bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Summary
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

Abstract
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

Abstract
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

Abstract
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.
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.
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

Abstract
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.