The Experts below are selected from a list of 26091 Experts worldwide ranked by ideXlab platform
Joseph Sangil Kwon - One of the best experts on this subject based on the ideXlab platform.
-
Koopman operator-based model identification and control of hydraulic fracture propagation
2020 American Control Conference (ACC), 2020Co-Authors: Abhinav Narasingam, Joseph Sangil KwonAbstract:The moving boundary nature of hydraulic Fracturing Process makes it extremely difficult to approximate using local models (i.e., approximate models whose validity is limited by the training data). In this work, we implement the Koopman operator methodology for closed-loop control of fracture propagation. Koopman theory models nonlinear dynamical systems as linear systems by lifting the states to an infinite-dimensional space of functions called observables. It is particularly attractive because of its ability to provide (nearly) global linearization valid in a larger domain (in some cases the entire basin of attraction) compared to local models. Additionally, due to its linear structure, it allows convex predictive control formulations that avoid any issues associated with nonlinear optimization. The numerical results show that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired fracture geometry.
-
deep hybrid modeling of chemical Process application to hydraulic Fracturing
Computers & Chemical Engineering, 2020Co-Authors: Mohammed Saad Faizan Bangi, Joseph Sangil KwonAbstract:Abstract Process modeling began with the use of first principles resulting in ‘white-box’ models which are complex but accurately explain the dynamics of the Process. Recently, there has been tremendous interest towards data-based modeling as the resultant ‘black-box’ models are simple, and easy to construct, but their accuracy is highly dependent on the nature and amount of training data used. In order to balance the advantages and disadvantages of ‘white-box’ and ‘black-box’ models, we propose a hybrid model that integrates first principles with a deep neural network, and applied it to hydraulic Fracturing Process. The unknown Process parameters in the hydraulic Fracturing Process are predicted by the deep neural network and then utilized by the first principles model in order to calculate the hybrid model outputs. This hybrid model is easier to analyze, interpret, and extrapolate compared to a ‘black-box’ model, and has higher accuracy compared to the first principles model.
-
Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition methodology
2018 Annual American Control Conference (ACC), 2018Co-Authors: Harwinder Singh Sidhu, Abhinav Narasingam, Joseph Sangil KwonAbstract:Developing reduced-order models for nonlinear parabolic partial differential equation (PDE) systems with time-varying spatial domains remains a key challenge as the dominant spatial patterns of the system change with time. Within this context, there have been several studies where the time-varying spatial domain is transformed to the time-invariant spatial domain by using an analytical expression that describes how the spatial domain changes with time. However, this information is not available in many real-world applications and therefore, the approach is not generally applicable. To overcome this challenge, we introduce sparse proper orthogonal decomposition (SPOD)-Galerkin methodology that exploits the key features of ridge and lasso regularization techniques for the model order reduction of such systems. This methodology is successfully applied to a hydraulic Fracturing Process, and a series of simulation results indicates that it is more accurate in approximating the original nonlinear system than the standard POD-Galerkin methodology.
-
temporal clustering for order reduction of nonlinear parabolic pde systems with time dependent spatial domains application to a hydraulic Fracturing Process
Aiche Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sangil KwonAbstract:In this work, we present a temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, we derive low-dimensional models by constructing appropriate temporally-local eigenfunctions. Within this context, we partition the time domain into multiple clusters (i.e. subdomains) by using the framework known as global optimum search (GOS). This approach, a variant of Generalized Benders Decomposition (GBD), formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition (POD) method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions. This article is protected by copyright. All rights reserved.
Abhinav Narasingam - One of the best experts on this subject based on the ideXlab platform.
-
Koopman operator-based model identification and control of hydraulic fracture propagation
2020 American Control Conference (ACC), 2020Co-Authors: Abhinav Narasingam, Joseph Sangil KwonAbstract:The moving boundary nature of hydraulic Fracturing Process makes it extremely difficult to approximate using local models (i.e., approximate models whose validity is limited by the training data). In this work, we implement the Koopman operator methodology for closed-loop control of fracture propagation. Koopman theory models nonlinear dynamical systems as linear systems by lifting the states to an infinite-dimensional space of functions called observables. It is particularly attractive because of its ability to provide (nearly) global linearization valid in a larger domain (in some cases the entire basin of attraction) compared to local models. Additionally, due to its linear structure, it allows convex predictive control formulations that avoid any issues associated with nonlinear optimization. The numerical results show that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired fracture geometry.
