bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Introgression of Pup1 Locus on Rice Seedling under Phosphorus Deficiency
Authors: Hidayatun Nurul , Prasetiyono Joko 
Published: March 2019

<p>The lack of soil-phosphorus (P) element will result in plant growth retardation. Plants could survive in P deficiency stress by increasing the ability of P uptake or by increasing the efficiency in the P utilization. The aims of this study were to understand genetic composition of rice genotypes possessing Pup1 locus and to know root and leaf growth responses at different P availability condition. The three rice genotypes (IR74, IR74-Pup1, and Kasalath) were analyzed for their genetic composition using SNP markers. The phenotypic experiment was arranged using a Completely Randomized Design with four replications and performed hydroponically in nutrient solution with different availability of P. The result showed that IR74-Pup1 had 84.4% similarities to its parent (IR74) with 13.6% of donor segments, where the Pup1 locus located. The influence of Pup1 locus<br />introgression on total length, surface area, diameter, and volume of the root varied at each growth stage. IR74 and IR74-Pup1 had root and leaf growth restriction on low P, but Pup1 locus introgression showed better growth performance, both in normal P and in low P conditions. The introgression of Pup1 locus increases plant ability to reduce the impact of growth inhibition<br />caused by P deficiency. </p>
An Integrated Carbon-Dioxide-Foam Enhanced-Oil-Recovery Pilot Program With Combined Carbon Capture, Utilization, and Storage in an Onshore Texas Heterogeneous Carbonate Field
Authors: Alcorn Z. P. , Fredriksen S. B. , Sharma M.. , Rognmo A. U. , Føyen T. L. , Fernø M. A. , Graue A.. 
Published: March 2019

SummaryA carbon–dioxide (CO2) –foam enhanced–oil–recovery (EOR) field pilot research program has been started to advance the technology of CO2 foam for mobility control in a heterogeneous carbonate reservoir. Increased oil recovery with associated anthropogenic–CO2 storage is a promising technology for mitigating global warming as part of carbon capture, utilization, and storage (CCUS). Previous field tests with CO2 foam report various results because of injectivity problems and the difficulty of attributing fluid displacement specifically to CO2 foam. Thus, a comprehensive integrated multiscale methodology is required for project design to better link laboratory– and field–scale displacement mechanisms. This study presents an integrated upscaling approach for designing a miscible CO2–foam field trial, including pilot–well–selection criteria and laboratory corefloods combined with reservoir–scale simulation to offer recommendations for the injection of alternating slugs of surfactant solution and CO2, or surfactant–alternating–gas (SAG) injection, while assessing CO2–storage potential.Laboratory investigations include dynamic aging, foam–stability scans, CO2–foam EOR corefloods with associated CO2 storage, and unsteady–state CO2/water endpoint relative permeability measurements. Tertiary CO2–foam EOR corefloods at oil–wet conditions result in a total recovery factor of 80% of original oil in place (OOIP), with an incremental recovery of 30% of OOIP by CO2 foam after waterflooding. Stable CO2 foam, using aqueous surfactants with a gas fraction of 0.70, provided mobility–reduction factors (MRFs) up to 340 compared with pure–CO2 injection at reservoir conditions. Oil recovery, gas–mobility reduction, producing–gas/oil ratio (GOR), and CO2 utilization at field pilot scale were investigated with a validated numerical model. Simulation studies show the effectiveness of foam to reduce gas mobility, improve CO2 utilization, and decrease GOR.
Artificial Neural Network Modeling of Compaction-Dilation Data for Unconventional Oil Reservoirs
Authors: Fedutenko Eugene , Nghiem Long , Yang Chaodong , Chen Tong , Seifi Mojtaba 
Published: March 2019

