bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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.
Impact of WAG Design on Calcite Scaling Risk in Coupled CO2-EOR and Storage Projects in Carbonate Reservoirs
Authors: Rodrigues Hydra , Mackay Eric , Arnold Daniel
Published: March 2019

WAG (Water-Alternating-Gas) schemes have been applied in Brazilian carbonate reservoirs aiming to minimize residual oil saturation and gas flaring by recycling CO2 naturally being produced alongside hydrocarbon gas. However, applying WAG injection in highly reactive and heterogeneous carbonate rocks can potentially create severe scaling problems. This work develops a reactive transport simulation-based workflow to evaluate the impact of key WAG design parameters on oil recovery, scale deposition risk and CO2 storage to support multi-objective decision-making.

Compositional simulations of WAG scenarios were performed as part of a sensitivity study followed by statistical analysis in order to quantify to what extent the outcomes of interest are sensitive to variations on four WAG design parameters: WAG ratio, CO2 concentration in the injection gas stream, injection rate and solvent slug-size. We established an Equation-of-State (EoS) using PVT data, a representative geochemical model and well constrains designed to control production of injected fluids. Scale risk was assessed by calcite changes around the wells, precipitation in well tubing and surface facilities, and water breakthrough.

Results of this study showed that values of calcite rate constant (Ksp) and reactive surface area (A0) assigned in numerical simulations can impact relative calcite changes in the reservoir. Using reactive surface areas from BET studies of crushed rocks can lead to prediction of unrealistic amounts of calcite dissolution. Cases with lower values of (Ksp×A0) appeared to be more numerically stable and more consistent with dissolution/precipitation rates of silicate minerals. Simulation results also suggested that calcite dissolution close to injection wells and precipitation in production wells and surface facilities become more severe as CO2 concentration in injection gas and WAG ratio increases. Based on the design variables and reservoir conditions studied, the most to least crucial factors affecting oil recovery were: CO2 concentration in the injection gas stream, injection rate, WAG ratio and solvent slug-size. From a storage perspective, the impact of the design variables had considerably more impact, with the most influential factor being again CO2 concentration in the injection gas stream, followed by WAG ratio, injection rate and solvent slug-size. Optimization study results suggested that low WAG ratio values combined with low to intermediate gas slug sizes could result in superior profitability and CO2 storage outcomes for this pilot.

Ultimately, we demonstrate the importance of integrating multiphase miscible displacement with geochemical reactions while modeling complex CO2-EOR in carbonate reservoirs and address how key design parameters impact our desired outcomes, knowledge that promotes a more robust decision-making framework.
Algebraic Dynamic Multilevel Method for Fractured Geothermal Reservoir Simulation
Authors: HosseiniMehr Mousa , Arbarim Rhadityo , Cusini Matteo , Vuik Cornelis , Hajibeygi Hadi
Published: March 2019

A dynamic multilevel method for fully-coupled simulation of flow and heat transfer in heterogeneous and fractured geothermal reservoirs is presented (FG-ADM). The FG-ADM develops an advanced simulation method which maintains its efficiency when scaled up to field-scale applications, at the same time, it remains accurate in presence of complex fluid physics and heterogeneous rock properties. The embedded discrete fracture model is employed to accurately represent fractures without the necessity of unstructured complex grids. On the fine-scale system, FG-ADM introduces a multi-resolution nested dynamic grid, based on the dynamic time-dependent solution of the heat and mass transport equations. The fully-coupled implicit simulation strategy, in addition to the multilevel multiscale framework, makes FG-ADM to be stable and efficient in presence of strong flow-heat coupling terms. Furthermore, its finite-volume formulation preserves local conservation for both mass and heat fluxes. Multi-level local basis functions for pressure and temperature are introduced, in order to accurately represent the heterogeneous fractured rocks. These basis functions are constructed at the beginning of the simulation, and are reused during the entire dynamic time-dependent simulation. For several heterogeneous test cases with complex fracture networks we show that, by employing only a fraction of the fine-scale grid cells, FG-ADM can accurately represent the complex flow-heat solutions in the fractured subsurface formations.
Artificial Neural Network Accelerated Flash Calculation for Compositional Simulations
Authors: Wang Kun , Luo Jia , Yan Lin , Wei Yizheng , Wu Keliu , Li Jing , Chen Fuli , Dong Xiaohu , Chen Zhangxin
Published: March 2019

