Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wave...Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.展开更多
Fractures are of great significance to tight oil and gas development.Fracture identification using conventional well logs is a feasible way to locate the underground fractures in tight sandstones.However,there are thr...Fractures are of great significance to tight oil and gas development.Fracture identification using conventional well logs is a feasible way to locate the underground fractures in tight sandstones.However,there are three problems affecting its interpretation accuracy and practical application,namely weak well log responses of fractures,a lack of specific logs for fracture prediction,and relative change omission in log responses.To overcome these problems and improve fracture identification accuracy,a fracture indicating parameter(FIP)method composed of a comprehensive index method(CIM)and a comprehensive fractal method(CFM)is introduced.The CIM tries to handle the first problem by amplifying log responses of fractures.The CFM addresses the third one using fractal dimensions.The flexible weight parameters corresponding to logs in the CIM and CFM make the interpretation possible for wells lacking specific logs.The reconstructed logs in the CIM and CFM try to solve the second problem.It is noted that the FIP method can calculate the probability of fracture development at a certain depth,but cannot show the fracture development degree of a new well compared with other wells.In this study,a formation fracture intensity(FFI)method is also introduced to further evaluate fracture development combined with production data.To test the validity of the FIP and FFI methods,fracture identification experiments are implemented in a tight reservoir in the Ordos Basin.The results are consistent with the data of rock core observation and production,indicating the proposed methods are effective for fracture identification and evaluation.展开更多
The identification of fractures is of great importance in gravity and magnetic data processing and interpretation.In this study,four fracture identification methods are applied,and widely used in processing and analys...The identification of fractures is of great importance in gravity and magnetic data processing and interpretation.In this study,four fracture identification methods are applied,and widely used in processing and analysis of the gravity anomaly,including vertical second derivative method,tilt derivative method,theta map method and normalized differential method,for gravity data acquired in a given area in Heilongjiang.By comparing the distribution of the zero contour or maximum contour,we summarize the application effects,and both advantages and disadvantages of each method.It is found that tilt derivative method and normalized differential method provide better effects than other two methods:the narrower anomaly gradient belt and higher identification precision of fracture or geological boundary.The inferred fractures and geological boundaries have a great match with the results obtained from geologic map and remote sensing data interpretation.Those study results have definitely provided the theoretical foundation for identifying faults and the geological boundaries.展开更多
The gravity gradient anomaly zone is produced due to density diff erences on both sides of a fault.Tracking of extreme points enables the characterization and description of fault locations.However,for some deep-seate...The gravity gradient anomaly zone is produced due to density diff erences on both sides of a fault.Tracking of extreme points enables the characterization and description of fault locations.However,for some deep-seated faults with large burial depths and secondary faults with moderate burial depths,the gravity horizontal total gradient anomaly must be enhanced using the concept of dip angle to strengthen the weak anomaly extraction for the identification of more fault information.This method was used to predict five regional deep-seated faults and six secondary faults in the Miquan region.The fracture plane extends in a near north-northeast direction;that is,it mostly expands out of the study area,spreads out in a trumpet shape to the southwest,and converges to the northeast.Fracture activity is an important factor in controlling structural units or local structures.The Miquan block is located in a complex structural zone in front of the Bogeda Mountains,which have very complex surface and subsurface geological conditions,and seismic data are unideal.Therefore,fracture prediction results using gravity data are important in-depth understanding of the structure in this area.展开更多
Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells...Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain r...The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain reference orientation deviation（GROD） distribution perpendicular to the fracture surface were obtained by EBSD observation, and the characteristics of each fracture mode were identified. The GROD value of the specimen fractured in tension decreases to a constant related to the elongation of corresponding specimen in the far field（farther than 5 mm away from the fracture surface）. The peak exhibits in GROD curves of two smooth specimens and a notched specimen near the fracture surface（within 5 mm away from the fracture surface）, and the formation mechanisms were discussed in detail based on the influences of specimen geometries（smooth or notched） and material toughness. The GROD value of fatigue fractured specimen is close to that at undeformed condition in the whole field, except the small area near the crack path. The loading conditions（constant stress amplitude loading or constant stress intensity factor range K loading） and the EBSD striation formation during fatigue crack propagation were also studied by EBSD observation parallel to the crack path.展开更多
