To evaluate the fracturing effect and dynamic change process after volume fracturing with vertical wells in low permeability oil reservoirs, an oil-water two-phase flow model and a well model are built. On this basis,...To evaluate the fracturing effect and dynamic change process after volume fracturing with vertical wells in low permeability oil reservoirs, an oil-water two-phase flow model and a well model are built. On this basis, an evaluation method of fracturing effect based on production data and fracturing fluid backflow data is established, and the method is used to analyze some field cases. The vicinity area of main fracture after fracturing is divided into different stimulated regions. The permeability and area of different regions are used to characterize the stimulation strength and scale of the fracture network. The conductivity of stimulated region is defined as the product of the permeability and area of the stimulated region. Through parameter sensitivity analysis, it is found that half-length of the fracture and the permeability of the core area mainly affect the flow law near the well, that is, the early stage of production;while matrix permeability mainly affects the flow law at the far end of the fracture. Taking a typical old well in Changqing Oilfield as an example, the fracturing effect and its changes after two rounds of volume fracturing in this well are evaluated. It is found that with the increase of production time after the first volume fracturing, the permeability and conductivity of stimulated area gradually decreased, and the fracturing effect gradually decreased until disappeared;after the second volume fracturing, the permeability and conductivity of stimulated area increased significantly again.展开更多
Horizontal well drilling and multi-stage hydraulic fracturing are key technologies for the development of shale gas reservoirs.Instantaneous acquisition of hydraulic fracture parameters is crucial for evaluating fract...Horizontal well drilling and multi-stage hydraulic fracturing are key technologies for the development of shale gas reservoirs.Instantaneous acquisition of hydraulic fracture parameters is crucial for evaluating fracturing effectiveness,optimizing processes,and predicting gas productivity.This paper establishes a transient flow model for shale gas wells based on the boundary element method,achieving the characterization of stimulated reservoir volume for a single stage.By integrating pressure monitoring data following the pumping shut-in period of hydraulic fracturing for well testing interpretation,a workflow for inverting fracture parameters of shale gas wells is established.This new method eliminates the need for prolonged production testing and can interpret parameters of individual hydraulic fracture segments,offering significant advantages over the conventional pressure transient analysismethod.The practical application of thismethodology was conducted on 10 shale gaswellswithin the Changning shale gas block of Sichuan,China.The results show a high correlation between the interpreted single-stage total length and surface area of hydraulic fractures and the outcomes of gas production profile tests.Additionally,significant correlations are observed between these parameters and cluster number,horizontal stress difference,and natural fracture density.This demonstrates the effectiveness of the proposed fracture parameter inversion method and the feasibility of field application.The findings of this study aim to provide solutions and references for the inversion of fracture parameters in shale gas wells.展开更多
Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fra...Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.展开更多
Affected by reservoir heterogeneity,developed natural fractures,and bedding fractures,the fracturing pressure curves in fracturing of shale gas horizontal wells present complex shapes.A large amount of information con...Affected by reservoir heterogeneity,developed natural fractures,and bedding fractures,the fracturing pressure curves in fracturing of shale gas horizontal wells present complex shapes.A large amount of information contained in the fracturing curves is still not fully excavated.Based on the theory of shale gas fracture network fracturing,the calculation model of bottom hole net pressure is established by integrating the real-time data such as casing pressure,pump rate,and proppant concentration.Net pressure slope and net pressure index are constructed as key parameters,and the net pressure curve is divided dynamically to describe the mechanical conditions corresponding to the fracture propagation behavior during the fracturing process.Six fracture propagation modes were identified,including fracture network propagation,fracture propagation blockage,normal fracture propagation,fracture propagation long bedding,fracture height growth,and rapidfluidfiltration,and then the operation pressure curve diagnosis and identification method were formed for shale gas fracture network fracturing in horizontal wells.The shortcomings of conventional operation curve diagnosis and identification methods are abandoned and the fracture network complexity index is presented.The higher index indicates more time of fracture network propagation and fracture propagation along bedding and the better reservoir stimulation effect.The model is applied to shale gas wells in the southeastern margin of Sichuan Basin,and the average fracture network complexity index of a single well is 0.3,which is in good agreement with the microseismic monitoring results.This proves the good reliability of the method developed.The method is helpful to improve the potential and level of fracturing stimulation of shale reservoirs and is of great significance for improving the post-fracturing evaluation technology of fracture network and guiding the real-time dynamic adjustment offield fracturing operations.展开更多
基金Supported by the China National Science and Technology Major Project (2017ZX05013-001)CNPC Science and Technology Major Research Project (2018B-4907)
文摘To evaluate the fracturing effect and dynamic change process after volume fracturing with vertical wells in low permeability oil reservoirs, an oil-water two-phase flow model and a well model are built. On this basis, an evaluation method of fracturing effect based on production data and fracturing fluid backflow data is established, and the method is used to analyze some field cases. The vicinity area of main fracture after fracturing is divided into different stimulated regions. The permeability and area of different regions are used to characterize the stimulation strength and scale of the fracture network. The conductivity of stimulated region is defined as the product of the permeability and area of the stimulated region. Through parameter sensitivity analysis, it is found that half-length of the fracture and the permeability of the core area mainly affect the flow law near the well, that is, the early stage of production;while matrix permeability mainly affects the flow law at the far end of the fracture. Taking a typical old well in Changqing Oilfield as an example, the fracturing effect and its changes after two rounds of volume fracturing in this well are evaluated. It is found that with the increase of production time after the first volume fracturing, the permeability and conductivity of stimulated area gradually decreased, and the fracturing effect gradually decreased until disappeared;after the second volume fracturing, the permeability and conductivity of stimulated area increased significantly again.