EOS-based phase equilibrium calculations are usually used in compositional simulation to have accurate phase behaviour. Phase equilibrium calculations include two parts: phase stability tests and phase splitting calculations. Since the conventional methods for phase equilibrium calculations need to iteratively solve strongly nonlinear equations, the computational cost spent on the phase equilibrium calculations is huge, especially for the phase stability tests. In this work, we propose artificial neural network (ANN) models to accelerate the phase flash calculations in compositional simulations. For the phase stability tests, an ANN model is built to predict the saturation pressures at given temperature and compositions, and consequently the stability can be obtained by comparing the saturation pressure with the system pressure. The prediction accuracy is more than 99% according to our numerical results. For the phase splitting calculations, another ANN model is trained to provide initial guesses for the conventional methods. With these initial guesses, the nonlinear iterations can converge much faster. The numerical results show that 90% of the computation time spent on the phase flash calculations can be saved with the application of the ANN models.
Single Block Productivity Approach - How it Identifies and Isolates Multiple Fracture Network Variants and Helps Improve History Matching and Prediction in Unconventional Reservoirs Using Reservoir Simulator
Authors: Kaul Sandeep P. , Vaz Robert F. , Gildin Eduardo
Published: March 2019

In an unconventional reservoir, the biggest challenge is to know how the natural fractures drain the reservoir as they have the greatest impact on production. But unfortunately, very little information is available about them. Microseismics aid in building a picture of the fracture network, but give no information about fractures where actual fluid flow occurs. Production logging results give information around wellbore area only. Conventional rate transient analysis has major drawbacks, as long shut-in times are not possible and with dimensionless variables multiple results are possible. The method outlined in this paper overcomes these limitations using simplified assumptions.

This simulation modeling method uses dual porosity as an idealization of the fracture network, which is the conventional wisdom, but with constant volume hydraulic fractures. This restricts the possible fracture lengths and the associated geometries of these hydraulic fractures, when modeled in 1D, 2D or 3D orientation. These HF-NF connectivity scenarios, using idealized fracture network of slabs (planar 1D HF-NF), matchstick (non-planar 2D HF-NF) and cubes (non-planar 3D HF-NF), is used to establish those fundamental connectivity scenarios where the fracture spacing can either be 1:1:1 (equidistant) or in the ratio 1:2:3. In order to assign permeability to the fractures, under these six different fundamental scenarios which have the same production performance, we follow the single block approach based on rate transient analysis. It also helps in establishing fracture permeability for other fracture connectivity variants such as 2D HF-3D NF or 3D HF-2D NF and with the two previously specified fracture spacings.

The results of this study, which essentially deals with the reservoir linear flow, are presented in the form of characteristic plots based on the ratio of average dimensionless pressure in the block with the square root of dimensionless time versus the dimensionless time for different fracture pressure declines. In each of fracture connectivity scenarios the solution rises to a discreet 1, 2, 3 value if idealized blocks are used or fall short of these values for non-idealized block combination depending on block geometry of NF. These conclusions are also shown by field models, analyzing actual history matched data.

Basic knowledge of the orientation of NF network gives better history match and prediction results. Also, with the help of a reservoir simulator one can assign physical meaning to different fracture spacings, which could be in the increasing or decreasing form. Rate transient analysis, using dimensionless parameters, fails to illustrate this fact. It helps in a long way to establish the optimum fracture spacing with the same volume of proppant being pumped in the reservoir and with known NF orientation.
Reduced Degrees of Freedom Gaussian Mixture Model Fitting for Large Scale History Matching Problems
Authors: Gao Guohua , Jiang Hao , Chen Chaohui , Vink Jeroen C. , Khamra Yaakoub El , Ita Joel , Saaf Fredrik
Published: March 2019

Gaussian-mixture-model (GMM) fitting has been proved a robust method to generate high quality, independent conditional samples of the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees-of-freedom (DOF) for all unknown GMM parameters may become huge for large-scale history-matching problems. A new formulation of GMM fitting with reduced number of DOF is proposed in this paper, to save memory-usage and reduce computational cost. Its performance is compared with other methods of GMM.

