bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Framework for the Integrated Reservoir and Surface Facilities Modeling
Authors: Zaydullin Rustem , Cao Hui , Liao Terrence , Obi Eguono 
Published: March 2019

Abstract
Integrated simulation of reservoirs, wells, and surface facilities is becoming increasingly popular for modeling hydrocarbon production from deep offshore assets. Currently, there exist two common approaches for the integration. The first approach employs separate reservoir and facility simulators; whereas, the second approach combines the two within one reservoir simulation framework. Both approaches have advantages and drawbacks. For example, the first approach can be more accurate for the facility modeling, but overall it suffers from stability issues and long running times. On the other hand, the second approach is always numerically stable and typically provides better runtime performance, but requires additional inputs, e.g., Vertical Lift Performance (VLP) tables. Preparation of these additional inputs can be time consuming and often error-prone. Moreover, the VLP tables used in the second approach are typically constructed with the averaged values of "auxiliary" parameters, such as inlet temperature, water salinity, etc. This averaging can potentially lead to inaccuracies during simulation.
In this paper, we propose a new framework for integrated asset modeling which combines the benefits of the two approaches and hence significantly improves the efficiencies in both workflow construction and simulation accuracy. Our framework is based on the previously presented fully coupled network approach implemented as an in-house extension to a reservoir simulator. Here we extend the approach by introduction of an additional coupling step with a separate pipe flow (network) simulator. However, instead of using the pipe flow simulator to solve the network, it is used only to dynamically generate the VLP tables for the simulator's internal network module. Comparing to the previous fully coupled network approach, our new approach streamlines the simulation workflow by avoiding the necessity of the additional manually created network input. Furthermore this new approach also improves the modeling accuracy by using a generalization of the VLP description (e.g. with temperature as additional dimension) and by avoiding tables extrapolations. In this paper we discuss the new workflow and the new dynamic generalized VLP table construction in details.
Modeling Hydraulically Fractured Shale Wells Using the Fast Marching Method with Local Grid Refinements LGRs and Embedded Discrete Fracture Model EDFM
Authors: Xue Xu , Yang Changdong , Onishi Tsubasa , King Michael J. , Datta-Gupta Akhil 
Published: March 2019

Abstract
Recently the Fast Marching Method (FMM) based flow simulation has shown great promise for rapid modeling of unconventional oil and gas reservoirs. Currently, the application of FMM-based simulation has been limited to the use of tartan grid to model the hydraulic fractures (HFs). The use of tartan grids adversely impacts the computational efficiency, particularly for field-scale applications with hundreds of HFs. This paper is aimed at extending the FMM-based simulation to incorporate local grid refinements (LGRs) and embedded discrete fracture model (EDFM) to simulate HFs with natural fractures and validating the accuracy and efficiency of the methodologies.
The FMM-based simulation is extended to LGRs and EDFM. This requires novel gridding through introduction of triangles (in 2D) and tetrahedrons (in 2.5D) to link the local and global domain and solution of the Eikonal equation in unstructured grids to compute the ‘diffusive time of flight'. The FMM-based flow simulation reduces 3D simulation to an equivalent 1D simulation using the ‘diffusive time of flight (DTOF)’ as a spatial coordinate. The 1D simulation can be carried out using standard finite-difference method leading to orders of magnitude savings in computation time compared to full 3D simulation for high-resolution models.
We first validate the accuracy and computational efficiency of the FMM-based simulation with LGRs by comparing with tartan grids. The results show good agreements and the FMM-based simulation with LGRs shows significant improvement in computational efficiency. Then, we apply the FMM based simulation with LGRs to a multi-stage hydraulically fractured horizontal well with multiphase flow case to demonstrate the practical feasibility of our proposed approach. After that, we investigate various discretization schemes for the transition between local and global domain in the FMM-based flow simulation. The results are used to identify optimal gridding schemes to maintain accuracy while improving computational efficiency. Finally, we demonstrate the workflow of the FMM-based simulation with EDFM, including grid generation, comparison with FMM with unstructured grid and validation of the results. The FMM with EDFM can simulate arbitrary fracture patterns without simplification and shows good accuracy and efficiency.
This is the first study to apply the FMM-based flow simulation with LGRs and EDFM. The three main contributions of the proposed methodology are: (i) unique mesh generation schemes to link fracture and matrix flow domains (ii) diffusive time of flight calculations in locally refined grids (iii) sensitivity studies to identify optimal discretization schemes for the FMM-based simulation.
A Bayesian Sampling Framework With Seismic Priors for Data Assimilation and Uncertainty Quantification
Authors: Nejadi Siavash , Kazemi Nasser , Hubbard Stephen M. , Gates Ian D. 
Published: March 2019

