bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Method for Multi-Resolution Earth Model Gridding
Authors: Suter Erich , Friis Helmer André , Vefring Erlend H. , Kårstad Terje , Escalona Alejandro 
Published: February 2017

Abstract
Earth models are important tools for support of decision making processes for optimal exploitation of subsurface resources. In earth modelling applications, massive amounts of information result from collecting and interpreting measurements and simulating physical scenarios at various scales and resolutions. The size of an earth model grid is a consequence of its scale and resolution. The grid size is a major factor for the computational efficiency when managing the model and performing various model based simulations. The grid is controlled by the geological structure, and the resolution of the grid is therefore controlled by the resolution of the geological structure (the number of structural elements).
We discuss principles for a multi-resolution framework for earth model gridding where the aim is to allow automatic, local control of the resolution of a populated grid by locally controlling the resolution of the geological structure. The structure is ordered in a hierarchical manner, and splits the subsurface into a nested set of regions that are ordered in a corresponding hierarchical fashion. Each region can be separately gridded. The method is based on a recent method for local updates of the connectivity (topology) of the geological structure and of the resolution of a populated grid. Similar to existing strategies for multi-resolution modelling in other sciences and applications, the proposed method allows local control with the trade-off between numerical accuracy and grid size. The method should allow to locally change the model resolution even during a modelling exercise; subsurface volumes of high interest can be represented at fine resolution, whereas in volumes of less interest, details can be omitted. The method also enables local updates and local scale uncertainty management of the geological structure.
The aim of the proposed method is to develop an effective methodology that supports real-time earth model based workflows such as geosteering where multiple grid realizations are never fixed and always updated with the most recent measurements and interpretations, and where each realization is always kept at an optimal resolution.
Coupled Geomechanics and Flow Simulation on Corner-Point and Polyhedral Grids
Authors: Andersen Odd , Nilsen Halvor MøII , Raynaud Xavier 
Published: February 2017

Abstract
Reservoir geology has traditionally been described using corner-point grids. For such grid and general polyhedral grids, two-point flux-approximation (TPFA) schemes, or modifications thereof, are simple and robust methods which, despite grid-orientation errors and a general lack of consistency, have been considered sufficient for simulation in light of the inherent geological uncertainty. However, there is no equivalent methods to simulate elasticity, and this has hampered flexible coupling between grids made for flow simulations and grids made for geomechanical calculations.
Using the newly developed virtual element method (VEM) for mechanics on polyhedral grids, we show how geomechanics simulations on corner-point grids can be coupled with traditional discretizations (and solvers) used in black-oil and compositional simulators. The new VEM method can be seen as an extension of finite element methods to general grids. In a first order VEM method, the energy of the non-linear basis functions is not computed exactly, as in FEM, but approximated by introducing a regularisation term. This may result in large error, especially for grids with high aspect ratio. We discuss the implications of the use of VEM for applications to reservoir modeling.
We will primarily use Biots type coupling restricted to linear elasticity. However, we also discuss how nonlinear coupling can be practically introduced using Automatic Differentiation (AD), which is the numerical technique of choice in several modern research simulators. The AD framework typically enables automatic evaluations of the derivative of vector functions. The difficulty in the case of nonlinear couplings with mechanics is that many of the important physical quantities involved, such as the mechanical properties and the stress, naturally live on cells, so that the discretization operators themselves becomes nonlinear quantities that have to be differentiated. Accurate and robust treatment of these challenges are discussed.
Computational cost is a major challenge when coupling geomechanical effects with flow simulation. First of all, the mechanical system needs to include the overburden and the rocks surrounding the reservoir. Secondly, mechanics in its simplest form is described by a vector-Laplace equation, which results in an additional elliptic system that is approximately three times larger than the pressure part of the flow equations. Several decoupling techniques have been introduced to overcome this challenge. We show how such methods can easily be implemented directly on the corner-point or polyhedral grids with the proposed VEM discretization. To showcase the coupling of geomechanics and flow simulation, we use a flexible open-source prototyping framework, which has been benchmarked against state-of-the-art commercial and research simulators.
A Robust Iterative Ensemble Smoother Method for Efficient History Matching and Uncertainty Quantification
Authors: Ma Xiang , Hetz Gill , Wang Xiaochen , Bi Linfeng , Stern Dave , Hoda Nazish 
Published: February 2017

