves higher objective function value in less simulation runs. The optimized SAG process achieves 27.55% more CO2 storage volume and reduces water and surfactant consumption by 54.84%.
General Semi-Structured Discretization for Flow and Geomechanics on Diffusive Fracture Networks
Authors: Jammoul Mohamad , Ganis Benjamin , Wheeler Mary
Published: March 2019

A novel approach is introduced for simulation of multiphase flow, geomechanics, and fracture propagation on very general semi-structured grids. Complex networks consisting of both natural and hydraulically stimulated fractures are able to be represented using a diffusive zone model in large scale reservoirs. A mass conservative method called the enhanced velocity mixed finite element method is used to model multiphase flow with a fully-compositional equation-of-state model. Its recent reformulation on semi-structured, spatially non-conforming grids allows very general local refinement and dynamic mesh adaptivity.

Iteratively coupled geomechanics is simulated, which can predict fracture opening on fixed networks based upon induced stresses and poromechanical effects. In the most complex case, it is coupled with the phase field method to model nucleation and branching of non-planar fractures in highly heterogeneous media. Several examples are demonstrated to model fracture networks. The general semi-structured discretization can simulate flow and geomechanics on networks of fractures in large reservoirs with local resolution where desired. Dynamic adaptive mesh refinement can be used for both tracking transient flow features such as sharp the propagation of new fractures via hydraulic stimulation. This framework allows the seamless ability to switch from production to propagation scenarios, by varying the degrees of physics.

This work demonstrates a capability to perform high-fidelity simulations on complex fracture networks in large reservoirs at a reasonable computational cost. The gridding algorithms are straightforward extensions to traditional finite difference reservoir simulators. It can also be coupled with state-of-the-art complex phase field fracture propagation. This extends the capabilities of many legacy reservoir simulators to handle more physics.
Boundary Segments for Multisegment Well Models
Authors: Stone Terry , McKee Francois , Istchenko Christopher
Published: March 2019

Petroleum recovery from oilfield assets increasingly involves wells that are very long in extent and have multiple laterals, multiple tubing strings and multiple control points to prevent breakthrough of unwanted fluids and/or to optimize recovery. Instead of simply controlling rates at the wellhead, downhole devices are now available where apertures and other controlling parameters can be set statically, autonomously, or through surface intervention,. Having various control points in a wellbore that may include numerous flow paths requires a flexible setup and robust algorithms to effectively set all local constraints at various measured depths. This paper describes special constructs called "boundary segments" with a similar set of flow rate and pressure control modes to those available for tubinghead or bottomhole well control. In a multisegment well model whose topology consists of a set of nodes with intrinsic properties such as pressure, global mole fractions, total enthalpy, saturations, etc. and a set of pipes with attributes of length, volume, and a flow rate, these special segments share an existing node but have their own unique pipe together with boundary conditions and an accompanying set of control modes.

Boundary segments are highly flexible, elegant, easy to implement, and useful in a variety of cases. This paper will provide reservoir simulation engineers and developers with an understanding of a simple method to calculate primary well control at the surface choke together with multiple downhole constraints from devices and tubing strings.
Implementation of Physics-Based Data-Driven Models With a Commercial Simulator
Authors: Ren Guotong , He Jincong , Wang Zhenzhen , Younis Rami M. , Wen Xian-Huan
Published: March 2019

The use of full-physics models in close-loop reservoir management can be computationally prohibitive as a large number of simulation runs are required for history matching and optimization. In this paper we propose the use of a physics-based data-driven model to accelerate reservoir management and we describe how it could be implemented with a commercial simulator.

In the proposed model, the reservoir is modeled as a network of 1D flow paths connecting perforations at different wells. These flow paths are discretized and the properties at each grid block along each flow path are derived from history matching of production data. To simulate flow in this network model through a commercial simulator with all the physics, an equivalent 2D Cartesian model is set up in which each row corresponds to one of the 1D flow paths. Finally, the history matching is performed with ensemble smoother with multiple data assimilation (ESMDA).