Abstract
We present a novel sampling algorithm for characterization and uncertainty quantification of heterogeneous multiple facies reservoirs. The method implements a Bayesian inversion framework to estimate physically plausible porosity distributions. This inversion process incorporates data matching at the well locations and constrains the model space by adding a priori information about the sub-surface structure using a seismic impedance volume. The new framework improves predictive performance and the geological realism of the assimilated ensemble through an efficient parameter estimation. The new parameter estimation process enables conditioning data assimilation to characterize the main features of geological uncertainty such as structural, stratigraphic, facies, and petrophysical properties.
The proposed workflow uses an ensemble-based Markov Chain Monte Carlo approach combined with sampling probability distributions that are physically meaningful. Moreover, the method targets geostatistical modeling to specific zones in the reservoir. Accordingly, it improves fulfilling the inherent stationarity assumption in geostatistical simulation techniques. Parameter sampling and geostatistical simulations are calculated through an inversion process. In other words, the models fit the known porosity field at the well locations and are structurally consistent within main reservoir compartments, zones, and layers obtained from the seismic impedance volume. The new sampling algorithm ensures that the automated history matching algorithm maintains diversity among ensemble members avoiding underestimation of the uncertainty in the posterior probability distribution.
We evaluate the efficiency of the sampling methodology on a synthetic model of a waterflooding field. The predictive capability of the assimilated ensemble is assessed by using production data and dynamic measurements. Also, the qualities of the results are examined by comparing the geological realism of the assimilated ensemble with the reference probability distribution of the model parameters and computing the predicted dynamic data mismatch. Our numerical examples show that incorporating the seismically constrained models as prior information results in an efficient model update scheme and favorable history matching.
Efficient and Robust Solver for Multiphase Rachford-Rice Equations in Compositional and Thermal Simulations
Authors: Pan Huanquan , Imai Motonao , Connolly Michael , Tchelepi Hamdi 
Published: March 2019

Abstract
Robust and efficient multiphase flash calculations are crucial in compositional and thermal simulations for complex fluid systems in which three or four phase may co-exist. Solution of the Richford-Rice (RR) equations is an important operation in the multiphase flash. The Newton method generally does not converge during solution of the RR equations unless very good initial values are provided. In this paper, the solution of the RR equations is formulated as a minimization of a convex function problem. For the first time, we use a trust-region (TR) method to solve the RR equations through minimization of the convex function. The Hessian matrix of the convex function is always positive-definite, and the TR-based solver guarantees convergence. The key to successful implementation is to determine the relaxation parameter in the Newton update. We select this relaxation parameter to meet the boundary of the objective function and to ensure an adequate step length. We tested the RR solver for three and four phase RR problems in the construction of phase diagrams. The test cases are representative of complex fluid systems encountered in enhanced oil recovery, including injection of CO2 into low temperature reservoirs and steam injection into heavy oil reservoirs at elevated temperatures. We performed tens of millions of multiphase flash computations, the results of which reveal our RR solver to be robust, efficient, insensitive to initial values, and capable of handling negative phase amounts. We also evaluated the effect of the initial values on convergence and recommend methods to estimate the initial values in our RR solver. In summary, our RR solver greatly improves the multiphase flash calculations and strengthens the coupling of phase equilibrium calculations to the governing equations in multiphase compositional and thermal simulation.
History Matching and Optimal Design of Chemically Enhanced Oil Recovery Using Multi-Objective Optimization
Authors: Zhang Zheng , Jung Hye Young , Datta-Gupta Akhil , Delshad Mojdeh 
Published: March 2019

Abstract
Chemical enhanced oil recovery (EOR) methods have received increased attention in recent years since they have the ability to recover the capillary trapped oil. Successful chemical flooding application requires accurate numerical models and reliable forecast across multiple scales: core scale, pilot scale, and field scale. History matching and optimization are two key steps to achieve this goal.
For history matching chemical floods, we propose a general workflow for multi-stage model calibration using an Evolutionary Algorithm. A comprehensive chemical flooding simulator is used to model important physical mechanisms including phase behavior, cation exchange, chemical and polymer adsorption and capillary desaturation. First, we identify dominant reservoir and process parameters based on a sensitivity analysis. The history matching is then carried out in a stage-wise manner whereby the most dominant parameters are calibrated first and additional parameters are incorporated sequentially until a satisfactory data misfit is achieved. Next, a diverse subset of history matched models is selected for optimization using a Pareto-based multi-objective optimization approach. Based on the concept of dominance, Pareto optimal solutions are generated representing the trade-off between increasing oil recovery while improving the efficiency of chemical usage. These solutions are searched using a Non-dominated Sorting Genetic Algorithm (NSGA-II). Finally we implement a History Matching Quality Index (HMQI) with Moving Linear Regression Analysis to evaluate simulation results from history matching process. The HMQI provides normalized values for all objective functions having different magnitude and leads to a more consistent and robust approach to evaluate the updated models through model calibration.
A Calibrated Model for the Carbonate-Brine-Crude Oil Surface Chemistry and its Effect on the Rock Wettability, Dissolution, and Mechanical Properties
Authors: Bonto Maria , Eftekhari Ali Akbar , Nick Hamid 
Published: March 2019