The GMM fitting method can significantly improve the accuracy of the GMM approximation by adding more Gaussian components. In the full-rank GMM fitting formulation, both memory-usage and computational cost are proportional to the number of Gaussian components. In the reduced DOF GMM fitting formulation, the covariance matrix of the newly added Gaussian component is efficiently parameterized, using products of a low number of vectors and their transposes, whereas the other Gaussian components are simply modified by multipliers. Thus, memory usage and computational cost increase only modestly as the number of Gaussian components increases.

Unknown GMM parameters, including the parameterized covariance matrix and mixture weighting factor for each Gaussian component, are first determined by minimizing the error that measures the distance between the GMM approximation and the actual posterior PDF. Then, performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method is found to perform more efficiently than the full-rank GMM fitting formulation, e.g., it further reduces the memory usage and computational cost by a factor of 5 to 10, while it achieves comparable accuracy. Although it is less efficient than the L-GMM approximation based on local linearization, it achieves much higher accuracy, e.g., it manages to further reduce the error by a factor of 20 to 600.

Finally, the new method together with the parallelized acceptance-rejection (AR) algorithm is applied to a history matching problem. It is found to reduce the computational cost (i.e., the number of simulations required to generate an accepted conditional realization on average) by a factor of 200 when compared with the Markov chain Monte Carlo (MCMC) method, while the quality of accepted GMM samples is comparable to the MCMC samples. Uncertainty of reservoir model parameters and production forecasts can be properly quantified with accepted GMM samples by conditioning to production data.
Distributed Parallel Hybrid CPU-GPGPU Implementation of the Phase-Field Method for Accelerated High-Accuracy Simulations of Pore-Scale Two-Phase Flow
Authors: Thiele Christopher , Araya-Polo Mauricio , Alpak Faruk Omer , Riviere Beatrice
Published: March 2019

Direct numerical simulation of multi-phase pore-scale flow is a computationally demanding task with strong requirements on time-to-solution for the prediction of relative permeabilities. In this paper, we describe the hybrid-parallel implementation of a two-phase two-component incompressible flow simulator using MPI, OpenMP, and general-purpose graphics processing units (GPUs), and we analyze its computational performance. In particular, we evaluate the parallel performance of GPU-based iterative linear solvers for this application, and we compare them to CPU-based implementations of the same solver algorithms. Simulations on real-life Berea sandstone micro-CT images are used to assess the strong scalability and computational performance of the different solver implementations and their effect on time-to-solution. Additionally, we use a Poisson problem to further characterize achievable strong and weak scalability of the GPU-based solvers in reproducible experiments. Our experiments show that GPU-based iterative solvers can greatly reduce time-to-solution in complex pore-scale simulations. On the other hand, strong scalability is currently limited by the unbalanced computing capacities of the host and the GPUs. The experiments with the Poisson problem indicate that GPU-based iterative solvers are efficient when weak scalability is desired. Our findings show that proper utilization of GPUs can help to make our two-phase pore-scale flow simulation computationally feasible in existing workflows.
Investigation of the Correlation Between the Quality of History Matching and that of Forecasting
Authors: Tabassum Tinni Afifa , King Peter
Published: March 2019

The aim of this study is to determine to what extent the quality of a history matched model is a good predictor of future production. The background is the common assumption that the better a model matches the production data is the better it is for forecasting, or, at the very least, it leads to an improved estimate of the uncertainty in future production. We demonstrate that the validity of this assumption depends on the length of the history match period and that of the forecasting period. It also depends on how heterogeneous the reservoir is.

The correlation between the quality of history match and quality of forecast depends on various factors. For the same level of heterogeneity one of the strongest factors is the water breakthrough time for the base and compared cases.