An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,mo...An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.展开更多
Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distributi...Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distribution rule in the rock microcrack zone and proposed an AE-energy-based method for identifying the real fracture.(1)A set of fracture experiments were performed on granite using wedgeloading,and the fracture process was detected and recorded by AE.The microcrack zone associated with the energy dissipation was characterized by AE sources and energy distribution,utilizing our selfdeveloped AE analysis program(RockAE).(2)The accumulated AE energy,an index representing energy dissipation,across the AE-depicted microcrack zone followed the normal distribution model(the mean and variance relate to the real fracture path and the microcrack zone width).This result implies that the nucleation and coalescence of massive cracks(i.e.,real fracture generation process)are supposed to follow a normal distribution.(3)Then,we obtained the real fracture extension path by joining the peak positions of the AE energy normal distribution curve at different cross-sections of the microcrack zone.Consequently,we distinguished between the microcrack zone and the concealed real fracture within it.The deviation was validated as slight as 1–3 mm.展开更多
Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fibe...Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.展开更多
This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,ro...This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,robust displacement and torque measurement facilities for rotary-core drilling are discussed.The conventional cable encoder for displacement measurement is replaced with a magnetostrictive displacement sensor,which is more reliable in harsh field drilling environments.This enables the measurement of the bit position with an accuracy of<1 mm.Most importantly,this new instrument is proven to be successful in improving the detection of structural discontinuities with thicknesses>1 mm.In addition,by measuring the electric current of the driving motor,the torque applied to the bit is conveniently and accurately converted.These innovations ensure high-quality data collection for DPM practices.Second,two indices derived from DPM are proposed to quantitatively describe rock mass quality.The specific energy index(SEI)and specific penetration index(SPI)are based on the principles of energy conservation and Mohr-Coulomb failure criterion,respectively.Extensive field tests conducted in a dam grouting area confirm a linear relationship between the thrust force and penetration per rotation,and between the torque and penetration per rotation.The correlation ratios of the related regressions are typically>0.9.These two indices allow for the quantitative interpretation of DPM data into rock mechanics characteristics,such as uniaxial compressive strength,rock quality designation(RQD),and rock mass permeability,eliminating the need for subjective judgment normally involved in the currently used rock mass quality rating approaches.展开更多
基金sponsored by National Science and Technology Major Project of China (No. 2008 ZX 05009-001)
文摘Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.
基金supported by the National Science and Technology Major Project(Grant No.2017ZX05009001-002 and 2017ZX05013002-004)the Fundamental Research Funds for the Central Universities(Grant No.2462020YJRC005)Science Foundation of China University of Petroleum,Beijing(Grant No.2462020XKJS02).
文摘Fractures are of great significance to tight oil and gas development.Fracture identification using conventional well logs is a feasible way to locate the underground fractures in tight sandstones.However,there are three problems affecting its interpretation accuracy and practical application,namely weak well log responses of fractures,a lack of specific logs for fracture prediction,and relative change omission in log responses.To overcome these problems and improve fracture identification accuracy,a fracture indicating parameter(FIP)method composed of a comprehensive index method(CIM)and a comprehensive fractal method(CFM)is introduced.The CIM tries to handle the first problem by amplifying log responses of fractures.The CFM addresses the third one using fractal dimensions.The flexible weight parameters corresponding to logs in the CIM and CFM make the interpretation possible for wells lacking specific logs.The reconstructed logs in the CIM and CFM try to solve the second problem.It is noted that the FIP method can calculate the probability of fracture development at a certain depth,but cannot show the fracture development degree of a new well compared with other wells.In this study,a formation fracture intensity(FFI)method is also introduced to further evaluate fracture development combined with production data.To test the validity of the FIP and FFI methods,fracture identification experiments are implemented in a tight reservoir in the Ordos Basin.The results are consistent with the data of rock core observation and production,indicating the proposed methods are effective for fracture identification and evaluation.