基金funded by the Science and Technology Cooperation Project of the CNPC-SWPU Innovation Alliance,grant numbers“2020CX020202,2020CX030202 and 2020CX010403”.
文摘Horizontal well drilling and multi-stage hydraulic fracturing are key technologies for the development of shale gas reservoirs.Instantaneous acquisition of hydraulic fracture parameters is crucial for evaluating fracturing effectiveness,optimizing processes,and predicting gas productivity.This paper establishes a transient flow model for shale gas wells based on the boundary element method,achieving the characterization of stimulated reservoir volume for a single stage.By integrating pressure monitoring data following the pumping shut-in period of hydraulic fracturing for well testing interpretation,a workflow for inverting fracture parameters of shale gas wells is established.This new method eliminates the need for prolonged production testing and can interpret parameters of individual hydraulic fracture segments,offering significant advantages over the conventional pressure transient analysismethod.The practical application of thismethodology was conducted on 10 shale gaswellswithin the Changning shale gas block of Sichuan,China.The results show a high correlation between the interpreted single-stage total length and surface area of hydraulic fractures and the outcomes of gas production profile tests.Additionally,significant correlations are observed between these parameters and cluster number,horizontal stress difference,and natural fracture density.This demonstrates the effectiveness of the proposed fracture parameter inversion method and the feasibility of field application.The findings of this study aim to provide solutions and references for the inversion of fracture parameters in shale gas wells.
基金supported by the National Natural Science Foundation of China(Grant No.52174044,52004302)Science Foundation of China University of Petroleum,Beijing(No.ZX20200134,2462021YXZZ012)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX 2020-01-07).
文摘Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.
基金National Natural Science Foundation of China Basic The ory of Efficient Development of Shale Oil and Gas(No.51490653)Theory and Method of Efficient Construction of Fracture Network in Deep and Ultra-Deep Shale Gas Horizontal Wells(No.U19A2043)Theory and Method of Long-term Propping for Deep Shale Gas Hydraulic Fractures based on DEM-LBM Hydro-Mechanical Coupling(No.52104039).
文摘Affected by reservoir heterogeneity,developed natural fractures,and bedding fractures,the fracturing pressure curves in fracturing of shale gas horizontal wells present complex shapes.A large amount of information contained in the fracturing curves is still not fully excavated.Based on the theory of shale gas fracture network fracturing,the calculation model of bottom hole net pressure is established by integrating the real-time data such as casing pressure,pump rate,and proppant concentration.Net pressure slope and net pressure index are constructed as key parameters,and the net pressure curve is divided dynamically to describe the mechanical conditions corresponding to the fracture propagation behavior during the fracturing process.Six fracture propagation modes were identified,including fracture network propagation,fracture propagation blockage,normal fracture propagation,fracture propagation long bedding,fracture height growth,and rapidfluidfiltration,and then the operation pressure curve diagnosis and identification method were formed for shale gas fracture network fracturing in horizontal wells.The shortcomings of conventional operation curve diagnosis and identification methods are abandoned and the fracture network complexity index is presented.The higher index indicates more time of fracture network propagation and fracture propagation along bedding and the better reservoir stimulation effect.The model is applied to shale gas wells in the southeastern margin of Sichuan Basin,and the average fracture network complexity index of a single well is 0.3,which is in good agreement with the microseismic monitoring results.This proves the good reliability of the method developed.The method is helpful to improve the potential and level of fracturing stimulation of shale reservoirs and is of great significance for improving the post-fracturing evaluation technology of fracture network and guiding the real-time dynamic adjustment offield fracturing operations.
