bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling Proppant Transport Through Perforations in a Horizontal Wellbore
Authors: Wu Chu-Hsiang , Sharma Mukul M. 
Published: March 2019

Summary
Proppant placement plays a crucial role in ensuring that the conductivity of fractures is maintained after a hydraulic-fracturing treatment. The process involves the transport of suspended solids in a liquid, usually a water-based fluid, from the wellbore through perforations and finally into fractures. Many studies have focused on proppant settling and transport in fractures, but relatively few studies have investigated the proppant transport process in a wellbore through perforations. This paper addresses the important issue of proppant transport through perforations using a novel numerical technique. The objective is to evaluate the efficiency of proppant transport in a perforated horizontal well under different suspension flow conditions.
In this paper, proppant transport through a perforated horizontal casing is modeled using a coupling of computational fluid dynamics and the discrete element method (CFD-DEM). Reasonable agreements are found between the modeling results and published experimental data. Furthermore, the effectiveness of proppant transport through a perforation is evaluated by the particle transport efficiency (Ei), which is defined as the mass fraction of particles transported through a perforation relative to the total mass of particles in the wellbore upstream of the perforation. The effects of casing diameter, proppant size, proppant density, proppant concentration, fluid-flow rate, fluid rheology, perforation size, and perforation orientation on Ei are investigated.
The simulation results show that proppant inertia strongly influences proppant transport into a perforation. The proportion of proppant that goes into a perforation is typically much different than the proportion of fluid that goes into the same perforation. This results in an increase in the proppant concentration in the slurry as the slurry flows from the heel to the toe side of a plug-and-perforate stage. Results and models presented in this paper provide directions to quantify and potentially control proppant distribution in perforation clusters in horizontal wells.
Efficient Assimilation of Crosswell Electromagnetic Data Using Ensemble-Based History-Matching Framework
Authors: Zhang Yanhui , Hoteit Ibrahim 
Published: March 2019

Abstract
An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.
The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.
A Robust Embedded Discrete Fracture Modeling Workflow for Simulating Complex Processes in Field-Scale Fractured Reservoirs
Authors: Hui Mun-Hong , Dufour Gaelle , Vitel Sarah , Muron Pierre , Tavakoli Reza , Rousset Matthieu , Rey Alvaro , Mallison Bradley 
Published: March 2019

Abstract
Traditionally, fractured reservoir simulations use Dual-Porosity, Dual-Permeability (DPDK) models that can idealize fractures and misrepresent connectivity. The Embedded Discrete Fracture Modeling (EDFM) approach improves flow predictions by integrating a realistic fracture network grid within a structured matrix grid. However, small fracture cells with high conductivity that pose a challenge for simulators can arise and ad hoc strategies to remove them can alter connectivity or fail for field-scale cases. We present a new gridding algorithm that controls the geometry and topology of the fracture network while enforcing a lower bound on the fracture cell sizes. It honors connectivity and systematically removes cells below a chosen fidelity factor. Furthermore, we implemented a flexible grid coarsening framework based on aggregation and flow-based transmissibility upscaling to convert EDFMs to various coarse representations for simulation speedup. Here, we consider pseudo-DPDK (pDPDK) models to evaluate potential DPDK inaccuracies and the impact of strictly honoring EDFM connectivity via Connected Component within Matrix (CCM) models. We combine these components into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications.
We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models. The coarse simulation results show biases consistent with the underlying assumptions (e.g., pDPDK can over-connect fractures). The preservation of fracture connectivity via the CCM aggregation strategy provides better accuracy relative to the fine EDFM forecast while maintaining computational speedup. We then demonstrate the robustness of the proposed EDFM workflow for practical studies through application to an improved oil recovery (IOR) study for a fractured carbonate reservoir. Our automatable workflow enables quick screening of many possibilities since the generation of full-field grids (comprising almost a million cells) and their preprocessing for simulation completes in a few minutes per model. The EDFM simulations, which account for complicated multiphase physics, can be generally performed within hours while coarse simulations are about a few times faster. The comparison of ensemble fine and coarse simulation results shows that on average, a DPDK representation can lead to high upscaling errors in well oil and water production as well as breakthrough time while the use of a more advanced strategy like CCM provides greater accuracy. Finally, we illustrate the use of the Ensemble Smoother with Multiple Data Assimilation (ESMDA) approach to account for field measured data and provide an ensemble of history-matched models with calibrated properties.
Quantifying Near-Wellbore Permeability Including Fractures Using Real-Time Pressure from Distributed Downhole Measurements
Authors: Beokhaimook Chayapod , Kazemi Hossein 
Published: March 2019