The proposed network model is tested on both waterflood and steamflood problems. It is demonstrated that the proposed model matches with well-level production history (including pressure and phase flow rate) well. The calibrated ensemble from ESMDA also provided a satisfactory probabilistic forecast of future production that almost always envelops the true solutions. This indicates that the proposed model, after calibrated with production data, is accurate enough for production forecast and optimization. In addition, the use of commercial simulator in the network model provided flexibility to account for complex physics, as demonstrated by the successfully application to the steamflood problem. Compared with traditional workflow that goes through the full cycle of geological modelling, history matching and probabilistic forecasting, the proposed network model only requires production data and can be built within hours. The resulted network model also runs much faster than a full-physics as it typically has much less grid blocks. We expect the proposed method to be most useful for mature fields when abundant of production data is available.

As far as we know, this is first time a physics-based data-driven model is implemented with a commercial simulator. The use of commercial simulator makes it easy to extend the model for complex reservoir such as thermal or compositional reservoirs.
Closed-Loop Stochastic Oilfield Optimization Under Uncertainty in Geologic Description and Future Development Plans
Authors: Jahandideh Atefeh , Jafarpour Behnam
Published: March 2019

Development and management of oilfields involve several sources of uncertainty that complicate an already challenging decision-making process. Two main sources of uncertainty are related to geologic description of reservoirs and future development scenarios. While geologic uncertainty has been widely studied and robust optimization methods have been developed to account for it, the uncertainty in future development plans has not been considered in optimization problems. Future development strategies have been included as decision variables in field development optimization problems. However, in practice, future field development plans tend to deviate from the solutions obtained in past optimization problems. Therefore, a more prudent and realistic approach toward oilfield optimization is to consider the uncertainty in both geology and future development plans to obtain robust solutions. We develop a closed-loop stochastic field development optimization formulation to account for the uncertainty in geologic description and future infill drilling scenarios. The proposed approach optimizes the decision variables for current stage of planning (e.g. well locations and operational settings) while accounting for geologic and future development uncertainties, where the former uncertainty is represented by using several reservoir model realizations while the latter uncertainty is represented through drilling scenario trees and probabilistic description of future drilling events/parameters. In the developed method, prior to each decision-making stage the reservoir is operated based on the current optimal strategy until dynamic data becomes available to calibrate the geological models. After each data assimilation step, a new optimization is performed to adjust controllable decision variables for the current well configuration (e.g., well rates or BHPs) using the updated models and potentially revised future development scenarios. Using a multi-stage stochastic optimization workflow this process is repeated after each decision stage. Several numerical experiments are presented to discuss various aspects of the proposed closed-loop stochastic optimization formulation and to compare the solutions from different methods adopted for treatment of future development plans. The results indicate that stochastic treatment of future development events (1) can hedge against uncertain future development activities by obtaining optimization solutions that are robust against changes in future decisions, and (2) considerably reduces the performance losses that can result from field development when uncertainty is disregarded.
Coupled Numerical Simulation of Transient Microseismicity and Flow in Fractured Reservoirs
Authors: Han Zheng , Ren Guotong , Younis Rami M.
Published: March 2019

An XFEM-EDFM scheme and associated monolithic solution method are proposed to model time-dependent poromechanics and two-phase flow. Fractures are modeled as interfaces with displacement discontinuities. The contact forces are treated using Lagrange Multipliers. A number of numerical tests are performed to investigate the Newmark scheme's accuracy and cases for wave propagation in poroelastic and natural fracture media are implemented to evaluate computational efficiency. We apply the method to model seismic data from hydraulic fracture network. Empirical results validate the Newmark scheme accuracy as well as computational efficiency and localization of newton update in seismic field is necessary for the further application. The synthetic model of multiple hydraulic stages illustrates the effect of flow coupling and newly generated fractures on the microseismic field. The model is applied to simultaneously assimilate well performance and microseismic observations, thereby informing about the causal event dynamics.
Ranking Fractured Reservoir Models Using Flow Diagnostics
Authors: Spooner Victoria , Geiger Sebastian , Arnold Dan
Published: March 2019