Abstract
Many recent developments in generating history matched reservoir models that approximately characterize subsurface uncertainty are associated with the ensemble smoother (ES) method. It is much better suited for practical history matching applications because it does not require updating of the dynamical variables and thus the frequent simulation restarts required by ensemble kalman filter (EnKF) are avoided. However, the performance of original single update scheme of ES is poor for strongly nonlinear problems and therefore iterations may be needed. Several iterative forms of ES were proposed in the past few years, most of which combine ideas from random maximum likelihood (RML) and ensemble-based techniques. Unlike previous implementations, we pose the history matching problem as a full nonlinear least squares optimization problem and classical Levenberg-Marquardt (LM) algorithm is used as the optimization solver. By showing the that solution of the linearized least squares subproblems arising from each iteration has similar structure to that of standard ES update equation, we propose to use ES as the linear least squares solver to avoid the expensive adjoint calculation. In this way, the proposed algorithm can be considered as an iterative ES and the regularization parameter can be updated following the standard LM rule. Furthermore, because it is casted as an optimization problem, it is straightforward to extend it to robust nonlinear least squares method that can automatically estimate the measurement noise level and reduce the effect of outliers in the data that is essential for field applications. Two synthetic reservoir models are used to showcase the effectiveness and robustness of the newly developed algorithm.
Parallel Enriched Algebraic Multiscale Solver
Authors: Manea A. M. , Hajibeygi H. , Vassilevski P. , Tchelepi H. A. 
Published: February 2017

Abstract
A Parallel Enriched Algebraic Multiscale Solver (PEAMS) for simulation of flow in heterogeneous formations with high contrasts is introduced. Built on the recently developed enrichment strategy for single processing algorithms, i.e., EAMS of Manea et al. (2016), the PEAMS describes an efficient parallel implementation procedure as to how to enrich a given multiscale formulation with additional local basis functions. These additional basis functions, constructed in parallel computational platform, aim to resolve large error components for a generic fine-scale system with no right-hand-side term. The design and computational overhead of the enrichment kernels in shared-memory parallel environments are discussed in detail. The robustness and scalability of PEAMS are then illustrated for highly heterogeneous and anisotropic 3D multi-milion-cell reservoir models. The presented results show that, by adding only a few locally- supported complementary basis functions, the convergence of the original multiscale method is significantly enhanced. This is achieved with incurring a marginal overhead in the complexity to the coarse-scale operator. Moreover, in shared-memory parallel environments, it is shown that both of the enrichment procedure and the resulting enriched solver are scalable. Therefore, PEAMS casts a promising framework for robust iterative multiscale formulations for real-field applications, where parallel processing architectures are essential.
C1-Continuous PPU Schemes for Efficient Simulation of Fully-Coupled Multi-Phase Flow with Gravity
Authors: Jiang Jiamin , Younis Rami M. 
Published: February 2017

Abstract
In the presence of counter-current flow, nonlinear convergence problems may arise in implicit time-stepping when the popular phase-potential upwinding (PPU) scheme is used. The PPU numerical flux is non-differentiable across the co-current/counter-current flow regimes. This may lead to cycles or divergence in the Newton iteration. Recently proposed methods address improved smoothness of the numerical flux. The objective of this work is to devise and analyze an alternative numerical flux scheme called C1-PPU that, in addition to improving smoothness with respect to saturations and phase potentials, also improves the level of scalar nonlinearity and accuracy. C1-PPU involves a novel use of the flux limiter concept from the context of high-resolution methods, and allows a smooth variation between the co-current/counter-current flow regimes. The scheme is general and applies to fully coupled flow and transport formulations with an arbitrary number of phases.
Several complex heterogeneous multi-dimensional numerical examples under the three-phase black-oil formulation are presented. The examples include a miscible gas injection problem that involves significant variations in the total-velocity field over time. The proposed scheme is compared to the conventional PPU and the recently proposed Hybrid Upwinding schemes. We investigate three properties of these numerical fluxes: smoothness, nonlinearity, and accuracy. The results indicate that in addition to smoothness, nonlinearity may also be critical for convergence behavior and thus needs to be considered in the design of an efficient numerical flux scheme. Moreover, the numerical examples show that our C1-PPU scheme is both entropy-satisfying, and exhibits superior convergence properties for large time steps compared to the other alternatives.
Using Machine Learning to Predict Production at a Peace River Thermal EOR Site
Authors: Martin Eileen , Wills Peter , Hohl Detlef , Lopez Jorge L. 
Published: February 2017