Abstract
In the petroleum industry, there is a need to accurately obtain downhole pressure to determine the distribution of flow rates at perforations in the wellbore, which can be used to quantify near-wellbore reservoir heterogeneity, such as major fracture conduits. Traditionally, electrical sensors are used to obtain the measurements, but fiber optics sensing has become more popular due to many advantages over traditional sensors, such as long-term durability and distributed sensing in harsh conditions. In addition, one of the most valuable measurement attributes that fiber-optic sensing could provide is a near-continuous recording of fluid pressure along the wellbore via distributed pressure sensing (DPS).
The measured pressure data along the wellbore from fiber-optic sensing can be used to calculate specific productivity index variations, which in turn, quantifies permeability spikes along the wellbore, such as high-permeability fractures and leaking faults. Because of the lack of field data, a proxy numerical model was developed to generate flowing pressure data in a horizontal wellbore. We then used a semi-analytical model to determine the permeability variations along the wellbore that yield the closest wellbore pressure profile to the simulated measured pressure, using the ‘interior-point barrier method’ algorithm. Several numerical examples, such as a single-phase and a water-oil flow, were examined.
Knowing the productivity of the reservoir along the wellbore allows operators to make quality decisions, leading to optimal reservoir development. In addition, one can generate a flow rate curve along the wellbore as a proxy to a production logging measurement. Furthermore, fiber-optic pressure sensing provides a continuous stream of valuable information, leading to reservoir description on a real-time basis.
A Natural Variable Well Model for Advanced Thermal Simulation
Authors: Zhou Yifan , Li Gary , Zapata Vito 
Published: March 2019

Abstract
For numerical reservoir simulation, the well model has always been a critical component that can have significant impact on the results and performance of the simulation. A new well model has been developed in a commercially available simulator to provide additional capabilities and improved robustness for advanced thermal simulation.
A Natural Variable (NV) formulation, similar to that used in the reservoir solution, has been adopted for the new well model. The NV formulation enables the well model to reuse many of the reservoir solution computations hence allows for rapidly adding support of new features as they get implemented in the reservoir solution. In addition, to model the steam injection process more accurately, we adopted full-upstream weighted mobility for injection connections, for which the amount of steam injected depends on the wellbore instead of reservoir cell condition. The NV well model also supports advanced features such as thermal multi-segment wells with loops for modeling annular flow, thermal drift flux model for counter-current flow, and dynamic coefficients for conductive heat transfer.
We present numerical results using real field data to demonstrate the new capabilities. Comparisons between the NV well model and the original Mass Variable (MV) well model, as well as between the full-upstream weighted and traditional cell voidage injection mobility are included. For cases using cell-voidage mobility and no advanced features, both well models produce similar results. On the other hand, for the cases tested using recommended full-upstream weighted mobility and advanced features such as thermal multi-segment wells with drift flux, and dynamic heat transfer, NV well model produces more stable results with superior convergence behavior. We also observed that, compared with cell-voidage mobility, full-upstream weighted mobility yields more realistic (higher) injectivity, which is critical in modeling steam injection processes.
With NV well model and its advanced features, we can obtain more efficient, accurate, and robust performance predictions for thermal recovery processes for better reservoir management of heavy oil fields. In addition, algorithmic reuse of reservoir calculations within the reservoir simulator enable easier extension of the well-model to support additional complex physics.
INSIM-FT-3D: A Three-Dimensional Data-Driven Model for History Matching and Waterflooding Optimization
Authors: Guo Zhenyu , Reynolds Albert C. 
Published: March 2019

