Pub Date : 2020-09-14DOI: 10.3997/2214-4609.202035123
A. Yewgat, D. Busby, M. Chevalier, C. Lapeyre, O. Teste
Summary Classical reservoir engineering studies require building geological models and solving complex fluid flow transport equations that require high-quality data, numerous computational resources, time and workflows. For large and mature fields data-driven models can be used to get faster answer and to perform production analysis more efficiently. Capacitance Resistive Models (CRM) are a class of methods based on material balance that can be used to estimate production wells liquid rates as a function of injected water and Bottom Hole Pressure (BHP) variations. CRM methods quantify the connectivity between producers and injectors using only dynamic data. An important drawback of CRM is that they can suffer from parameter identification problems. Moreover, the analytical solution can be only obtained in specific conditions: linear variations of BHP and fixed injection rate between two consecutive time steps. In this work we present a new approach combining CRM material balance equations with neural networks in order to obtain more robust and reliable estimation of the CRM parameters (i.e. well connectivity, productivity indices and time constants). This proposal is also interesting since it is not based on any assumption on BHP and injection rates. To this end, we use a recent approach called Physics Informed Neural Networks (PINNs). In this approach neural networks are trained on observed data with additional physics constraints traduced in appropriate loss functions. The parameters of this physical equation are evaluated at the same time as the neural network weights. The introduction of PINNs in our approach raised after testing classical machine learning (ML) models (SVMs, Random Forests …) and deep learning models (MLP, LSTM, RNNs…). Indeed, such models can perform well in some specific cases but usually struggle to produce robust results (i.e. forecasting) in the long term. Unfortunately, such systems do not natively integrate physics constraints. Our aim is to impose physic constraints in neural networks. Thus, we may obtain more stable and reliable results. On the same time, we should be able to account for more behaviors that are not explained by simplified physic equations such as material balance. We performed a full comparison between our approach using PINNs, other standard ML and DL approaches and a given framework of CRMs on two data-sets: a simple but realistic model build using a commercial reservoir simulator, and a real data set. We show that our approach gives more robust results (in terms of MSE) while not suffering from parameter identification issue.
{"title":"Deep-CRM: A New Deep Learning Approach for Capacitance Resistive Models","authors":"A. Yewgat, D. Busby, M. Chevalier, C. Lapeyre, O. Teste","doi":"10.3997/2214-4609.202035123","DOIUrl":"https://doi.org/10.3997/2214-4609.202035123","url":null,"abstract":"Summary Classical reservoir engineering studies require building geological models and solving complex fluid flow transport equations that require high-quality data, numerous computational resources, time and workflows. For large and mature fields data-driven models can be used to get faster answer and to perform production analysis more efficiently. Capacitance Resistive Models (CRM) are a class of methods based on material balance that can be used to estimate production wells liquid rates as a function of injected water and Bottom Hole Pressure (BHP) variations. CRM methods quantify the connectivity between producers and injectors using only dynamic data. An important drawback of CRM is that they can suffer from parameter identification problems. Moreover, the analytical solution can be only obtained in specific conditions: linear variations of BHP and fixed injection rate between two consecutive time steps. In this work we present a new approach combining CRM material balance equations with neural networks in order to obtain more robust and reliable estimation of the CRM parameters (i.e. well connectivity, productivity indices and time constants). This proposal is also interesting since it is not based on any assumption on BHP and injection rates. To this end, we use a recent approach called Physics Informed Neural Networks (PINNs). In this approach neural networks are trained on observed data with additional physics constraints traduced in appropriate loss functions. The parameters of this physical equation are evaluated at the same time as the neural network weights. The introduction of PINNs in our approach raised after testing classical machine learning (ML) models (SVMs, Random Forests …) and deep learning models (MLP, LSTM, RNNs…). Indeed, such models can perform well in some specific cases but usually struggle to produce robust results (i.e. forecasting) in the long term. Unfortunately, such systems do not natively integrate physics constraints. Our aim is to impose physic constraints in neural networks. Thus, we may obtain more stable and reliable results. On the same time, we should be able to account for more behaviors that are not explained by simplified physic equations such as material balance. We performed a full comparison between our approach using PINNs, other standard ML and DL approaches and a given framework of CRMs on two data-sets: a simple but realistic model build using a commercial reservoir simulator, and a real data set. We show that our approach gives more robust results (in terms of MSE) while not suffering from parameter identification issue.","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"385 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114899221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.3997/2214-4609.202035215
Z. Han, G. Ren, R. Younis