-
Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition methodology
2018 Annual American Control Conference (ACC), 2018Co-Authors: Harwinder Singh Sidhu, Abhinav Narasingam, Joseph Sangil KwonAbstract:Developing reduced-order models for nonlinear parabolic partial differential equation (PDE) systems with time-varying spatial domains remains a key challenge as the dominant spatial patterns of the system change with time. Within this context, there have been several studies where the time-varying spatial domain is transformed to the time-invariant spatial domain by using an analytical expression that describes how the spatial domain changes with time. However, this information is not available in many real-world applications and therefore, the approach is not generally applicable. To overcome this challenge, we introduce sparse proper orthogonal decomposition (SPOD)-Galerkin methodology that exploits the key features of ridge and lasso regularization techniques for the model order reduction of such systems. This methodology is successfully applied to a hydraulic Fracturing Process, and a series of simulation results indicates that it is more accurate in approximating the original nonlinear system than the standard POD-Galerkin methodology.
-
temporal clustering for order reduction of nonlinear parabolic pde systems with time dependent spatial domains application to a hydraulic Fracturing Process
Aiche Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sangil KwonAbstract:In this work, we present a temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, we derive low-dimensional models by constructing appropriate temporally-local eigenfunctions. Within this context, we partition the time domain into multiple clusters (i.e. subdomains) by using the framework known as global optimum search (GOS). This approach, a variant of Generalized Benders Decomposition (GBD), formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition (POD) method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions. This article is protected by copyright. All rights reserved.
-
Temporal clustering for order reduction of nonlinear parabolic PDE systems with time-dependent spatial domains: Application to a hydraulic Fracturing Process
AIChE Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sang-il KwonAbstract:© 2017 American Institute of Chemical Engineers.A temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains is presented. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, low-dimensional models are derived by constructing appropriate temporally-local eigenfunctions. Within this context, first of all, the time domain is partitioned into multiple clusters (i.e., subdomains) by using the framework known as global optimum search. This approach, a variant of Generalized Benders Decomposition, formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions.
Prashanth Siddhamshetty - One of the best experts on this subject based on the ideXlab platform.
-
incorporation of sustainability in Process control of hydraulic Fracturing in unconventional reservoirs
Chemical Engineering Research & Design, 2018Co-Authors: Priscille Etoughe, Prashanth Siddhamshetty, Rajib Mukherjee, Joseph Sangii KwonAbstract:Abstract Typically, the term shale oil refers to natural oil trapped in rock of low porosity and ultra-low permeability. What has made the recovery of shale oil and gas economically viable is the extensive use of hydraulic Fracturing and horizontal drilling. Research on the relationship between the distribution of propping agent, called proppant, and shale well performance indicates that uniformity of proppant bank height and suspended proppant concentration across the fracture at the end of pumping determines the productivity of produced wells. However, it is important to note that traditional Fracturing fluid pumping schedules have not considered the environmental and economic impacts of the post-Fracturing Process such as treatment and reuse of flowback water from fractured wells. Motivated by this consideration, a control framework is proposed to integrate sustainability considerations of the post-Fracturing Process into the hydraulic Fracturing Process. In this regard, a dynamic model is developed to describe the flow rate and the concentration of total dissolved solids (TDS) in flowback water from fractured wells. Thermal membrane distillation is considered for the removal of TDS. An optimization problem is formulated to find the optimal Process that consists of hydraulic Fracturing, storage, transportation, and water treatment, through minimizing annualized cost and water footprint of the Process. The capabilities of the proposed approach are illustrated through the simulation results of different scenarios that are performed to examine effects of water availability on the productivity of stimulated wells. Finally, the environmental impact of flowback water treatment is evaluated using TRACI, a tool for the reduction and assessment of chemical and other environmental impacts.