Abstract
When many daily measurements of a thermal EOR field are taken throughout production, it is not cost effective to manually interpret trends to update reservoir models, so we developed an automated data-driven approach for production prediction using machine learning techniques. This is a two-step scheme that first predicts auxilary field measurements from directly-controlled field settings, then uses these predicted field measurements to predict production. The full two-step prediction process needs further refinement, but the second step alone shows promise for aiding in automated interpretation of data. Time shifts from daily seismic surveys improved production predictions.
A Feature-Enriched Multiscale Method for Simulating Complex Geomodels
Authors: Lie K.-A. , Møyner O. , Natvig J. R. 
Published: February 2017

Abstract
Multiscale methods have been shown to offer an order-of-magnitude increase in the speed of reservoir simulators. This may enable users to model complex fluid flow and geology with greater speed and flexibility than is available with the current computational technologies. Contemporary multiscale methods typically use a restriction operator to construct a reduced system of flow equations and a prolongation operator to map pressure unknowns from the reduced flow equations back to the original simulation grid. When combined with a local smoother, this gives an iterative solver that can efficiently compute approximate pressures to within a prescribed accuracy and still provide mass-conservative fluxes. We present an adaptive and flexible framework for combining multiple sets of such multiscale approximations. Each multiscale approximation can target a certain scale; geological features like faults, fractures, facies, or other geobodies; or a particular computational challenge like propagating displacement and chemical fronts, wells being turned on or off, etc. Multiscale methods that fit the framework are characterized by three features. First, the prolongation and restriction operators are constructed using a non-overlapping partition of the fine grid. Second, the prolongation operator is composed of a set of basis functions, each of which has compact support within a support region that contains a coarse grid block. Finally, the basis functions form a partition of unity.
Through a series of numerical examples that include idealized geology and flow physics as well as geological models of real assets, we demonstrate that the new framework increases the accuracy and efficiency of multiscale technology. In particular, we show how it is possible to combine multiscale approximations with different resolution as well as multiscale approximations targeting, among others, high-contrast fluvial sands; fractured carbonate reservoirs; challenging grids including faults, pinchouts and inactive cells; and complex wells.
A Novel IPR Calculation Technique to Reduce Oscillations in Time-Lagged Network-Reservoir Coupled Modeling Using Analytical Scaling and Fast Marching Method
Authors: Zhang Yanbin , Seth Gaurav , Chen Jianping 
Published: February 2017

Abstract
Surface network and subsurface reservoir coupled modeling adds value to asset development assessments when gathering networks and processing units impose significant constraints on production and injection rates. However, it is often difficult and time-consuming to reduce or avoid oscillations in production forecasts that often occur when the surface network and reservoir model are coupled together time step lagged. We present a new technique for IPR calculations which significantly reduces or eliminates these oscillations and provides smoother more realistic production forecasts from time-lagged coupled modeling without significant user intervention. This technique has three steps. First, the conventional IPR curve is calculated by solving the well model in the reservoir simulator multiple times over a range of flow rates or bottom-hole pressures (BHP). Second, a dynamic "region of influence" (ROI) is generated for each well using the Fast Marching Method (FMM). Third, the average pressure in the ROI is used to analytically scale the conventional IPR curve. The analytical scaling process honors the well operating point in the reservoir model at the time of coupling, maintains its existing non-linear shape, and shifts the no-flow BHP of the conventional IPR curve to the average pressure in the ROI.
The conventional IPR curve mentioned earlier only considers the cells in which the well is completed and assumes no change in pressure in these cells until the next coupling time step. As a result, it typically predicts well productivity indices (PI) that are too high so that the coupled model forecasts oscillatory production rates over time. Previous studies have identified this problem and proposed improvements by considering regions around wells for IPR calculations. These methods usually require advancing and winding back flow simulations in reservoir or sub-domain models multiple times between coupling time steps. This implementation requires access to reservoir simulation code and the run time cost can be high and in some cases prohibitive. In contrast, the new technique presented in this paper can be implemented independent from the reservoir simulation code and the run time cost is low. In addition, previous studies provide no clear guideline on how to define the region for a particular well and how the region should change over time. In this paper, we use FMM to define the ROI dynamically around wells, so that its extent depends on simulation time, the coupling time interval and reservoir heterogeneity. We believe the ROI obtained this way reasonably captures the region of the reservoir which most significantly influences well performance until the next coupling time step. Therefore, the average pressure in the ROI provides a good estimate of the no-flow BHP which is used as the basis to adjust the IPR curve. We demonstrate the application of this new technique in a field example.
Bi-Objective Optimization of Well Placement and Controls Using StoSAG
Authors: Lu Ranran , Forouzanfar Fahim , Reynolds A. C. 
Published: February 2017