Summary Seismic deformation in poroelastic media may be triggered by a variety of physical events including stick-slip frictional instabilities in fracture. While in the context of simulation-aided engineering to mitigate the risks of induced-seismicity, it is sufficient to be able to resolve the onset of seismic slip using quasi-static assumptions, applications involving microseismicity require inertial models throughout the intended operational activity. In this work, we develop a fully-dynamic (inertial), time-adaptive, and coupled numerical model incorporating transient poromechanics and multiphase flow in fractured reservoirs. The model is applied to simultaneously assimilate well-performance and dynamic seismic event sequences, thereby informing about the causal event dynamics. First, we extend the mixed XFEM-EDFM numerical scheme to time-dependent mechanics. A stable and second-order implicit Newark method is developed in time. The pressure-dependent contact forces in fracture are treated using Lagrange multiplier constraints, and a Polynomial Projection Method is developed to stabilize the computation of contact traction. A temporal adaptivity indicators is developed to resolve preseismic triggering and coseismic spontaneous rupture. The model is validated empirically (for accuracy, consistency, and computational efficiency). Numerical examples are presented to benchmark the proposed dynamic model relative to predictions from a quasi-static approach. In particular, it is demonstrated that computed waveforms can differ to first-order. Furthermore, in simulation test cases with water injection, coseismic rupture and microseismic signals are detected and in-situ stress migration is observed. We outline implications towards unifying toolchains and workflows for combined geophysical, well completions design, and reservoir performance analysis.
{"title":"Coupled Forward Simulation of Seismicity: a Stick-Slip Model for Fractures and Transient Geomechanics","authors":"Z. Han, G. Ren, R. Younis","doi":"10.3997/2214-4609.202035215","DOIUrl":"https://doi.org/10.3997/2214-4609.202035215","url":null,"abstract":"Summary Seismic deformation in poroelastic media may be triggered by a variety of physical events including stick-slip frictional instabilities in fracture. While in the context of simulation-aided engineering to mitigate the risks of induced-seismicity, it is sufficient to be able to resolve the onset of seismic slip using quasi-static assumptions, applications involving microseismicity require inertial models throughout the intended operational activity. In this work, we develop a fully-dynamic (inertial), time-adaptive, and coupled numerical model incorporating transient poromechanics and multiphase flow in fractured reservoirs. The model is applied to simultaneously assimilate well-performance and dynamic seismic event sequences, thereby informing about the causal event dynamics. First, we extend the mixed XFEM-EDFM numerical scheme to time-dependent mechanics. A stable and second-order implicit Newark method is developed in time. The pressure-dependent contact forces in fracture are treated using Lagrange multiplier constraints, and a Polynomial Projection Method is developed to stabilize the computation of contact traction. A temporal adaptivity indicators is developed to resolve preseismic triggering and coseismic spontaneous rupture. The model is validated empirically (for accuracy, consistency, and computational efficiency). Numerical examples are presented to benchmark the proposed dynamic model relative to predictions from a quasi-static approach. In particular, it is demonstrated that computed waveforms can differ to first-order. Furthermore, in simulation test cases with water injection, coseismic rupture and microseismic signals are detected and in-situ stress migration is observed. We outline implications towards unifying toolchains and workflows for combined geophysical, well completions design, and reservoir performance analysis.","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124482855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.3997/2214-4609.202035101
X. Lyu, D. Voskov, W. Rossen
Summary Geological storage of CO2 is a crucial emerging technology to reduce anthropogenic greenhouse gas emissions. Due to the buoyant characteristic of injected gas and the complex geology of subsurface reservoirs, most injected CO2 either rapidly migrates to the top of the reservoir or fingers through high-permeability layers due to instability in the convection-dominated displacement. Both of these phenomena reduce the storage capacity of subsurface media. CO2-foam injection is a promising technology for reducing gas mobility and increasing trapping within the swept region in deep brine aquifers. A consistent thermodynamic model based on a combination of a classic cubic equation of state (EOS) for gas components with an activity model for the aqueous phase has been implemented to describe the phase behavior of the CO2-brine system with impurities. This phase-behavior module is combined with representation of foam by an implicit-texture (IT) model with two flow regimes. This combination can accurately capture the complicated dynamics of miscible CO2 foam at various stages of the sequestration process. The Operator-Based Linearization (OBL) approach is applied to reduce the nonlinearity of the CO2-foam problem by transforming the discretized conservation equations into space-dependent and state-dependent operators. Surfactant-alternating-gas (SAG) injection is applied to overcome injectivity problems related to pressure build-up in the near-well region. In this study, a 3D large-scale heterogeneous reservoir is used to examine CO2-foam behaviour and its effects on CO2 storage. Simulation studies show foams can reduce gas mobility effectively by trapping gas bubbles and inhibit CO2 from migrating upward in the presence of gravity, which in turn improves remarkably the sweep efficiency and opens the unswept region for CO2 storage. We also study how surfactant injection and forming of foam affect enhanced dissolution of CO2 at various thermodynamic conditions. This work provides a possible strategy to develop robust and efficient CO2 storage technology.