-
temporal clustering for order reduction of nonlinear parabolic pde systems with time dependent spatial domains application to a hydraulic Fracturing Process
Aiche Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sangil KwonAbstract:In this work, we present a temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, we derive low-dimensional models by constructing appropriate temporally-local eigenfunctions. Within this context, we partition the time domain into multiple clusters (i.e. subdomains) by using the framework known as global optimum search (GOS). This approach, a variant of Generalized Benders Decomposition (GBD), formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition (POD) method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions. This article is protected by copyright. All rights reserved.
-
Temporal clustering for order reduction of nonlinear parabolic PDE systems with time-dependent spatial domains: Application to a hydraulic Fracturing Process
AIChE Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sang-il KwonAbstract:© 2017 American Institute of Chemical Engineers.A temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains is presented. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, low-dimensional models are derived by constructing appropriate temporally-local eigenfunctions. Within this context, first of all, the time domain is partitioned into multiple clusters (i.e., subdomains) by using the framework known as global optimum search. This approach, a variant of Generalized Benders Decomposition, formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions.
Xiating Feng - One of the best experts on this subject based on the ideXlab platform.
-
experimental investigation on Fracturing Process of marble under biaxial compression
Journal of rock mechanics and geotechnical engineering, 2020Co-Authors: Zhaofeng Wang, Xiating Feng, Chengxiang Yang, Yangyi Zhou, Hong XuAbstract:Abstract In this study, servo-controlled biaxial compression tests were conducted on marble specimens to investigate their failure characteristics and Fracturing Process. The complete stress-strain curves were obtained, and the three-dimensional (3D) features of the failure surfaces were acquired by 3D laser scanning. Acoustic emission (AE) monitoring and moment tensor (MT) analysis were used in combination to better understand the Fracturing mechanism of marble under biaxial compression. It was noted that a type of 3D stepwise cracking behaviour occurred on the Fracturing surfaces of the examined specimens. The stress dropped multiple times, and a repeated Fracturing mode corresponding to the repeated stress drops in the post-peak regime was observed. Three substages, i.e. stress stabilisation, stress decrease and stress increase, were identified for a single Fracturing mode. Then quantitative and statistical analyses of the Fracturing Process at each substage were discussed. Based on the testing results, it was found that at the stress stabilisation substage, the proportion of mixed-mode fractures increased. At the stress decrease substage, the proportion of mixed-mode fractures decreased, and the tensile or shear fractures increased. At the stress increase substage, the proportion of mixed-mode or tensile fractures decreased, and the shear fractures increased. Finally, a conceptual model for the stepwise crack formation was proposed.
-
a novel application of strain energy for Fracturing Process analysis of hard rock under true triaxial compression
Rock Mechanics and Rock Engineering, 2019Co-Authors: Yan Zhang, Zhaofeng Wang, Xiating Feng, Xiwei Zhang, Mostafa Sharifzadeh, Chengxiang YangAbstract:Energy principles, which can favorably explain the complete rock failure Process, are one of the most reliable analysis approaches in rock mechanics and engineering. In this study, a strain energy approach under true triaxial compression (TTC) is proposed. On this basis, the energy evolution characteristics and variations of different failure behavior types (Class I, Class II and ductile failure) under TTC are analyzed. The variations of the strain energy characteristics of Beishan granite with σ2 and σ3 under TTC are studied. The results indicate that the total strain energy U and the elastic strain energy $$U^{e}$$ of Beishan granite increase with the increasing σ2 or σ3. The dissipated strain energy $$U^{d}$$ rapidly increases when the value of e1/e1peak is approximately 0.6–0.8. The influence of σ3 on the rock failure mode and energy evolution characteristics is greater than that of σ2. In highly brittle rocks, the tensile cracking of the rock microstructure is dominant, and the rock has a high strain energy storage capacity and a low strain energy dissipation capacity. The cumulative acoustic emission (AE) count rate curve shows the same trend as the total dissipated strain energy $$U^{d}$$ curve. The research results show that the proposed strain energy analysis method for TTC can explain the macroscopic failure behaviors, microscopic failure mechanism and AE characteristics of Beishan granite under TTC, thereby providing new ideas and methods for investigating the behaviors of deep underground hard rock.