This paper describes the application and testing of innovative dual porosity flow diagnostics to quantitatively rank large ensembles of fractured reservoir models. Flow diagnostics can approximate the dynamic response of multi-million cell models in seconds on standard hardware. The need for new faster screening methods stems from the challenge of making robust forecasts for naturally fractured carbonate reservoirs. First order uncertainties including the distribution and properties of natural fractures, matrix heterogeneity and wettability can all negatively impact on recovery. A robust multi-realisation approach to production forecasting is often rendered impractical due to the time cost for simulating many models.

We have extended existing flow diagnostics techniques to dual porosity systems by accounting for the matrix-fracture exchange. New metrics combine the transfer rate with the advective time of flight in the fractures identifying risk factors for early water breakthrough and providing quantitative measures of dynamic heterogeneity.

We have compared ranking a large ensemble of synthetic fractured reservoir models using dual porosity flow diagnostics and using full-physics simulation. The synthetic ensemble explores a number of different geological concepts around the fracture distributions, wettability and matrix heterogeneity which can. Not only does the flow diagnostic ranking agree well with the cumulative oil ranking the run time for the flow diagnostics is <0.25% of the total simulation time. This significant reduction in the time to compare models allows more time to spend running full physics simulation on the important and geologically diverse cases that offer the most insight.
Numerical Simulation of Matrix Acidizing in Fractured Carbonate Reservoirs Using Adaptive Enriched Galerkin Method
Authors: Dong Rencheng , Lee Sanghyun , Wheeler Mary
Published: March 2019

Acidizing in un-fractured carbonate reservoirs has been well studied through modeling and simulation. Since carbonate reservoirs are often naturally fractured, fractures should be modeled for realistic acidizing operations. We present adaptive enriched Galerkin (EG) methods to simulate acidizing in fractured carbonate reservoirs. We adopt a two-scale continuum model for the acid transport. The coupled flow and reactive transport systems are spatially discretized by EG methods. Fractures are introduced using local grid refinement (LGR) technique. Adaptive mesh refinement (AMR) is implemented around wormhole interfaces. Simulation results show that acidizing in fractured carbonate reservoirs is largely dependent on the fracture system while acidizing in unfractured carbonate reservoirs is mainly determined by operation parameters such as acid injection rate. Computationally, the proposed EG scheme has less numerical dispersion and grid orientation effects than standard cell center finite difference/volume methods. AMR is very efficient to track the wormhole growth and speed up acidizing simulations.
Tight Oil and Shale Gas PVT Modelling for Flow Simulation with Matrix-Fracture Interaction
Authors: Sobecki Nicolas , Wang Shihao , Ding Didier-Yu , Nieto Draghi Carlos , Wu Yu-Shu
Published: March 2019