Abstract
The incorporation of geomechanical effects into the flow simulation is crucial for accurate modeling of unconventional hydraulic fracture systems. Such an incorporation requires a proper construction of stress and pressure dependent permeability/porosity distribution for the whole reservoir domain. Usually this is accomplished by the generation of the correspondent multipliers for porosity and permeability under the assumption of exponential and power law correlations. This task can become complicated for sandstones that display non-linear and inelastic behavior such as hysteresis when subjected to cyclic loading, as it involves many parameters including different moduli for loading and unloading introduced as best fit for experimental values.
We propose a novel Machine Learning approach to this problem based on Multi-Layer Neural Network (MLNN) Modeling of hysteresis nonlinearity caused by Elastic, Dilation, and Compaction compressibility term. MLNN considers the experimentally obtained pressure dependency of permeability and/or porosity for Elastic, Dilation and Maximum Compaction as its training data. After the Network is trained it is used to predict the multiplier curve for any intermediate (i.e. low, middle, and high) compactions. This is accomplished by MLNN hysteresis function which considers the curve forecast as a pattern identification problem. Such an approach is often used for modeling of hysteresis in piezo-actuators and magnetic systems, however it has never been applied to geomechanical modeling before. This paper modifies the classical MLNN in the way that it exactly matches the training data, i.e. honors the boundary conditions for dilation-compaction hysteresis. A history match of a couple of field examples indicate that the proposed model provides a good match with production data.
Dynamic Modeling and Design Optimization of Cyclonic Autonomous Inflow Control Devices
Authors: Gurses Sule , Chochua Gocha , Rudic Aleksandar , Kumar Amrendra 
Published: March 2019

Abstract
Autonomous inflow control devices (AICDs) have recently been introduced in the petroleum industry to restrict the production of unwanted fluids, namely water and gas, much more effectively than conventional inflow control devices (ICDs). As with ICDs, AICDs are installed downhole along the completion string to first delay water/gas coning and then restrict their influx, without well intervention, if/when coning such occurs. Unlike ICDs, AICDs selectively choke back water and gas significantly more so than oil.
A novel cyclonic AICD was recently developed using computational fluid dynamics (CFD) driven design optimization. The cyclonic AICD's unique internal geometry increases the flow resistance to unwanted fluids based on how their viscosities and densities differ from oil, as initially predicted using CFD and subsequently validated by extensive, carefully controlled single- and two-phase flow tests. The resulting excellent match obtained between CFD and such laboratory tests yielded accurate mathematical models for predicting flow performance over a broad range of flow rates and oil, water and gas properties.
The flow performance models were then incorporated into a state-of-the-art dynamic reservoir simulator with multi-segmented wellbore capability to compare the production performance over time for the same well but completed with no ICDs, conventional ICDs, and cyclonic AICDs. A synthetic but realistic three- dimensional (3-D) reservoir model has used that allowed oil, gas and water production. Multiple sensitivity runs were initially performed to optimize the number of compartments using packers for annular isolation, and the number of ICDs per compartment. Once these parameters were optimized, only the ICD type was varied for performance comparison.
The results of this systematic, multi-step process, as presented herein, demonstrate that the cyclonic AICD adds significant value to the improvement of oil production by controlling unwanted fluids, such as water and gas, and by preserving the reservoir energy.
A Multiphysics Fully-Coupled Flow and Geomechanics Simulation System with Hydraulic-Fracturing Simulation Capability
Authors: Alpak Faruk O. 
Published: March 2019

