We study CO_(2) injection into a saline aquifer intersected by a tectonic fault using a coupled modeling approach to evaluate potential geomechanical risks.The simulation approach integrates the reservoir and mechanic...We study CO_(2) injection into a saline aquifer intersected by a tectonic fault using a coupled modeling approach to evaluate potential geomechanical risks.The simulation approach integrates the reservoir and mechanical simulators through a data transfer algorithm.MUFITS simulates non-isothermal multiphase flow in the reservoir,while FLAC3D calculates its mechanical equilibrium state.We accurately describe the tectonic fault,which consists of damage and core zones,and derive novel analytical closure relations governing the permeability alteration in the fault zone.We estimate the permeability of the activated fracture network in the damage zone and calculate the permeability of the main crack in the fault core,which opens on asperities due to slip.The coupled model is applied to simulate CO_(2) injection into synthetic and realistic reservoirs.In the synthetic reservoir model,we examine the impact of formation depth and initial tectonic stresses on geomechanical risks.Pronounced tectonic stresses lead to inelastic deformations in the fault zone.Regardless of the magnitude of tectonic stress,slip along the fault plane occurs,and the main crack in the fault core opens on asperities,causing CO_(2) leakage out of the storage aquifer.In the realistic reservoir model,we demonstrate that sufficiently high bottomhole pressure induces plastic deformations in the near-wellbore zone,interpreted as rock fracturing,without slippage along the fault plane.We perform a sensitivity analysis of the coupled model,varying the mechanical and flow properties of the storage layers and fault zone to assess fault stability and associated geomechanical risks.展开更多
We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability ...We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability map,incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well.This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data.We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability.The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations,the integral permeability of the reservoir domain around each well,and skin factors.This inverse optimization problem is addressed by repeatedly solving forward problems,where an artificial neural network(ANN)predicts the integral permeability of the formation surrounding a well and skin factor.The ANN is trained on a physics-based dataset generated through a synthetic well test procedure,which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model.The proposed method is tested on the“Egg Model”,a synthetic reservoir with significant heterogeneity due to highly permeable channels.The permeability map created by our approach demonstrates hydrodynamic similarity to the original map.Numerical reservoir simulations,corresponding to the constructed and original permeability maps,yield comparable pore pressure and water saturation distributions at the end of the simulation period.Additionally,we observe a notable match in flow rates and total volumes of produced oil,water,and injected water between simulations.The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes.This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning.Unlike traditional variogrambased geostatistical simulation algorithms,our method provides a permeability distribution that is hydrodynamically similar to the actual one,enhancing initial guess in the history matching process.The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods,offering an innovative approach that can benefit the petroleum industry.We also provide recommendations for further development of the proposed algorithm to account for geological realism.展开更多
Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challe...Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challenging,as it requires the integration of various data sources by experts from different disciplines.Moreover,there are no sources to provide direct information about the inter-well space.In this work,a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area.This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources:well logs,well tests,and seismics.A convolutional neural network is developed to process seismic data and then incorporate it with other sources.A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct an adequate permeability map.The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia.The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells,and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data.展开更多
文摘We study CO_(2) injection into a saline aquifer intersected by a tectonic fault using a coupled modeling approach to evaluate potential geomechanical risks.The simulation approach integrates the reservoir and mechanical simulators through a data transfer algorithm.MUFITS simulates non-isothermal multiphase flow in the reservoir,while FLAC3D calculates its mechanical equilibrium state.We accurately describe the tectonic fault,which consists of damage and core zones,and derive novel analytical closure relations governing the permeability alteration in the fault zone.We estimate the permeability of the activated fracture network in the damage zone and calculate the permeability of the main crack in the fault core,which opens on asperities due to slip.The coupled model is applied to simulate CO_(2) injection into synthetic and realistic reservoirs.In the synthetic reservoir model,we examine the impact of formation depth and initial tectonic stresses on geomechanical risks.Pronounced tectonic stresses lead to inelastic deformations in the fault zone.Regardless of the magnitude of tectonic stress,slip along the fault plane occurs,and the main crack in the fault core opens on asperities,causing CO_(2) leakage out of the storage aquifer.In the realistic reservoir model,we demonstrate that sufficiently high bottomhole pressure induces plastic deformations in the near-wellbore zone,interpreted as rock fracturing,without slippage along the fault plane.We perform a sensitivity analysis of the coupled model,varying the mechanical and flow properties of the storage layers and fault zone to assess fault stability and associated geomechanical risks.
基金supported by the Analytical center under the RF Government (subsidy agreement 000000D730321P5Q0002,Grant No.70-2021-00145 02.11.2021)
文摘We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs,consistent with well logging and well test measurements.The primary objective is to create a permeability map,incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well.This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data.We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability.The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations,the integral permeability of the reservoir domain around each well,and skin factors.This inverse optimization problem is addressed by repeatedly solving forward problems,where an artificial neural network(ANN)predicts the integral permeability of the formation surrounding a well and skin factor.The ANN is trained on a physics-based dataset generated through a synthetic well test procedure,which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model.The proposed method is tested on the“Egg Model”,a synthetic reservoir with significant heterogeneity due to highly permeable channels.The permeability map created by our approach demonstrates hydrodynamic similarity to the original map.Numerical reservoir simulations,corresponding to the constructed and original permeability maps,yield comparable pore pressure and water saturation distributions at the end of the simulation period.Additionally,we observe a notable match in flow rates and total volumes of produced oil,water,and injected water between simulations.The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes.This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning.Unlike traditional variogrambased geostatistical simulation algorithms,our method provides a permeability distribution that is hydrodynamically similar to the actual one,enhancing initial guess in the history matching process.The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods,offering an innovative approach that can benefit the petroleum industry.We also provide recommendations for further development of the proposed algorithm to account for geological realism.
基金supported by the grant for research centers in the fi of AI provided by the Ministry of Economic Development of the Russian Federation in accordance with the agreement 000000C313925P4F0002the agreement with Skoltech N◦139-10-2025-033.
文摘Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and,therefore,designing effective recovery strategies.This problem,however,remains challenging,as it requires the integration of various data sources by experts from different disciplines.Moreover,there are no sources to provide direct information about the inter-well space.In this work,a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area.This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources:well logs,well tests,and seismics.A convolutional neural network is developed to process seismic data and then incorporate it with other sources.A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct an adequate permeability map.The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia.The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells,and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data.