Abstract
We suggest two new thermodynamic models for the adsorption of ions to the brine/carbonate and brine/crude oil interface. We calibrate the model parameters to the ionic adsorption and zeta potential data. We then investigate the effect of the rock and oil surface charges on the dissolution, wettability alteration, and mechanical properties of the carbonates in the context of modified-salinity water flooding in the North Sea chalk reservoirs.
We modify a charge-distribution multi-site complexation (CD-MUSIC) model and optimize its parameters by fitting the model to a large data set of calcite surface zeta potential in presence of different brine compositions. We also modify and optimize a diffuse layer model for the oil/brine interface. We then use the optimized surface complexation models with a finite-volume solver to model the two phase reactive transport of oil and brine in a chalk reservoir, including the impact of dissolution, polar-group adsorption, and compaction on the relative permeability of chalk to water and oil. We compare the simulation results with the published experimental data.
Application of Algebraic Smoothing Aggregation Two Level Preconditioner to Multiphysical Fluid Flow Simulations in Porous Media
Authors: Wang Shihao , Lukyanov Alexander , Wu Yu-Shu 
Published: March 2019

Abstract
Traditionally, preconditioners are used to damp slowly varying error modes in the linear solver stage. State-of-the-art multilevel preconditioners use a sequence of aggressive restriction, coarse-grid correction and prolongation operators to handle low-frequency modes on the coarse grid. High-frequency errors are then resolved by employing a smoother on fine grid. In this paper, the algebraic smoothing aggregation two level preconditioner is implemented to solve different coupled problems.
The proposed method generalizes the existing MsRSB and smoothing aggregation AMG methods. This method does not require any coarse partitioning and, hence, can be applied to general unstructured topology of the fine scale. Inspired by smoothing aggregation algebraic multigrid solver, the algebraic smoothing aggregation preconditioner constructs basis functions which allow mapping of some high-frequency modes from fine scale to low-frequency modes on the coarse scale. These basis functions are also used to reconstruct unknown primary variables at the fine scale using their approximations at the coarse level.
The proposed preconditioner has been adopted to challenging multiphysical problems, including fully coupled simulation of filtration and geomechanics processes including non-isothermal fluid flow problems. The preconditioner provides a reasonably good approximation to the coupled physical processes and speeds up the convergence. Compared to traditional ILU0+GMRES linear solvers, our preconditioner with GMRES solver reduces the number of iterations by about 3 times. In addition, the proposed method obeys a good theoretical scalability essential for parallel simulations.
Compact Stacked Contour Applied to Full Field Simulation Lifecycle
Authors: Middya Usuf , Crumpton Paul 
Published: March 2019

Abstract
The objective is to enable a full simulation lifecycle of multi-billion cell reservoir models for gigantic Middle Eastern reservoirs utilizing parallel hardware. Debugging, building and developing models with this resolution is not possible without seamlessly changing the scale of the reservoir model and applying a portfolio of boundary conditions to fit the current workflow.
Throughout the lifecycle of the full-field reservoir model a variety of simulations are needed, including small, single and multiple well sensitivity studies (O(108) cells), regional models (O(107) cells), and ultimately full-field modeling (O(109) cells). It is crucial this can be achieved in a way to exploit the current hardware, with a minimum memory and computational overhead, and have a quick and seamless way to maintain the integrity of the full-field model, without the need for complex pre- and post-processing tools. This is achieved here by the definition of a compact stacked contour; this is cheap enough to be read by every processor in the current run, and by exploiting advanced parallel IO techniques; only the portion of the full-field grid to be simulated needs be imported to core memory. This definition easily lends itself as a natural way to apply boundary conditions to model aquifers, fluxes derived from larger models, and pressure boundary conditions.
The techniques described here will be demonstrated on a number of workflows. Firstly, in aquifer modelling, to exclude the large number of aquifer cells that often arises from simulating on a geological level model, and replace these cells with an analytical model. An external program takes the area of interest, and using the convex hull of the area of interest generates a stacked contour, which is subsequently used to define a Fetkovich aquifer. Secondly, a pressure boundary condition is applied to an area of interest within a full-field model to mimic the decline.
The definition of a stacked compact contour enables the large reservoir model to be analyzed at local, regional and full-field scale while maintaining the integrity of the full-field model. A variety of boundary conditions are applied to the stacked compact contour.
Three-Dimensional Geological Boundary Aligned Unstructured Grid Generation, and CVD-MPFA Flow Computation
Authors: Manzoor Shahid , Edwards Michael G. , Dogru Ali H. 
Published: March 2019