Abstract
A modular multiphysics reservoir simulation system is developed that has the capability of simulating multiphase-multicomponent-thermal flow, poro-elasto-plastic geomechanics, and hydraulic-fracture evolution. The focus of the work is on the full-physics numerical hydraulic-fracture evolution simulation capability of the multiphysics simulation system. Fracture growth computations utilize a cohesive zone model as part of the computation of fracture propagation criterion. The cohesive zone concept is developed based on energy-release rates and cohesive stresses. They capture the strain-softening behavior of deforming porous material consistent with real-life observations of poro-plastic deformation. Thus, they can be reliably used within both poro-elastic and poro-plastic geomechanics applications unlike the conventional stress-intensity-factor-based fracture propagation criterion.
The partial differential equations that govern the Darcy-scale multiphase-multicomponent-thermal flow, poro-elasto-plastic geomechanics, hydraulic-fracture evolution, and laminar channel flow in the fracture are tightly coupled to each other to give rise to a numerical protocol solvable by the fully-implicit method. The ensuing nonlinear system of equations is solved by use of a novel adaptively damped Newton-Raphson method.
Example fully-coupled single-phase isothermal flow, geomechanics, and hydraulic-fracture growth simulations are analyzed to demonstrate the predictive power of the simulation system. Numerical model predictions of fracture length/radius and width are validated against analytical solutions for plane-strain and ellipsoid-shaped fractures, respectively. Results indicate that the simulator is capable of modeling hydraulic-fracture evolution accurately by use of the cohesive zone model as the propagation criterion. We also simulate and explore the sensitivities around a real-life hydraulic-fracture growth problem by fully accounting for the thermal, multiphase, and compositional flow effects.
Unconventional Reservoir Management Modeling Coupling Diffusive Zone/Phase Field Fracture Modeling and Fracture Probability Maps
Authors: Wheeler Mary F. , Srinivasan Sanjay , Lee Sanghyun , Singh Manik 
Published: March 2019

Abstract
Optimal design of hydraulic fractures is controlled by the distribution of natural fractures in the reservoir. Due to sparse information, there is uncertainty associated with the prediction of the natural fracture system. Our objective here is to: i) Quantify uncertainty associated with prediction of natural fractures using micro-seismic data and a Bayesian model selection approach, and ii) Use fracture probability maps to implement a finite element phase-field approach for modeling interactions of propagating fractures with natural fractures.
The proposed approach employs state-of-the-art numerical modeling of natural and hydraulic fractures using a diffusive adaptive finite element phase-field approach. The diffusive phase field is defined using the probability map describing the uncertainty in the spatial distribution of natural fractures. That probability map is computed using a model selection procedure that utilizes a suite of prior models for the natural fracture network and a fast proxy to quickly evaluate the forward seismic response corresponding to slip events along fractures. Employing indicator functions, diffusive fracture networks are generated utilizing an accurate computational adaptive mesh scheme based on a posteriori error estimators.
The coupled algorithm was validated with existing benchmark problems which include prototype computations with fracture propagation and reservoir flows in a highly heterogeneous reservoir with natural fractures. Implementation of a algorithm for computing fracture probability map based on synthetic micro-seismic data mimicking a Fort Worth basin data set reveals consistency between the interpreted fracture sets and those observed in the reference. Convergence of iterative solvers and numerical efficiencies of the methods were tested against different examples including field-scale problems. Results reveal that the interpretation of uncertainty pertaining to the presence of fractures and utilizing that uncertainty within the phase field approach to simulate the interactions between induced and natural fracture yields complex structures that include fracture branching, fracture hooking etc.
The novelty of this work lies in the efficient integration of the phase-field fracture propagation models to diffusive natural fracture networks with stochastic representation of uncertainty associated with the prediction of natural fractures in a reservoir. The presented method enables practicing engineers to design hydraulic fracturing treatment accounting for the uncertainty associated with the location and spatial variations in natural fractures. Together with efficient parallel implementation, our approach allows for cost-efficient approach to optimizing production processes in the field.
Non-Intrusive Reduced Order Modelling for Reconstruction of Saturation Distributions
Authors: Kostorz Wawrzyniec , Muggeridge Ann , Jackson Matthew , Moncorge Arthur 
Published: March 2019