Tight oil and shale gas reservoirs have a significant part of their pore volume occupied by micro (below 2nm) and mesopores (between 2 and 50nm). This kind of environment creates strong interactions forces in the confined fluid with pore walls as well as between its own molecules and then changes dramatically the fluid phase behavior and its thermodynamic properties. Pressure-Vapor-Temperature (PVT) modeling of such fluids becomes therefore a challenge in order to get accurate production forecast reservoir simulations. Furthermore along the flow from the matrix to the well through the fractures, the fluid will pass through a very heterogeneous pore size distribution which will alter it differently according to the pore size and the spatial distribution. An important work has therefore to be done on developing upscaling methodology of the pore size distribution for large scale reservoir simulations. Firstly molecular simulations will be performed on pure components and mixtures in order to get reference thermodynamic properties at liquid/vapor equilibrium for different pore sizes. The comparison with commonly used modified equation of state (EOS) in the literature highlighted the model of flash with capillary pressure and critical temperature and pressure shift as the best one to match reference molecular simulation results. Secondly fine grid matrix/fracture simulations have been built and performed for different pore size distributions. The study has shown that the pore size distribution has an important impact on reservoir production and that this impact is highly dependent of the volume fraction of nanopores inside the matrix. Capillary pressure heterogeneity and pore radius dependent EOS cause gas flow slowdown or gas trapping inside the matrix and postponed gas flow apparition in the fractures during depletion which reduce the GOR (Gas-Oil Ratio) at the well. Coarse grid upscaling models have then been performed on the same synthetic case and compared to the reference fine grid results. The commonly used upscaling methodology of dual porosity model with average pore radius for the pore size distribution is unable to match the fine grid results. A new triple porosity model considering fracture, small pores and large pores with their own capillary pressure and EOS, together with MINC (Multiple Interacting Continua) approach, has shown very good match with the reference fine grid results. Finally a large scale stimulated reservoir volume with different pore size distribution inside the matrix has been built using the upscaling method developed here. The proposed triple porosity methodology is able to model the PVT of the confined fluid and its flow across a very heterogeneous pore size distribution up to the well through fractures in a large scale reservoir simulation.
Higher Resolution Unstructured Spectral Finite-Volume Method for Flow in Porous Media
Authors: Xie Yawei , Edwards Michael G.
Published: March 2019

A novel higher resolution spectral volume method coupled with a control-volume distributed multi-Point flux approximation (CVD-MPFA) is presented on unstructured triangular grids for subsurface reservoir simulation. The flow equations involve an essentially hyperbolic convection equation coupled with an elliptic pressure equation resulting from Darcy's law together with mass conservation. The spectral volume (SV) method is a locally conservative, efficient high-order finite volume method for convective flow. In 2D geometry, the triangular cell is subdivided into sub-cells, and the average state variables in the sub-cells are used to reconstruct a high-order polynomial in the triangular cell. The focus here is on an efficient strategy for reconstruction of both a higher resolution approximation of the convective transport flux and Darcy-flux approximation on sub-cell interfaces. The strategy involves coupling of the SV method and reconstructed CVD-MPFA fluxes at the faces of the spectral volume, to obtain an efficient finer scale higher resolution finite-volume method which solves for both the saturation and pressure. A limiting procedure based on the Barth-Jespersen limiter is used to prevent non-physical oscillations on unstructured grids. The fine scale saturation/concentration field is then updated via the reconstructed finite volume approximation over the sub-cell control-volumes. The method is also coupled with a discrete fracture model. Performance comparisons are presented for tracer and two phase flow problems on 2D unstructured meshes including fractures. The results demonstrate that the spectral-volume method achieves further enhanced resolution of flow and fronts in addition to that of achieved by the standard higher resolution method over first order upwind, while improving upon efficiency.
A General Framework Model for Fully Coupled Thermal-Hydraulic-Mechanical Simulation of CO2 EOR Operations
Authors: Wang Shihao , Di Yuan , Wu Yu-Shu , Winterfeld Philip
Published: March 2019

In this work, we present the development of a comprehensive mathematical formulation and reservoir simulator for thermal-hydraulic-mechanical simulation of CO2-EOR processes

We adopt the integral finite difference method to simulate coupled thermal-hydraulic-mechanical processes during CO2-EOR in conventional and unconventional reservoirs. In our method, the governing equations of the multiphysical processes are solved fully coupled on the same unstructured grid. A multiscale algebraic linear solver is adopted to speed up the non-isothermal flow calculation. Inspired by the meshless method, the algebraic solver eliminates the low-frequency terms through smoothing on a coarse grid. In order to simulate the phase behavior of a three-phase system, a three-phase flash calculation module, based on direct minimization of Gibbs energy, is implemented in the simulator.