Abstract
We previously published a two-dimensional data-driven model (INSIM-FT) for history matching waterflooding production data and to identify flow barriers and regions of high connectivity between injector-producer pairs. This two-dimensional INSIM model assumed vertical wells. The history-matched models can be used for prediction of waterflooding performance and life-cycle waterflooding optimization. The INSIM-FT-3D model presented here extends INSIM-FT to three dimensions, considers gravity and enables the use of arbitrary well trajectories. INSIM-FT-3D places nodes at each well perforation and then adds nodes throughout the reservoir. Flow occurs through "streamtubes" between each pair of connected nodes. Mitchell's best candidate algorithm is used to place nodes and a three-dimensional (3D) connection map is generated with Delaunay triangulation. Pressures and saturations at nodes, respectively, are obtained from IMPES-like pressure equations and a Riemann solver that include gravity effects. With history-matched model(s) as the forward model(s), we estimate the optimal well controls (pressure or rates at control steps) that maximize the life-cycle net-present-value (NPV) of production under waterflooding using a gradient-based method that employs a stochastic gradient. Two 3D reservoirs are considered to establish the viability of using INSIM-FT-3D history-matched models for waterflooding optimization, a channelized reservoir and the Brugge reservoir. Unlike history-matching and waterflooding optimization based on reservoir simulation models, INSIM-FT-3D is not a detailed geological model. Moreover, the time required to run INSIM-FT-3D is more than one order of magnitude less the cost of running a comparable reservoir simulation model.
Rapid Forecast Calibration Using Nonlinear Simulation Regression with Localization
Authors: He Jincong , Sun Wenyue , Wen Xian-Huan 
Published: March 2019

Abstract
Calibrating production and economic forecasts (objective functions) to observed data is a key component in oil and gas reservoir management. Traditional model-based data assimilation (history matching) entails first calibrating models to the data and then using the calibrated models for probabilistic forecast, which is often ill-posed and time-consuming. In this study, we present an efficient regression-based approach that directly predicts the objectives conditioned to observed data without model calibration.
In the proposed workflow, a set of samples is drawn from the prior distribution of the uncertainty parameter space, and simulations are performed on these samples. The simulated data and values of the objective functions are then assembled into a database, and a functional relationship between the perturbed simulated data (simulated data plus error) and the objective function is established through nonlinear regression methods such as nonlinear partial least square (NPLS) with automatic parameter selection. The prediction from this regression model provides estimates for the mean of the posterior distribution. The posterior variance is estimated by a localization technique.
The proposed methodology is applied to a data assimilation problem on a field-scale reservoir model. The posterior distributions from our approach are validated with reference solution from rejection sampling and then compared with a recently proposed method called ensemble variance analysis (EVA). It is shown that EVA, which is based on a linear-Gaussian assumption, is equivalent to simulation regression with linear regression function. It is also shown that the use of NPLS regression and localization in our proposed workflow eliminates the numerical artifact from the linear-Gaussian assumption and provides substantially better prediction results when strong nonlinearity exists. Systematic sensitivity studies have shown that the improvement is most dramatic when the number of training samples is large and the data errors are small.
The proposed nonlinear simulation-regression procedure naturally incorporates data error and enables the estimation of the posterior variance of objective quantities through an intuitive localization approach. The method provides an efficient alternative to traditional two-step approach (probabilistic history matching and then forecast) and offers improved performance over other existing methods. In addition, the sensitivity studies related to the number of training runs and measurement errors provide insights into the necessity of introducing nonlinear treatments in estimating the posterior distribution of various objective quantities.
Reduced-Order Modeling of Coupled Flow-Geomechanics Problems
Authors: Jin Zhaoyang Larry , Garipov Timur , Volkov Oleg , Durlofsky Louis J. 
Published: March 2019

Abstract
A reduced-order modeling framework is developed and applied to simulate coupled flow-geomechanics problems. The reduced-order model is constructed using POD-TPWL, in which proper orthogonal decomposition (POD), which enables representation of the solution unknowns in a low-dimensional subspace, is combined with tra jectory piecewise linearization (TPWL), where solutions with new sets of well controls are represented via linearization around previously simulated (training) solutions. The over-determined system of equations is pro jected into the lowdimensional subspace using a least-squares Petrov-Galerkin procedure, which has been shown to maintain numerical stability in POD-TPWL models. The states and derivative matrices required by POD-TPWL, generated by an extended version of Stanford's Automatic-Differentiation-based General Purpose Research Simulator, are provided in an offline (pre-processing or training) step. Offline computational requirements correspond to the equivalent of 5-8 full-order simulations, depending on the number of training runs used. Runtime (online) speedups of O(100) or more are typically achieved for new POD-TPWL test-case simulations. The POD-TPWL model is tested extensively for a 2D coupled problem involving oil-water flow and geomechanics. It is shown that POD-TPWL provides predictions of reasonable accuracy, relative to full-order simulations, for well-rate quantities, global pressure and saturation fields, global maximum and minimum principal stress fields, and the Mohr-Coulomb rock failure criterion, for the cases considered. A systematic study of POD-TPWL error is conducted using various training procedures for different levels of perturbation between test and training cases. The use of randomness in the well bottom-hole pressure profiles used in training is shown to be beneficial in terms of POD-TPWL solution accuracy. The procedure is also successfully applied to a prototype 3D example case.
High Fidelity Simulation of Recovery Mechanisms in Complex Natural Fracture Systems
Authors: Vo Hai , Kamath Jairam , Hui Robin 
Published: March 2019