Abstract
Three-dimensional unstructured grid generation for reservoirs with geological layers, faults, pinchouts, fractures and wells is presented. Grids are generated for example cases, and pressure fields and flow fields computed by the cell-centered and vertex-centered control-volume distributed multi-point flux approximation (CVD-MPFA) schemes are compared and contrasted together with the methods. Grid generation for reservoir simulation, must honour classical key geological features and multilateral wells. The geological features are classified into two groups; 1) involving layers, faults, pinchouts and fractures, and 2) involving well distributions. In the former, control-volume boundary aligned grids (BAGs) are required, while in the latter, control-point well aligned grids (WAGs) are required. In reservoir simulation a choice of grid type and consequent control-volume type is made, i.e. either primal or dual-cells are selected as control-volumes. The control-point is defined as the centroid of the control-volume for any grid type. Three-dimensional unstructured grid generation methods are proposed that automate control-volume boundary alignment to geological features and control point alignment to wells, yielding essentially perpendicular bisector (PEBI) meshes either with respect to primal or dual-cells depending on grid type. Both primal and dual-cell boundary aligned grid generators use primal-cells (tetrahedra, pyramids, prisms and hexahedra) as grid elements. Dual-cell feature aligned grids are derived from underlying primal-meshes, such that features are recovered, with control-volume faces aligned with interior feature boundaries. The grids generated enable a comparative performance study of cell- vertex versus cell-centered CVD-MPFA finite-volume formulations using equivalent degrees of freedom. The benefits of both types of approximation are presented in terms of flow resolution relative to the respective degrees of freedom employed. Stability limits of the methods are also explored. For a given mesh the cell-vertex method uses approximately a fifth of the unknowns used by a cell-centered method and proves to be the most beneficial with respect to accuracy and efficiency, which is verified by flow computation. Novel techniques for generating three-dimensional unstructured hybrid essentially PEBI-grids, honouring geological features are presented. Geological boundary aligned grid generation is performed for primal and dual-cell grid types. Flow results show that vertex-centered CVD-MPFA methods outperform cell-centered CVD-MPFA methods.
A Massively Parallel Algebraic Multiscale Solver for Reservoir Simulation on the GPU Architecture
Authors: Manea A. M. , Almani T. 
Published: March 2019

Abstract
In this work, the scalability of the Algebraic Multiscale Solver (AMS) (Wang et al. 2014) for the pressure equation arising from incompressible flow in heterogeneous porous media is investigated on the GPU massively parallel architecture. The solvers robustness and scalability is compared against its carefully optimized implementation on the shared-memory multi-core architecture (Manea et al. 2016), which this work is directly extending. Although several components in the AMS algorithm are directly parallelizable, its scalability on GPU's depends heavily on the underlying algorithmic details and data-structures design of each step, where one needs to ensure favorable control- and data-flow on the GPU, while extracting enough parallel work for a massively parallel environment. In addition, the type of the algorithm chosen for each step greatly influences the overall robustness of the solver. Taking all these constraints into account, we have developed a GPU-based AMS that exploits the parallelism in every module of the solver, including both the setup phase and the solution phase. The performance of AMS—with our carefully optimized algorithmic choices on the GPU for both the setup phase and the solution phase, is demonstrated using highly heterogeneous 3D problems derived from the SPE10 Benchmark (Christie et al. 2001). Those problems range in size from millions to tens of millions of cells. The GPU implementation is benchmarked on a massively parallel architecture consisting of NVIDIA Kepler K80 GPU's, where its performance is compared to the multi-core CPU architecture using an optimized multi-core AMS implementation (Manea et al. 2016) running on a shared memory multi-core architecture consisting of two packages of Intel's Haswell-EP Xeon(R) CPU E5-2667. While the GPU-based AMS parallel implementation shows good scalability for the solution stage, its setup stage is less efficient compared to the CPU, mainly due to the dependence on a QR-based basis functions solver.