We have investigated the impact of cold CO2 injection on injectivity as well as on phase behavior. We conclude that cold injection is an effective way to increase injectivity in tight-oil reservoirs. We have observed and studied the temperature decreasing phenomena near the production well, known as the Joule-Thomson effect, induced by expansion of in-situ fluids.

The novelty of this work lies in the fully coupled simulation scheme, including non-isothermal effects on CO2-EOR processes and recoveries, which has been ignored in almost all modeling studies of CO2-EOR. The multiscale solution strategy and the unique phenomena of non-isothermal compositional modeling coupled with geomechanics are captured by our simulator.
Recent Developments in Unstructured Aggregation-Based Upscaling for Simulation Workflows and Applications
Authors: Thomas Sunil , Du Song , Dufour Gaelle , Mallison Brad , Muron Pierre , Rey Alvaro
Published: March 2019

New developments in unstructured aggregation-based upscaling are presented that improve the flexibility of coarsening designs and enable a more integrated reservoir simulation workflow. Field cases and synthetic tests demonstrate the advantages of the method compared to legacy upscaling methods and fine scale simulations.

Aggregation-based upscaling has recently emerged as a favorable alternative to conventional upscaling methods in reservoir simulation workflows. We outline these developments and describe algorithms used to compute flexible aggregation schemes, coarse transmissibility, and upscaled well indices. The main value additions are,

the ability to selectively coarsen and adapt areal and vertical resolution based on geological features, areas of interest, and/or stratigraphic layer metrics resulting in improved accuracy, the improved simplicity and robustness resulting from avoiding the explicit creation of coarse grids and maintaining one grid for earth modeling and reservoir simulation workflows, and the broad applicability to fields modeled by many grid types including unstructured grids and discrete fracture models.

The aggregation-based upscaling methodology is tested in the simulation of some synthetic benchmarks, and of full field models. Comparisons are provided to fine scale simulations in each case, and to legacy upscaling simulations, wherever practically feasible. The most important findings are the seamless integration afforded by the new workflow by eliminating the need for the coarse simulation grid, the significant savings in user interaction time and computational time, and the overall improvement in accuracy, when compared to legacy upscaling workflows. This is important because reservoir engineers operate on tight deadlines to complete projects, and because the logistical challenges of handling fine and coarse grids are significant for studies that involve multiple reservoir model realizations.
Robust Multi-Objective Field Development Optimization for the Mariner Asset
Authors: Hanea Remus Gabriel , Bjorlykke Ole Petter , Hashmi Yastoor , Feng Tao , Fonseca Rahul Mark
Published: March 2019

In this paper we present our results, challenges and learnings, over a two-year period wherein robust multi-objective optimization was applied at the Mariner asset which is being currently developed. Many different problems were solved with different objectives. These problems were formulated based on the phases of planning and development at the asset. The optimization problems include drilling order and well trajectory optimization as the main objectives with reduction in water cut and reduction of gas production to minimize flaring as secondary objectives. We use the efficient stochastic gradient technique, StoSAG, to achieve optimization incorporating geological and petrophysical uncertainty. For some problems computational limitations introduced challenges while for other problems operational constraints introduced challenges for the optimization.

Depending on the problems significant increases between 5% and 20% in the expected value of the objective function were achieved. For the multi-objective optimization cases we show that non-trivial optimal strategies are obtained which significantly reduce (40% decrease) gas production with minimal loss (less than 1%) in the economic objective. Our results illustrate the importance of flexible optimizations workflows to achieve results of significant practical value at different stages of the planning and development cycle at an operational asset.
System-AMG for Fully Coupled Reservoir Simulation with Geomechanics
Authors: Gries Sebastian , Metsch Bram , Terekhov Kirill M. , Tomin Pavel
Published: March 2019

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

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

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

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

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

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

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

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

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.