文摘The identification of fractures is of great importance in gravity and magnetic data processing and interpretation.In this study,four fracture identification methods are applied,and widely used in processing and analysis of the gravity anomaly,including vertical second derivative method,tilt derivative method,theta map method and normalized differential method,for gravity data acquired in a given area in Heilongjiang.By comparing the distribution of the zero contour or maximum contour,we summarize the application effects,and both advantages and disadvantages of each method.It is found that tilt derivative method and normalized differential method provide better effects than other two methods:the narrower anomaly gradient belt and higher identification precision of fracture or geological boundary.The inferred fractures and geological boundaries have a great match with the results obtained from geologic map and remote sensing data interpretation.Those study results have definitely provided the theoretical foundation for identifying faults and the geological boundaries.
基金supported by the Sinopec Science and Technology Research Project(No.P22161 and No.24029).
文摘The gravity gradient anomaly zone is produced due to density diff erences on both sides of a fault.Tracking of extreme points enables the characterization and description of fault locations.However,for some deep-seated faults with large burial depths and secondary faults with moderate burial depths,the gravity horizontal total gradient anomaly must be enhanced using the concept of dip angle to strengthen the weak anomaly extraction for the identification of more fault information.This method was used to predict five regional deep-seated faults and six secondary faults in the Miquan region.The fracture plane extends in a near north-northeast direction;that is,it mostly expands out of the study area,spreads out in a trumpet shape to the southwest,and converges to the northeast.Fracture activity is an important factor in controlling structural units or local structures.The Miquan block is located in a complex structural zone in front of the Bogeda Mountains,which have very complex surface and subsurface geological conditions,and seismic data are unideal.Therefore,fracture prediction results using gravity data are important in-depth understanding of the structure in this area.
基金funded by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735).
文摘Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金financially supported by Mitsubishi Heavy Industries,Ltd.,Japanthe National Natural Science Foundation of China(Nos.11572171,11632010 and U1533134)the opening project(No.KFJJ15-12M)of State Key Laboratory of Explosion Science and Technology(Beijing Institute of Technology)
文摘The fracture modes of low alloy steels and cast irons under tensile and fatigue conditions were identified by electron back-scattered diffraction（EBSD） misorientation analysis in this research. The curves of grain reference orientation deviation（GROD） distribution perpendicular to the fracture surface were obtained by EBSD observation, and the characteristics of each fracture mode were identified. The GROD value of the specimen fractured in tension decreases to a constant related to the elongation of corresponding specimen in the far field（farther than 5 mm away from the fracture surface）. The peak exhibits in GROD curves of two smooth specimens and a notched specimen near the fracture surface（within 5 mm away from the fracture surface）, and the formation mechanisms were discussed in detail based on the influences of specimen geometries（smooth or notched） and material toughness. The GROD value of fatigue fractured specimen is close to that at undeformed condition in the whole field, except the small area near the crack path. The loading conditions（constant stress amplitude loading or constant stress intensity factor range K loading） and the EBSD striation formation during fatigue crack propagation were also studied by EBSD observation parallel to the crack path.
基金Supported by the China Youth Program of National Natural Science Foundation(42002134)The 14th Special Support Program of China Postdoctoral Science Foundation(2021T140735).
文摘An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.
基金supported by the National Natural Science Foundation of China(No.52274013)the Fundamental Research Funds for the Central Universities(No.2024ZDPYYQ1005)+1 种基金the National Key Research and Development Program of China(No.2021YFC2902103)the Independent Research Project of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources,CUMT(No.SKLCRSM23X002).
