bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Fast Gridding Method for Capturing Geological Complexity and Uncertainty
Authors: Xu Yifei , Srivastava Priyesh , Ma Xiao , Kaul Karan , Huang Hao 
Published: October 2021

Abstract
In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.
Scalable Hierarchical Multilevel Sampling of Lognormal Fields Conditioned on Measured Data
Authors: Barker A. T. , Lee C. S. , Forouzanfar F. , Guion A. , Wu X.-H. 
Published: October 2021

Abstract
We explore the problem of drawing posterior samples from a lognormal permeability field conditioned by noisy measurements at discrete locations. The underlying unconditioned samples are based on a scalable PDE-sampling technique that shows better scalability for large problems than the traditional Karhunen-Loeve sampling, while still allowing for consistent samples to be drawn on a hierarchy of spatial scales. Lognormal random fields produced in this scalable and hierarchical way are then conditioned to measured data by a randomized maximum likelihood approach to draw from a Bayesian posterior distribution. The algorithm to draw from the posterior distribution can be shown to be equivalent to a PDE-constrained optimization problem, which allows for some efficient computational solution techniques. Numerical results demonstrate the efficiency of the proposed methods. In particular, we are able to match statistics for a simple flow problem on the fine grid with high accuracy and at much lower cost on a scale of coarser grids.
Coupling-Strength Criteria for Sequential Implicit Formulations
Authors: Franc Jacques , Møyner Olav , Tchelepi Hamdi A. 
Published: October 2021

Abstract
Sequential Fully Implicit (SFI) schemes have been proposed as an alternative to the Fully Implicit Method (FIM). A significant advantage of SFI is that one can employ scalable strategies to the flow and transport problems. However, the primary disadvantage of using SFI compared with FIM is the fact that the splitting errors induced by the decoupling operator, which separates the pressure from the saturation(s), can lead to serious convergence difficulties of the overall nonlinear problem. Thus, it is important to quantify the coupling strength in an adaptive manner in both space and time. We present criteria that localize the computational cells where the pressure and saturation solutions are tightly coupled. The approach is using terms in the FIM Jacobian matrix, we quantify the sensitivity of the mass and volume-balance equations to changes in the pressure and the saturations. We identify three criteria that provide a measure of the coupling strength across the equations and variables. The standard CFL stability criteria, which are based entirely on the saturation equations, are a subset of the new criteria. Here, the pressure equation is solved using Algebraic MultiGrid (AMG), or a multiscale solver, such as the Multiscale Restricted-Smooth Basis (MsRSB) approach. The transport equations are then solved using a fixed total-velocity. These ‘coupling strength’ criteria are used to identify the cells where the pressure-saturation coupling is strong. The applicability of the derived coupling-strength criteria is tested using several test cases. The first test is using a gravitational immiscible dead-oil lock-exchange under a unit mobility ratio and large differences in density. For this case, the SFI algorithm fails to converge to the fully coupled solution due to the large splitting errors. Introducing a fully coupled solution stage on the local subdomains as an additional correction step restores nonlinear convergence. Detailed analysis of the ‘coupling strength’ criteria indicates that the criteria related to the sensitivity of the mass balance to changes in the pressure and the sensitivity of the volume balance to changes in the saturations are the most important ones to satisfy. Other test cases include an alternate gas-water-gas injection in a top layer of the SPE 10 test case and an injection-production scenario in a three-dimensional reservoir with layered lognormally distributed permeability. We propose novel criteria to estimate the strength of coupling between pressure and saturation. These CFL-like numbers are used to identify the cells that require fully implicit treatment in the nonlinear solution strategy. These criteria can also be used to improve the nonlinear convergence rates of Adaptive Implicit Methods (AIM).
The Modeling for Coupled Elastoplastic Geomechanics and Two-Phase Flow With Capillary Hysteresis in Porous Media
Authors: Yoon H. C. , Kim J. 
Published: October 2021