Broadly if both the base and compared case have water breakthrough before the end of the history match period then the forecasts are reasonable. However, there appears to be a very rapid transition from a reasonably good history match leading to a good forecast to a moderately good history match leading to a very poor forecast. If water breakthrough has not occurred there is a very poor correlation between the quality of the history match and the quality of the forecast. So, the traditional belief that a good history matched model will also produce a good forecast is not always true.
Using Bayesian Model Probability for Ranking Different Prior Scenarios in Reservoir History Matching
Authors: Aanonsen Sigurd Ivar , Tveit Svenn , Alerini Mathias
Published: March 2019

This paper considers Bayesian methods to discriminate between models depending on posterior model probability. When applying ensemble-based methods for model updating or history matching, the uncertainties in the parameters are typically assumed to be univariate Gaussian random fields. In reality, however, there often might be several alternative scenarios that are possible a priori. We take that into account by applying the concepts of model likelihood and model probability and suggest a method that uses importance sampling to estimate these quantities from the prior and posterior ensembles. In particular, we focus on the problem of conditioning a dynamic reservoir-simulation model to frequent 4D-seismic data (e.g., permanent-reservoir-monitoring data) by tuning the top reservoir surface given several alternative prior interpretations with uncertainty. However, the methodology can easily be applied to similar problems, such as fault location and reservoir compartmentalization. Although the estimated posterior model probabilities will be uncertain, the ranking of models according to estimated probabilities appears to be quite robust.
An Innovative Solution for Horizontal Well Optimization: A Techno-Economic Assessment
Authors: Alshmakhy Ahmed , Habib Arslan
Published: March 2019

Horizontal wells are extensively drillied in many ADNOC fields present several challenges. Limited accessibility, uneven stimulation and early breakthrough of the water are main challenges. Early water breakthrough from the heal section results into early shut-in of the well affecting production and recovery adversely. In order to optimize productivity of horizontal wells, an innovative technique, Production Segmentation Completion Techniue (PSCT) was proposed and successfully implemented leading to incremental revenues and saving in both capital and operating expenses. The design comprises running a dual completion, where long string (LS) isolates the hoirizontal well into two sections, near the heel and rest, with the help of two swellable packers. The LS produces from rest of the well while the near heel section is producecd through the short string (SS) through a specially designed perforated spool. Design provided a method to choose appropriate horizontal section for isolation and demonstrated value of having such dual completions in terms of oil gain and water management as well as improved accessibility. This paper presents an out of box solution where the target reservoir was successfully produced through dual completion in the same horizontal hole. This technique enabled reducing the number of wells, the production increase of 1.5 times was realized. To estimate value creation from use of this technology, a simulated study was conducted on a hypothetical field of 20 wells and 20 MMstb recoverable oil. The investment methodology used a combination of discounted cash flow (DCF) and decision tree approach to appropriately capture the value addition while considering risk probabilities at each decision point. The results estimate savings of several million US dollars by application of this rtechnique. The unique techno-economic approach paves a way to benchmark this technique with competing technologies.
Comparison of Performance Characteristics of DxN VERIS System versus Qiagen PCR for HBV Genotype D and HCV Genotype 1b Quantification
Authors: SAYAN MURAT , ARIKAN AYSE , SANLIDAG TAMER
Published: March 2019

The Beckman Coulter DxN VERIS system is a fully automated, closed molecular diagnostic instrument for viral load quantification of hepatitis B virus and hepatitis C virus. In this study, the analytical performance of this new system was compared to routine diagnostic Qiagen PCR kit by using the same clinical samples. The DxN VERIS system demonstrated a high analytical performance. The DxN VERIS allows random access, which means that samples can be uploaded straight on to the system at any time; so, it provides an improvement of workflow, staff productivity and allows faster turn-around of viral load results.
Phoxite, (NH4)2Mg2(C2O4)(PO3OH)2(H2O)4, the first phosphate-oxalate mineral
Authors: Kampf Anthony R. , Celestian Aaron J. , Nash Barbara P. , Marty Joe
Published: March 2019