{"title":"Simulation of Foam-Assisted CO2 Storage in Saline Aquifers","authors":"X. Lyu, D. Voskov, W. Rossen","doi":"10.3997/2214-4609.202035101","DOIUrl":"https://doi.org/10.3997/2214-4609.202035101","url":null,"abstract":"Summary Geological storage of CO2 is a crucial emerging technology to reduce anthropogenic greenhouse gas emissions. Due to the buoyant characteristic of injected gas and the complex geology of subsurface reservoirs, most injected CO2 either rapidly migrates to the top of the reservoir or fingers through high-permeability layers due to instability in the convection-dominated displacement. Both of these phenomena reduce the storage capacity of subsurface media. CO2-foam injection is a promising technology for reducing gas mobility and increasing trapping within the swept region in deep brine aquifers. A consistent thermodynamic model based on a combination of a classic cubic equation of state (EOS) for gas components with an activity model for the aqueous phase has been implemented to describe the phase behavior of the CO2-brine system with impurities. This phase-behavior module is combined with representation of foam by an implicit-texture (IT) model with two flow regimes. This combination can accurately capture the complicated dynamics of miscible CO2 foam at various stages of the sequestration process. The Operator-Based Linearization (OBL) approach is applied to reduce the nonlinearity of the CO2-foam problem by transforming the discretized conservation equations into space-dependent and state-dependent operators. Surfactant-alternating-gas (SAG) injection is applied to overcome injectivity problems related to pressure build-up in the near-well region. In this study, a 3D large-scale heterogeneous reservoir is used to examine CO2-foam behaviour and its effects on CO2 storage. Simulation studies show foams can reduce gas mobility effectively by trapping gas bubbles and inhibit CO2 from migrating upward in the presence of gravity, which in turn improves remarkably the sweep efficiency and opens the unswept region for CO2 storage. We also study how surfactant injection and forming of foam affect enhanced dissolution of CO2 at various thermodynamic conditions. This work provides a possible strategy to develop robust and efficient CO2 storage technology.","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121245263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary This work is devoted to an analysis of the near-tip region of a hydraulic fracture driven by slickwater in a permeable saturated rock. We consider a steady-state problem of a semi-infinite fracture propagating with constant velocity. The host rock is elastic and homogeneous, and fracture propagates according to linear elastic fracture mechanics. The fluid exchange between the fracture and reservoir is governed by Carter’s law. The distinguishing feature of the model is an account for the transition of the flow regime inside the crack channel from laminar to turbulent moving away from the fracture front. The main objective is to analyse the influence of the leak-off process on the laminar-to-turbulent transition and, thus, potential prominence of turbulent flow effects. Hydraulic fracturing fluid is water with polymeric additives (slickwater). These additives reduce viscous friction resulting in the decrease of energy consumption required for pumping. Compared to water, the slickwater exhibits significantly delayed transition to the turbulent regime described by the maximum drag reduction asymptote ( Virk 1975 ). The system of governing equations, which consists of elasticity equation, propagation condition, the continuity equation for viscous incompressible Newtonian fluid, and Poiseuille’s law modified for the turbulent flow regime, is solved for the fracture aperture and fluid pressure along the fracture as a function of problem parameters. We find out that the leak-off process enhances the turbulent flow effects by shifting the transition between laminar and turbulent flow regimes closer to the fracture front, as compared to the zero-leak-off case ( Lecampion & Zia, 2019 ), resulting in a broader region of the fracture hosting turbulent flow. Consequently, in the permeable reservoir case, the transition to turbulent flow can be realised at a distance from the front smaller than the typical field hydraulic fracture size (10 – 100 meters). We compare the fracture width profiles with the impermeable rock case and reveal that the fracture volume increases when leak-off occurs. We analyse the problem parametric space where five limiting regimes are identified: toughness, laminar-viscosity and -leak-off, turbulent-viscosity and -leak-off. We derive analytical expressions for the fracture width and pressure profiles in the turbulent-leak-off regime while others have been established previously. By comparing the limiting solutions with the general numerical solution, we can define their applicability domains and corresponding solution regime maps. The toughness and turbulent-viscosity regimes approximate the general solution in the near- and far-fields, while the other three limiting cases can emerge in the intermediate field.