Abstract
We study new constitutive relations employing the fundamental theory of elastoplasticity for two coupled irreversible processes: elastoplastic geomechanics and two-phase flow with capillary hysteresis. The fluid content is additively decomposed into elastic and plastic parts with infinitesimal transformation assumed. Specifically, the plastic fluid content, i.e., the total residual (or irrecoverable) saturation, is also additively decomposed into constituents due to the two irreversible processes: the geomechanical plasticity and the capillary hysteresis. The additive decomposition of the plastic fluid content facilitates combining the existing two individual simulators easily, for example, by using the fixed-stress sequential method. For pore pressure of the fluid in multi-phase which is coupled with the geomechanics, the equivalent pore pressure is employed, which yields the well-posedness of coupled multi-phase flow and geomechanics, regardless of the capillarity. We perform an energy analysis to show the well-posedness of the proposed model. And numerical examples demonstrate stable solutions for cyclic imbibition/drainage and loading/unloading processes. Employing the van Genuchten and the Drucker Prager models for capillary and the plasticity, respectively, we show the robustness of the model for capillary hysteresis in multiphase flow and elastoplastic geomechanics.
Numerical Simulation of Proppant Transport in Hydraulically Fractured Reservoirs
Authors: Gorucu Seyhan Emre , Shrivastava Vijay , Nghiem Long X. 
Published: October 2021

Abstract
An existing equation-of-state compositional simulator is extended to include proppant transport. The simulator determines the final location of the proppant after fracture closure, which allows the computation of the permeability along the hydraulic fracture. The simulation then continues until the end of the production.
During hydraulic fracturing, proppant is injected in the reservoir along with water and additives like polymers. Hydraulic fracture gets created due to change in stress caused by the high injection pressure. Once the fracture opens, the bulk slurry moves along the hydraulic fracture. Proppant moves at a different speed than the bulk slurry and sinks down by gravity. While the proppant flows along the fracture, some of the slurry leaks off into the matrix. As the fracture closes after injection stops, the proppant becomes immobile. The immobilized proppant prevents the fracture from closing and thus keeps the permeability of the fracture high.
All the above phenomena are modelled effectively in this new implementation. Coupled geomechanics simulation is used to model opening and closure of the fracture following geomechanics criteria. Proppant retardation, gravitational settling and fluid leak-off are modeled with the appropriate equations. The propped fracture permeability is a function of the concentration of immobilized proppant. The developed proppant simulation feature is computationally stable and efficient. The time step size during the settling adapts to the settling velocity of the proppants. It is found that the final location of the proppants is highly dependent on its volumetric concentration and slurry viscosity due to retardation and settling effects. As the location and the concentration of the proppants determine the final fracture permeability, the additional feature is expected to correctly identify the stimulated region.
In this paper, the theory and the model formulation are presented along with a few key examples. The simulation can be used to design and optimize the amount of proppant and additives, injection timing, pressure, and well parameters required for successful hydraulic fracturing.
Physics Informed Deep Learning for Flow and Transport in Porous Media
Authors: Fraces Cedric G. , Tchelepi Hamdi 
Published: October 2021

Abstract
We present our progress on the application of physics informed deep learning to reservoir simulation problems. The model is a neural network that is jointly trained to respect governing physical laws and match boundary conditions. The methodology is hereby used to simulate a 2-phase immiscible transport problem (Buckley-Leverett). The model is able to produce an accurate physical solution both in terms of shock and rarefaction and honors the governing partial differential equation along with initial and boundary conditions. We test various hypothesis (uniform and non-uniform initial conditions) and show that with the proper implementation of physical constraints, a robust solution can be trained within a reasonable amount of time and iterations. We revisit some of the limitations presented in previous work [1] and further the applicability of this method in a forward, pure hyperbolic setup. We also share some practical findings on the application of physics informed neural networks (PINN). We review various network architectures presented in the literature and show tips that helped improve their convergence and accuracy. The proposed methodology is a simple and elegant way to instill physical knowledge to machine-learning algorithms. This alleviates the two most significant shortcomings of machine-learning algorithms: the requirement for large datasets and the reliability of extrapolation. The principles presented can be generalized in innumerable ways in the future and should lead to a new class of algorithms to solve both forward and inverse physical problems.
Bayesian Optimization for Field Scale Geological Carbon Sequestration
Authors: Lu Xueying , Jordan Kirk E. , Wheeler Mary F. , Pyzer-Knapp Edward O. , Benatan Matthew 
Published: October 2021

Abstract
We present a framework of the application of Bayesian Optimization (BO) to well management in geological carbon sequestration. The coupled compositional flow and poroelasticity simulator, IPARS, is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS is coupled to IBM Bayesian Optimization (IBO) for parallel optimizations of CO2 injection strategies during field-scale CO2 sequestration. Bayesian optimization builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm, Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. IBO addresses the three weak points of the standard BO in that it supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these algorithmic merits by an application to the optimization of the CO2 injection schedule in the Cranfield site using field data. The performance is benchmarked with genetic algorithm (GA) and covariance matrix adaptation evolution strategy (CMA-ES). Results show that IBO achieves competitive objective function value with over 60% less number of forward model evaluations. Furthermore, the Bayesian framework that BO builds upon allows uncertainty quantification and naturally extends to optimization under uncertainty.
Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations
Authors: Heidari Mohammad , Istchenko Christopher , Bailey William , Stone Terry 
Published: October 2021