Abstract
Two new Non-Intrusive Reduced Order Modelling approaches to estimate time varying, spatial distributions of variables from arbitrary unseen inputs are introduced. One is a generalization of an existing ‘dynamic’ approach which requires multiple surrogate evaluations to model the solutions at different time instances, the other is a ‘steady-state’ approach that evaluates all time instances simultaneously, reducing the local approximation error.
The ability of these approaches to estimate the water saturation distributions expected during a gas flood through a 2D, dipping reservoir is investigating for a range of unseen input parameters. The range of these parameters has been chosen so that a range of flow regimes will occur, from a gravity tongue to a viscous dominated Buckley-Leverett displacement. A number of practically relevant model error measures were employed as opposed to the standard L2 (Euclidean) norm. The influence of the number and the structure of training simulations for the model was also investigated, by employing two simple experimental design methods.
The results show that POD based NIROM approaches are prone to significant deviations from the true model. The main sources of error are due to the non-smooth variation of system responses in hyperspace and the transient nature of the flows as well as the underlying dimensionality reduction. Since the first two sources are properties of the physical system modelled it may be expected that similar problems are likely to arise independently of the interpolation method and the reduction process used.
Identifiability of Model Discrepancy Parameters in History Matching
Authors: Rammay Muzammil H. , Elsheikh Ahmed H. , Chen Yan 
Published: March 2019

Abstract
In this work, we investigate different approaches for history matching of imperfect reservoir models while accounting for model error. The first approach (base case scenario) relies on direct Bayesian inversion using iterative ensemble smoothing with annealing schedules without accounting for model error. In the second approach the residual, obtained after calibration, is used to iteratively update the covariance matrix of the total error, that is a combination of model error and data error. In the third approach, PCA-based error model is used to represent the model discrepancy during history matching. However, the prior for the PCA weights is quite subjective and is generally hard to define. Here the prior statistics of model error parameters are estimated using pairs of accurate and inaccurate models. The fourth approach, inspired from Köpke et al. (2017), relies on building an orthogonal basis for the error model misfit component, which is obtained from difference between PCA-based error model and corresponding actual realizations of prior error. The fifth approach is similar to third approach, however the additional covariance matrix of error model misfit is also computed from the prior model error statistics and added into the covariance matrix of the measurement error. The sixth approach, inspired from Oliver and Alfonzo (2018), is the combination of second and third approach, i.e. PCA-based error model is used along with the iterative update of the covariance matrix of the total error during history matching. Based on the results, we conclude that a good parameterization of the error model is needed in order to obtain good estimate of physical model parameters and to provide better predictions. In this study, the last three approaches (i.e. 4, 5, 6) outperform the others in terms of the quality of the estimated parameters and the prediction accuracy (reliability of the calibrated models).
Numerical and Experimental Analysis of Diffusion and Sorption Kinetics Effects in Marcellus Shale Gas Transport
Authors: Zhang Miao , Chakraborty Nirjhor , Karpyn Zuleima , Emami-Meybodi Hamid , Ayala Luis 
Published: March 2019

Abstract
Nano-scale pores and a dual storage mechanism shared between free and adsorbed gas make the transport behavior in shale gas reservoirs very different from conventional macropore reservoirs. This work explores a straightforward model for the gas transport behavior in shale nanopores, which couples sorption, diffusion, and sorbed-phase surface diffusion phenomena. The model combines two governing equations for free and sorbed gas phase transport processes in nanopores, respectively: a diffusion-based equation for free gas phase transport, and a surface-diffusion equation for the sorbed phase. Mass transfer between the two phases is quantified by kinetic models of sorption. The two governing equations are solved simultaneously using finite element methods (FEM). Model performance is successfully validated by closely matching density propagation profiles of a gas transport experiment obtained by quantitative X-ray computerized tomography (CT) imaging for a Marcellus shale sample. Transport-related parameters estimated from history matching are shown to be consistent with literature data.
An Open Source Numerical Framework for Dual-Continuum Geomechanical Simulation
Authors: Ashworth Mark , Doster Florian 
Published: March 2019