Abstract
Due to numerical difficulties in conducting high fidelity simulation of recovery mechanisms in complex natural fracture systems, there are no published studies that address the impact of preserving details of the fracture networks. We used highly refined grids to conduct fine scale simulations of various recovery mechanisms in different complex fracture settings and compared the results to those obtained on simplified dual porosity dual permeability (DPDK) representations created by applying a consistent upscaling procedure.
Our study considers densely connected, sparsely connected, and isolated fracture networks that are extracted from a field-scale fractured carbonate reservoir model. Discrete fracture-matrix (DFM) models were constructed using an unstructured grid with refinement of the matrix rock near fractures. High-resolution simulations of spontaneous imbibition, gravity drainage, and viscous displacement recovery mechanisms were conducted on these DFM models. We also built equivalent DPDK models by using single phase flow-based upscaling and actual fracture geometry and distribution. The recovery mechanisms were simulated on these DPDK models and compared to high-resolution DFM models.
The fine scale simulations revealed that lateral viscous displacement recovery depends on the details of the fracture networks and can be significantly higher than those predicted from equivalent DPDK models. The DPDK models all predict the same recovery. For spontaneous imbibition, both fine scale and equivalent DPDK models show dependence on fracture geometry, but the DPDK models predict much higher rates. Fine scale and equivalent DPDK models agree reasonably for gravity drainage. These findings are explained by analyzing the matrix-fracture flows, and implications on efforts to improve shape factors in DPDK models and upscaling efforts in DFM models are discussed.
An Adaptive Preconditioning Strategy to Speed up Parallel Reservoir Simulations
Authors: Wang Kun , Liu Hui , Yan Lin , Luo Jia , Wu Keliu , Li Jing , Chen Fuli , Dong Xiaohu , Chen Zhangxin 
Published: March 2019

Abstract
In reservoir simulation, an ILU preconditioner is the most widely used preconditioner for preconditioning linear systems due to its simplicity and low computational cost. However, an ILU preconditioner sometimes is not effective enough, especially for a large-scale parallel reservoir simulation problem with a highly heterogeneous geological model. A constrained pressure residual (CPR) preconditioner is considered a more efficient one, which employs two stages of a preconditioning process: the first stage uses the Algebraic Multi-grid (AMG) method to solve a pressure system, and the second stage uses the ILU method to solve the whole system. Its disadvantage is the high computational cost of the AMG method. In order to reduce the computation costs on preconditioners and the resulting linear solvers, we have developed an adaptive preconditioning strategy [16] to automatically select a preconditioner between an ILU preconditioner and a CPR preconditioner or switch the ILU preconditioner to the CPR preconditioner and vice versa during a linear solution process. In this paper, the adaptive strategy is further analyzed and studied to understand its numerical performance and to choose optimal switch criteria.
Multiscale Computations of Hydraulic Fracture Propagation in Low-Permeability Heterogeneous Rocks
Authors: Wan Richard G. , Eghbalian Mahdad , Pouragha Mehdi , Fung Larry S.K. 
Published: March 2019

Abstract
A multiscale fracture model for low-permeability brittle rocks which accounts for their microstructure is presented. The work hinges on a microcrack-damage model within a poroelasticity and multiscale framework. A set of damage tensors describes the effect of dual-scale porosities (nanopores and microcracks) on both the hydraulic and poroelasticrock properties. Failure is formulated as a material degradation phenomenon driven by microcrack growth which impacts on hydro-mechanical properties. Essentially, the multiscale model reconstructs the coupling effect of hydro-mechanical forces at the continuum level from the ground up through the upscaling of the phase interactions at the fundamental scales of the material, which is novel in rock mechanics applied to hydraulic fracturing. As an illustration of the enhanced capabilities of the developed model, numerical simulations based on the extended finite element method are presented consideringbench mark problems and lab experimental results of hydraulic fracturing in heterogeneous brittle rocks.