Phoxite, (NH4)2Mg2(C2O4)(PO3OH)2(H2O)4, is a new mineral species from the Rowley mine, Maricopa County, Arizona, U.S.A., and it has potential uses in agricultural applications for soil conditioning, fertilizing, and as a natural pesticide. It was found in an unusual bat-guano-related, post-mining assemblage of phases that include a variety of vanadates, phosphates, oxalates, and chlorides, some containing NH4+. Other secondary minerals found in association with phoxite are antipinite, aphithitalite, bassanite, struvite, thenardite, and weddellite. Crystals of phoxite are colorless composite blades up to about 0.4 mm. The streak is white, and the luster is vitreous to oily. The Mohs hardness is 2½, the tenacity is brittle, fracture is irregular, there is fair {100} cleavage, and the measured density is 1.98(2) g/cm3. Phoxite is optically biaxial (–) with α = 1.499(1), β = 1.541(1), γ = 1.542(1) (white light); 2V = 16(1)°; dispersion r < ν, slight; orientation Y = b, X ^ a ≈ 9° in obtuse β. Electron microprobe analyses yielded the empirical formula [(NH4)1.77K0.23]Σ2Mg2.00(C2O4)(PO3OH)2(H2O)4, with the C and H content inferred from the crystal structure. Raman spectroscopy confirmed the presence of NH4 and C2O4. Phoxite is monoclinic, P21/c, with a = 7.2962(3), b = 13.5993(4), c = 7.8334(6) Å, β = 108.271(8)°, V = 738.07(7) Å3, and Z = 2. In the crystal structure of phoxite (R1 = 0.0275 for 1147 Io > 2σI reflections), bidentate linkages between C2O4 groups and Mg-centered octahedra yield chains, which link to one another via PO3OH tetrahedra to create undulating[Mg2(C2O4)(PO3OH)2(H2O)4]2– sheets. Strong hydrogen bonds link the sheets into a “soft framework,” with channels containing NH4+. The NH4+ forms both ordered hydrogen bonds and electrostatic bonds with O atoms in the framework. Phoxite is the first mineral known to contain both phosphate and oxalate groups as essential components.
Estimating the Parameter of Weighted Ailamujia Distribution using Bayesian Approximation Techniques
Authors: Jan Uzma , Ahmad S P
Published: March 2019

From table 5.1 and 5.2, it is clearly evident that the posterior variance of gamma prior is less than the other priors under both the approximation techniques especially when the value of both the hyper parameters a1 and b1 is taken as 2. Further, it is also noted that the values of normal approximation are less than the T-K approximation for all the three data sets.
Bayesian Inference for Exponential Rayleigh Distribution Using R Software
Authors: Fatima Kawsar , Ahmad S P
Published: March 2019

In this chapter, we have paralleled the Baye’s estimates of the parameter of the ExponentialRayleigh (ER) distribution under extension of Jeffrey’s prior and gamma prior using different loss functions with that of maximum likelihood estimate. From the results, Al-Bayyati’s loss function gives the minimum MSE as compared to the other loss functions and among the priors Gamma prior gives the less MSE than other assumed priors.
Diéguez, A. (2017). Transhumanismo: La búsqueda tecnológica del mejoramiento humano. Barcelona: Herder. 248pp.
Authors: Cascales Tornel Raquel
Published: March 2019

Reseña del libro de Antonio Diéguez,  Transhumanismo: La búsqueda tecnológica del mejoramiento humano, Herder, Barcelona, 2017, 248 págs.
Correspondencias en la epistemología y la filosofía política de Luis Villoro. Análisis y evaluación de una propuesta interpretativa
Authors: García Iver Armando Beltrán
Published: March 2019