Abstract
When geological uncertainty is considered in production optimization problem, robust optimization on a set of plausible reservoir models is essential. We consider biobjective optimization where the two objectives are to find the optimal trajectories and/or control settings of injectors and producers that maximize the expected value of the life-cycle net-present-value (NPV) and minimize the risk where the risk is defined as the minimum NPV achieved for any realization. We consider cases where the vector of design (optimization) variables includes either well trajectories or well controls or both sets of variables. The trajectory of each well is parametrized in terms of the (x,y, z) coordinates of the two endpoints of the well segment whereas well controls are represented by the pressure or rates for all control steps. In this work, the optimization is performed using a slightly modified version of a new ensemble-based optimization algorithm known as StoSAG (stochastic simplex approximate gradient). We analyzed the inferiority of the simultaneous optimization using the StoSAG method compared to the sequential optimization scheme and proposed an iterative simultaneous procedure where one iteration of trajectory update is followed by one iteration of control update. In this work, the iterative simultaneous optimization procedure outperforms the sequential procedure (a complete well trajectory optimization followed by a complete control optimization), based on the numerical results of a channelized water flooding reservoir. We also focus on developing efficient implementations of the lexicographic optimization method to find a reasonable trade-off between the expected NPV and the risk. The biobjective optimization is firstly implemented to the well placement optimization problem to illustrate the efficiency of using the proposed splitted StoSAG search direction for the augmented Lagrangian function compared to the basic StoSAG search direction. Besides the channelized reservoir, an inclined gas flooding reservoir loosely modelled after the Oseberg field, is also tested. Finally, the biobjective optimization to both well controls and trajectories are carried out combining the lexicographic method with both the iterative simultaneous procedure and the sequential procedure. Furthermore, in order to avoid less attractive well trajectory solutions where wells are too close together, the constraints that minimum distance between any two well trajectories is greater than or equal to a prescribed distance are also enforced in this paper. To the best of our knowledge, this paper presents the first illustration of the joint optimization of well placement and control with the StoSAG method, considering minimum well spacing constraints. Also the implementation of a computationally efficient decoupled StoSAG method to the biobjective optimization problem is discussed.
Hysteresis Modeling of Capillary Pressure and Relative Permeability by using the Theory of Plasticity
Authors: Yoon H. C. , Zhou P. , Kim J. 
Published: February 2017

Abstract
We investigate a robust and systematic modeling approach for hysteretic capillary pressure and relative permeability by using the theory of plasticity. Plasticity and hysteresis exhibit both irreversible physical processes, and theory of plasticity has been developed, encompassing general behavior of irreversibility. Focusing on immiscible two-phase flow, we mainly investigate mathematical analysis of stability, and find that the method based on the plasticity can provide well-posedness (thermodynamic stability) and algorithmic stability (B-stability). Then, the numerical scheme of this study can stably model several cyclic drainage-imbibition processes. The plastic parameters can track and compute history-dependent properties. In numerical simulation, we apply the algorithm of the 1D isotropic/kinematic hardening plasticity to simulation of gas-water flow. For weak and strong capillarity, the modeling yields strong numerical stability even for several drainage-imbibition processes. We also identify differences between with and without hysteresis, particularly in capillary pressure. Thus, the hysteresis modeling based on the theory of plasticity is promising for numerical simulation of strong hysteresis.
General Implicit Coupling Framework for Multi-Physics Problems
Authors: Rin Ruslan , Tomin Pavel , Garipov Timur , Voskov Denis , Tchelepi Hamdi 
Published: February 2017

Abstract
We present a new framework for solving coupled multi-physics problems. The objective is to develop a platform where different coupling strategies for the simulation of complex physical processes can be employed with great flexibility in order to find an optimal - in terms of robustness and computational efficiency - strategy for a given problem. The new simulator is modular; each module represents a particular physics process, such as compositional, thermal, poromechanics, reactions, wells, and surface facilities. The platform provides seamless coupling between the physics modules without resorting to conditional branches and intermediate interface and treats terms that are coupled across multiple physics modules efficiently. The different modules can be coupled with each other in a sequential or fully coupled manner, and different solution strategies can be applied to different modules. This allows investigation of complex coupling strategies that have not been studied before. Examples of target problems include modeling compositional-thermal EOR processes in both conventional and unconventional resources with tight coupling to nonlinear poromechanics. The paper addresses the design of the framework and provides details of its implementation.