Abstract
Modelling multiscale-multiphysics geology at field scales is non-trivial due to computational resources and data availability. At such scales it is common to use implicit modelling approaches as they remain a practical method of understanding the first order processes of complex systems. In this work we introduce a numerical framework for the simulation of geomechanical dual-continuum materials. Our framework is written as part of the open source MATLAB Reservoir Simulation Toolbox (MRST). We discretise the flow and mechanics problems using the finite volume method (FVM) and virtual element method (VEM) respectively. The result is a framework that ensures local mass conservation with respect to flow and is robust with respect to gridding. Solution of the coupled linear system can be achieved with either fully coupled or fixed-stress split solution strategies. We demonstrate our framework on an analytical comparison case and on a 3D geological grid case. In the former we observe a good match between analytical and numerical results, for both fully coupled and fixed-stress split strategies. In the latter, the geological model is gridded using a corner point grid that contains degenerate cells as well as hanging nodes. For the geological case, we observe physically plausible and intuitive results given the boundary conditions of the problem. Our initial testing with the framework suggests that the FEM-VEM discretisation has potential for conducting practical geomechanical studies of multiscale systems.
Optimal Design of CO2 Sequestration with Three-Way Coupling of Flow-Geomechanics Simulations and Evolution Strategy
Authors: Lu Xueying , Ganis Benjamin , Wheeler Mary F. 
Published: March 2019

Abstract
Coupled reservoir flow and geomechanics has numerous important applications in the oil & gas industry, such as land subsidence, hydraulic fracturing, fault reaction and hydrocarbon recovery etc. High fidelity numerical schemes and multiphysics models must be coupled in order to simulate these processes and their interactions accurately and efficiently. Specifically, in the applications of CO2 sequestration, the effect of geomechanics on carbon storage estimation is not negligible. However, coupled flow-geomechanics simulations are very computationally expensive and most of the computational time is usually spent for geomechanics calculations. This paper investigates a three-way coupling algorithm that uses an error indicator to determine when displacement must be updated and whether fixed-stress iterative coupling technique is required. Numerical experiments with coupled nonlinear single-phase flow and linear poromechanics shows that the three-way coupling algorithm can speed up 4 times comparing to fixed-stress iterative coupling algorithm. Extensions to coupled compositional flow with poromechanics also shows a speed-up for 5 times for continuous CO2 sequestration applications and 2 times for surfactant-alternating-gas applications (SAG). The substantial speed up makes the three-way coupling algorithm of flow and geomechanics feasible in the large-scale optimizations. Based on the three-way coupling of compositional flow and geomechanics, we experimented two black box optimization algorithms, covariance-matrix adaptation evolution strategy (CMA-ES) and genetic algorithm (GA), for the optimization of well controls during SAG process to maximize CO2 storage volume. CMA-ES outperforms GA in that it is more robust, and it achieves higher objective function value in less simulation runs. The optimized SAG process achieves 27.55% more CO2 storage volume and reduces water and surfactant consumption by 54.84%.
General Semi-Structured Discretization for Flow and Geomechanics on Diffusive Fracture Networks
Authors: Jammoul Mohamad , Ganis Benjamin , Wheeler Mary 
Published: March 2019

Abstract
A novel approach is introduced for simulation of multiphase flow, geomechanics, and fracture propagation on very general semi-structured grids. Complex networks consisting of both natural and hydraulically stimulated fractures are able to be represented using a diffusive zone model in large scale reservoirs. A mass conservative method called the enhanced velocity mixed finite element method is used to model multiphase flow with a fully-compositional equation-of-state model. Its recent reformulation on semi-structured, spatially non-conforming grids allows very general local refinement and dynamic mesh adaptivity.
Iteratively coupled geomechanics is simulated, which can predict fracture opening on fixed networks based upon induced stresses and poromechanical effects. In the most complex case, it is coupled with the phase field method to model nucleation and branching of non-planar fractures in highly heterogeneous media. Several examples are demonstrated to model fracture networks. The general semi-structured discretization can simulate flow and geomechanics on networks of fractures in large reservoirs with local resolution where desired. Dynamic adaptive mesh refinement can be used for both tracking transient flow features such as sharp the propagation of new fractures via hydraulic stimulation. This framework allows the seamless ability to switch from production to propagation scenarios, by varying the degrees of physics.
This work demonstrates a capability to perform high-fidelity simulations on complex fracture networks in large reservoirs at a reasonable computational cost. The gridding algorithms are straightforward extensions to traditional finite difference reservoir simulators. It can also be coupled with state-of-the-art complex phase field fracture propagation. This extends the capabilities of many legacy reservoir simulators to handle more physics.