Mediante el análisis de conceptos y la comparación con las fuentes originales, este artículo evalúa la propuesta interpretativa de Mario Teodoro Ramírez sobre las correspondencias o analogías entre el pensamiento epistemológico y el pensamiento ético-político de Luis Villoro. Se concluye que, aunque integra elementos que deben ser rechazados, reemplazados o replanteados, y no obstante el considerable número de precisiones que exige para no ser malinterpretada, la propuesta es fundamentalmente correcta, además de que arroja luz sobre la unidad sistemática de ese pensamiento y sobre el espíritu que lo anima. La importancia del artículo reside en que, exhibiendo la compleja articulación de los principales conceptos en la epistemología y la ética política de Villoro, contribuye a la mejor comprensión de la filosofía de este pensador, y con ello, a la reflexión actual sobre el nexo entre el conocimiento, la ética y la política.
Kinetic study of dye removal using TiO2 supported on polyethylene terephthalate by advanced oxidation processes through neural networks
Authors: Barbosa Ada Azevedo , de Aquino Ramon Vinicius Santos , da Cruz Santana Neves Naiana Santos , Dantas Renato Falcão , Duarte Marta Maria Menezes Bezerra , Rossiter Sá da Rocha Otidene
Published: March 2019

This work investigated the efficiency of polyethylene terephthalate (PET) as support material for TiO2 films in the photocatalytic degradation of red Bordeaux and yellow tartrazine dyes. The optimum operating conditions were determined by a factorial design, which resulted after 180 min of treatment in degradations of 99.5% and 99.1% for the UVC/H2O2/TiO2Sup and solar/H2O2/TiO2Sup systems, respectively. For the kinetic study, the experimental data fitted to the pseudo-first-order model and the calculated kinetic constants (k) values were 0.03 min−1 for the UVC/H2O2/TiO2Sup system and 0.0213 min−1 for the system solar/H2O2/TiO2Sup. It was verified that TiO2 supported in the PET remained with high degradation efficiency even after five cycles of reuse, indicating a good stability of the photocatalyst in the support. A significant reduction of TOC content was also observed along the reaction time. The phytotoxicity bioassay with Lactuca sativa demonstrated that after treatment with UVC/H2O2/TiO2Sup and solar/H2O2/TiO2SUP, an increase in IC50 and consequently lower toxicity was observed.
A simple sensitivity analysis of the turbulence-induced flocculation model of cohesive sediment
Authors: Zhu Zhongfan
Published: March 2019

In this study, the local and global sensitivity analyses of the Winterwerp model to the input parameters have been carried out using the Garson algorithm and the PaD2 method by virtue of an artificial neural network. The main results of the sensitivity analyses are that the model is most sensitive to the breakup parameter and that only two parameters (the floc aggregation and breakup parameters) are significant. The result that the model output is less sensitive to the choice of fractal dimension seems to imply that the modification work on the fractal dimension might be unnecessary.
Antenatal Corticosteroids for Fetal Lung Maturity - Too Much of a Good Thing?
Authors: Hrabalkova Lenka , Takahashi Tsukasa , Kemp Matthew W. , Stock Sarah J.
Published: March 2019

Background:

Between 5-15% of babies are born prematurely worldwide, with preterm birth defined as delivery before 37 completed weeks of pregnancy (term is at 40 weeks of gestation). Women at risk of preterm birth receive antenatal corticosteroids as part of standard care to accelerate fetal lung maturation and thus improve neonatal outcomes in the event of delivery. As a consequence of this treatment, the entire fetal organ system is exposed to the administered corticosteroids. The implications of this exposure, particularly the long-term impacts on offspring health, are poorly understood.

Aims:

This review will consider the origins of antenatal corticosteroid treatment and variations in current clinical practices surrounding the treatment. The limitations in the evidence base supporting the use of antenatal corticosteroids and the evidence of potential harm to offspring are also summarised.

Results:

Little has been done to optimise the dose and formulation of antenatal corticosteroid treatment since the first clinical trial in 1972. International guidelines for the use of the treatment lack clarity regarding the recommended type of corticosteroid and the gestational window of treatment administration. Furthermore, clinical trials cited in the most recent Cochrane Review have limitations which should be taken into account when considering the use of antenatal corticosteroids in clinical practice. Lastly, there is limited evidence regarding the long-term effects on the different fetal organ systems exposed in utero, particularly when the timing of corticosteroid administration is sub-optimal.

Conclusion:

Further investigations are urgently needed to determine the most safe and effective treatment regimen for antenatal corticosteroids, particularly regarding the type of corticosteroid and optimal gestational window of administration. A clear consensus on the use of this common treatment could maximise the benefits and minimise potential harms to offspring.