{"title":"Turbulent flow effects in a slickwater fracture propagation in permeable rock","authors":"E. Kanin, D. Garagash, A. Osiptsov","doi":"10.31223/osf.io/bq2t6","DOIUrl":"https://doi.org/10.31223/osf.io/bq2t6","url":null,"abstract":"Summary This work is devoted to an analysis of the near-tip region of a hydraulic fracture driven by slickwater in a permeable saturated rock. We consider a steady-state problem of a semi-infinite fracture propagating with constant velocity. The host rock is elastic and homogeneous, and fracture propagates according to linear elastic fracture mechanics. The fluid exchange between the fracture and reservoir is governed by Carter’s law. The distinguishing feature of the model is an account for the transition of the flow regime inside the crack channel from laminar to turbulent moving away from the fracture front. The main objective is to analyse the influence of the leak-off process on the laminar-to-turbulent transition and, thus, potential prominence of turbulent flow effects. Hydraulic fracturing fluid is water with polymeric additives (slickwater). These additives reduce viscous friction resulting in the decrease of energy consumption required for pumping. Compared to water, the slickwater exhibits significantly delayed transition to the turbulent regime described by the maximum drag reduction asymptote ( Virk 1975 ). The system of governing equations, which consists of elasticity equation, propagation condition, the continuity equation for viscous incompressible Newtonian fluid, and Poiseuille’s law modified for the turbulent flow regime, is solved for the fracture aperture and fluid pressure along the fracture as a function of problem parameters. We find out that the leak-off process enhances the turbulent flow effects by shifting the transition between laminar and turbulent flow regimes closer to the fracture front, as compared to the zero-leak-off case ( Lecampion & Zia, 2019 ), resulting in a broader region of the fracture hosting turbulent flow. Consequently, in the permeable reservoir case, the transition to turbulent flow can be realised at a distance from the front smaller than the typical field hydraulic fracture size (10 – 100 meters). We compare the fracture width profiles with the impermeable rock case and reveal that the fracture volume increases when leak-off occurs. We analyse the problem parametric space where five limiting regimes are identified: toughness, laminar-viscosity and -leak-off, turbulent-viscosity and -leak-off. We derive analytical expressions for the fracture width and pressure profiles in the turbulent-leak-off regime while others have been established previously. By comparing the limiting solutions with the general numerical solution, we can define their applicability domains and corresponding solution regime maps. The toughness and turbulent-viscosity regimes approximate the general solution in the near- and far-fields, while the other three limiting cases can emerge in the intermediate field.","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115840399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3997/2214-4609.202035059
T. H. Sandve, O. S. vareid, I. Aavatsmark
{"title":"Improved Extended Blackoil Formulation for CO2EOR Simulations","authors":"T. H. Sandve, O. S. vareid, I. Aavatsmark","doi":"10.3997/2214-4609.202035059","DOIUrl":"https://doi.org/10.3997/2214-4609.202035059","url":null,"abstract":"","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134644298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.3997/2214-4609.202035170
A. Voskresenskiy, N. Bukhanov, Z. Filippova, R. Brandão, V. Segura, E. V. Brazil
{"title":"Feature Selection for Reservoir Analogues Similarity Ranking As Model-Based Causal Inference","authors":"A. Voskresenskiy, N. Bukhanov, Z. Filippova, R. Brandão, V. Segura, E. V. Brazil","doi":"10.3997/2214-4609.202035170","DOIUrl":"https://doi.org/10.3997/2214-4609.202035170","url":null,"abstract":"","PeriodicalId":440594,"journal":{"name":"ECMOR XVII","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130586837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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