Abstract
The paper examines new horizontal drift-flux correlations for their ability to accurately model phase flow rates and pressure drops in horizontal and undulating wells that are part of a Steam-Assisted Gravity Drainage (SAGD) field operation. Pressure profiles within each well correlate to the overall performance of the pair. SAGD is a low-pressure process that is sensitive to reservoir heterogeneity and other factors, hence accurate simulation of in situ wellbore pressures is critical for both mitigating uneven steam chamber evolution and optimizing wellbore design and operation.
Recently published horizontal drift-flux correlations have been implemented in a commercial thermal reservoir simulator with a multi-segment well model. Valid for horizontally drilled wells with undulations, they complement previously reported drift-flux models developed for vertical and inclined wells down to approximately 5 degrees from horizontal. The formulation of these correlations has a high degree of nonlinearity. These models are tested in simulations of SAGD field operations.
First, an overview of drift-flux models is discussed. This differentiates those based on vertical flow with gravity segregation to those that model horizontal flow with stratified and slug flow regimes. Second, the most recent and significant drift-flux correlation by Bailey et al. (2018, and hereafter referred to as Bailey-Tang-Stone) was robustly designed to be used in the well model of a reservoir simulator, can handle all inclination angles and was optimized to experimental data from the largest available databases to date. This and earlier drift-flux models are reviewed as to their strengths and weaknesses. Third, governing equations and implementation details are given of the Bailey-Tang-Stone model. Fourth, six case studies are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios.
A Cut-Cell Polyhedral Finite Element Model for Coupled Fluid Flow and Mechanics in Fractured Reservoirs
Authors: Shovkun Igor , Tchelepi Hamdi A. 
Published: October 2021

Abstract
Mechanical deformation induced by injection and withdrawal of fluids from the subsurface can significantly alter the flow paths in naturally fractured reservoirs. Modeling coupled fluid-flow and mechanical deformation in fractured reservoirs relies on either sophisticated gridding techniques, or enhancing the variables (degrees-of-freedom) that represent the physics in order to describe the behavior of fractured formation accurately. The objective of this study is to develop a spatial discretization scheme that cuts the "matrix" grid with fracture planes and utilizes traditional formulations for fluid flow and geomechanics.
The flow model uses the standard low-order finite-volume method with the Compartmental Embedded fracture Model (cEDFM). Due to the presence of non-standard polyhedra in the grid after cutting/splitting, we utilize numerical harmonic shape functions within a Polyhedral finite-element (PFE) formulation for mechanical deformation. In order to enforce fracture-contact constraints, we use a penalty approach.
We provide a series of comparisons between the approach that uses conforming Unstructured grids and a Discrete Fracture Model (Unstructured DFM) with the new cut-cell PFE formulation. The manuscript analyzes the convergence of both methods for linear elastic, single-fracture slip, and Mandel’s problems with tetrahedral, Cartesian, and PEBI-grids. Finally, the paper presents a fully-coupled 3D simulation with multiple inclined intersecting faults activated in shear by fluid injection, which caused an increase in effective reservoir permeability.
Our approach allows for great reduction in the complexity of the (gridded) model construction while retaining the solution accuracy together with great saving in the computational cost compared with UDFM. The flexibility of our model with respect to the types of grid polyhedra allows us to eliminate mesh artifacts in the solution of the transport equations typically observed when using tetrahedral grids and two-point flux approximation.
Reduced-Order Modeling for Multiphase Flow Using a Physics-Based Deep Learning
Authors: Alsulaimani Thamer , Wheeler Mary 
Published: October 2021