-
modeling of an excavation induced rock Fracturing Process from continuity to discontinuity
Engineering Analysis With Boundary Elements, 2019Co-Authors: Xiating Feng, Zhenhua WuAbstract:Abstract A convenient and efficient approach to simulate the excavation-induced rock Fracturing Process from continuity to discontinuity is proposed. The excavation boundary is considered to be an internal discontinuity, which is enriched by a Heaviside function. By using partition of unity concept and level set method, the cellular automaton updating rule for elasto-plastic Fracturing induced by excavation is developed. The excavation induced rock mass strain localization is described by plasticity. A plastic strain-dependent criterion is proposed to link continuity and discontinuity, by which crack initiation and propagation of rock mass are described. By using the cellular automaton neighborhood information and level set method, the crack propagation path and excavation boundary can be tracked efficiently. The excavation and the induced crack initiation and propagation can be represented and simulated without explicit remeshing. This approach is implemented in a self-developed numerical model, i.e. a rock continuous-discontinuous cellular automaton (CDCA). The reliability and versatility of CDCA in the modeling of excavation-induced crack initiation, propagation, coalescence and block formation in rock mass are well demonstrated. The method helps estimate the onset of stable and unstable failure development, as well as the magnitude of plastic strain before cracking in rocks.
-
isrm suggested method for in situ acoustic emission monitoring of the Fracturing Process in rock masses
Rock Mechanics and Rock Engineering, 2019Co-Authors: Xiating Feng, R P Young, J M Reyesmontes, Omer Aydan, Tsuyoshi IshidaAbstract:The purpose of this ISRM Suggested Method is to describe a methodology for in situ acoustic emission monitoring of the rock mass Fracturing Processes occurring as a result of excavations for tunnels, large caverns in the fields of civil, rock slopes and mining engineering, etc. In this Suggested Method, the equipment that is required for an acoustic emission monitoring system is described; the procedures are outlined and illustrated, together with the methods for data acquisition and Processing for improving the monitoring results. There is an explanation of the methods for presenting and interpreting the results, and recommendations are supported by several examples.
-
isrm suggested method for in situ microseismic monitoring of the Fracturing Process in rock masses
Rock Mechanics and Rock Engineering, 2016Co-Authors: Yaxun Xiao, Xiating Feng, J A Hudson, Bingrui Chen, Guangliang FengAbstract:The purpose of this ISRM Suggested Method is to describe a methodology for in situ microseismic monitoring of the rock mass Fracturing Processes occurring as a result of excavations for rock slopes, tunnels, or large caverns in the fields of civil, hydraulic, or mining engineering. In this Suggested Method, the equipment that is required for a microseismic monitoring system is described; the procedures are outlined and illustrated, together with the methods for data acquisition and Processing for improving the monitoring results. There is an explanation of the methods for presenting and interpreting the results, and recommendations are supported by several examples.
Joseph Sang-il Kwon - One of the best experts on this subject based on the ideXlab platform.
-
Temporal clustering for order reduction of nonlinear parabolic PDE systems with time-dependent spatial domains: Application to a hydraulic Fracturing Process
AIChE Journal, 2017Co-Authors: Abhinav Narasingam, Prashanth Siddhamshetty, Joseph Sang-il KwonAbstract:© 2017 American Institute of Chemical Engineers.A temporally-local model order-reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time-dependent spatial domains is presented. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, low-dimensional models are derived by constructing appropriate temporally-local eigenfunctions. Within this context, first of all, the time domain is partitioned into multiple clusters (i.e., subdomains) by using the framework known as global optimum search. This approach, a variant of Generalized Benders Decomposition, formulates clustering as a Mixed-Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed-Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low-dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order-reduction technique is applied to a hydraulic Fracturing Process described by a nonlinear parabolic PDE system with the time-dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order-reduction technique with temporally-global eigenfunctions.