Abstract
Reservoir simulation is the most widely used tool for oil and gas production forecasting and reservoir management. Solving a large-scale system of nonlinear differential equations every timestep can be computationally expensive. In this work, we present a two-phase physics-constrained deep-learning reduced-order model as a surrogate model for subsurface flow production forecast. The implemented deep learning model is a physics-guided encoder-decoder, constructed based on the Embed-to-Control (E2C) framework. In our implementation, the E2C works in a way analogous to Proper Orthogonal Decomposition combined with Discrete Empirical Interpolation Method (POD-DEIM) or Trajectory Piece-Wise Linearization approach (POD-TPWL). The E2C-Reduced-order model (ROM) involves projecting the system from a high-dimensional space into a low-dimensional subspace using the encoder-decoder, approximating the progression of the system from one timestep to the next using a linear transition model, and finally projecting the system back to high-dimensional space using the encoder-decoder. To guarantee mass conservation, we adopt the Finite Elements Mixed Formulation in the neural network's loss function combined with the original data-based loss function. Training simulations are generated using a full-physics reservoir simulator (IPARS). High-fidelity pressure, velocity, and saturation solution snapshot at constant time intervals are taken as training input to the neural network. After training, the model is tested over large variations of well control settings. Accurate pressure and saturation solutions are predicted along with the injection and production well quantities using the proposed approach. Errors in the predicted quantities of interest are reduced with the increase in the number of training simulations used. Although it required a large number of training simulations for the offline (training) step, the model achieved a significant speedup in the online stage compared to the full physics model. Considering the overall computational cost, this ROM model is proper for cases when a large number of simulations are required like in the case of production optimization and uncertainty assessments. The proposed approach shows the capability of the deep-learning reduced-order model to accurately predict multiphase flow behavior such as well quantities, and global pressure and saturation fields. The model honors mass conservation and the underlying physics laws, which many existing approaches don't take into direct consideration.
Simplified Initialization of Reservoir Simulation Models with Continuously Varying Tilted Contacts and Complex Fluid Distributions
Authors: Kachuma Dick , Hild Jean-Claude , Belushko Irina 
Published: October 2021

Abstract
We tackle reservoir simulation model initialization in situations where the current free water level has risen significantly above the original paleo-contact. In such cases, traditional initialization based on primary drainage capillary-gravity equilibrium is insufficient because it does not consider the reimbibition that takes place after primary oil migration. Usage of traditional initialization would require a generation of several thousands of regions to have a satisfactory representation of such phenomenon in the model.
We apply this initialization to a reservoir with a complex initial fluid in which hydrocarbons come from multiple sources resulting in significant spatial variation. To capture this spatial variation, the scripting feature of a commercial simulator is used to generate on-the-fly PVT regions and fluid descriptions based on continuous fluid properties. Capillary-gravity equilibrium is then used to initialize the model with the primary drainage capillary pressure curves at the paleo-contact, which gives the minimum water saturation for each grid-block. From this, an imbibition scanning curve is generated for a second initialization based on the current oil-water contact thereby including reimbibition.
Our proposed approach was applied to a giant carbonate field for which we were able to generate on-the-fly PVT regions based on continuous property maps of fluid properties such as oil API, solution gas-oil-ratio and water salinity. This resulted in fewer regions being generated and thereby reducing the amount of memory required to initialize the model. We were also able to reduce the initialization time as compared to use of discretized contacts and regions. Our approach enabled us to initialize with both the paleo- and current contacts allowed to continuously vary spatially. This together with the use of a more appropriate capillary pressure hysteresis model, resulted in an initial reservoir state that gave a better match to the saturations obtained from logs.
This work illustrates the creation of PVT data and fluid regions on-the-fly based on continuous fluid properties. We also demonstrate the use of continuously varying paleo- and current oil-water contacts without the need to discretize these into regions. These aspects facilitate the propagation of an uncertainty workflow, starting with continuous fluid properties and structural modelling, directly to the simulation model with no need for intermediate discretization and without massive computational penalties.
Downregulation of the microbial protein biosynthesis machinery in response to weeks, years, and decades of soil warming
Authors: Söllinger Andrea , Séneca Joana , Dahl Mathilde Borg , Motleleng Liabo L. , Prommer Judith , Verbruggen Erik , Sigurdsson Bjarni , Janssens Ivan , Penuelas Josep , Urich Tim , Richter Andreas , Tveit Alexander 
Published: October 2021

Abstract
How soil microorganisms respond to global warming is key to infer future soil-climate feedbacks, yet poorly understood. Here we applied metatranscriptomics to investigate microbial physiological responses to medium- (8 years) and long-term (>50 years) subarctic grassland soil warming of +6 °C. Besides indications for a community-wide upregulation of central metabolisms and cell replication we observed a downregulation of the protein biosynthesis machinery in the warmed soils, coinciding with a lower microbial biomass, RNA, and soil substrate content. We conclude that permanently accelerated reaction rates at higher temperatures and reduced substrate concentrations results in a cellular reduction of ribosomes, the macromolecular complexes carrying out protein biosynthesis. Later efforts to test this, including a short-term warming experiment (6 weeks, +6 °C), further supported our conclusion. Downsizing the protein biosynthesis machinery facilitates liberation of energy and matter, allowing microorganisms to maintain high metabolic activities and cell division rates even after decades of warming.