文摘Identifying the real fracture of rock hidden in acoustic emission(AE)source clusters(AE-depicted microcrack zone)remains challenging and crucial.Here we revealed the AE energy(representing dissipated energy)distribution rule in the rock microcrack zone and proposed an AE-energy-based method for identifying the real fracture.(1)A set of fracture experiments were performed on granite using wedgeloading,and the fracture process was detected and recorded by AE.The microcrack zone associated with the energy dissipation was characterized by AE sources and energy distribution,utilizing our selfdeveloped AE analysis program(RockAE).(2)The accumulated AE energy,an index representing energy dissipation,across the AE-depicted microcrack zone followed the normal distribution model(the mean and variance relate to the real fracture path and the microcrack zone width).This result implies that the nucleation and coalescence of massive cracks(i.e.,real fracture generation process)are supposed to follow a normal distribution.(3)Then,we obtained the real fracture extension path by joining the peak positions of the AE energy normal distribution curve at different cross-sections of the microcrack zone.Consequently,we distinguished between the microcrack zone and the concealed real fracture within it.The deviation was validated as slight as 1–3 mm.
基金the financial support by National Natural Science Foundation of China(No.52334001)。
文摘Shale reservoirs contain numerous bedding fractures,making the formation of complex fracture networks during fracturing a persistent technical challenge in evaluating shale fracture morphology.Distributed optical fiber sensing technology can effectively capture the process of fracture initiation and propagation,yet the evaluation method for the initiation and propagation of bedding fractures remains immature.This study integrates a distributed optical fiber sensing device based on optical frequency domain reflectometry(OFDR)with a large-scale true tri-axial fracturing physical simulation apparatus to conduct real-time monitoring experiments on shale samples from the Lianggaoshan Formation in the Sichuan Basin,where bedding is well-developed.The experimental results demonstrate that two bedding fractures in the shale sample initiated and propagated.The evolution characteristics of fiber-optic strain in a horizontal adjacent well,induced by the initiation and propagation of bedding fractures,are characterized by the appearance of a tensile strain convergence zone in the middle of the optical fiber,flanked by two compressive strain convergence zones.The initiation and propagation of the distal bedding fracture causes the fiber-optic strain in the horizontal adjacent well to superimpose,with the asymmetric propagation of the bedding fracture leading to an asymmetric tensile strain convergence zone in the optical fiber.Utilizing a finite element method coupled with a cohesive element approach,a forward model of fiber-optic strain in the horizontal adjacent well induced by the initiation and propagation of hydraulic fracturing bedding fractures was constructed.Numerical simulation analyses were conducted to evaluate the evolution of fiber-optic strain in the horizontal adjacent well,confirming the correctness of the observed evolution characteristics.The presence of a"wedge-shaped"tensile strain convergence zone in the fiber-optic strain waterfall plot,accompanied by two compressive strain convergence zones,indicates the initiation and propagation of bedding fractures during the fracturing process.These findings provide valuable insights for interpreting distributed fiber-optic data in shale fracturing field applications.
基金financially supported by the National Natural Science Foundation of China(Grant No.52079150)Science and Technology Major Project of the Xizang Autonomous Region of China(XZ202201ZD0003G)Water Conservancy Technology Demonstration Project(SF-202404).
文摘This study aims to enhance the digital drilling process monitoring(DPM)or monitoring while drilling(MWD)technique,which is a widely recognized method in geological exploration for evaluating rock mass quality.First,robust displacement and torque measurement facilities for rotary-core drilling are discussed.The conventional cable encoder for displacement measurement is replaced with a magnetostrictive displacement sensor,which is more reliable in harsh field drilling environments.This enables the measurement of the bit position with an accuracy of<1 mm.Most importantly,this new instrument is proven to be successful in improving the detection of structural discontinuities with thicknesses>1 mm.In addition,by measuring the electric current of the driving motor,the torque applied to the bit is conveniently and accurately converted.These innovations ensure high-quality data collection for DPM practices.Second,two indices derived from DPM are proposed to quantitatively describe rock mass quality.The specific energy index(SEI)and specific penetration index(SPI)are based on the principles of energy conservation and Mohr-Coulomb failure criterion,respectively.Extensive field tests conducted in a dam grouting area confirm a linear relationship between the thrust force and penetration per rotation,and between the torque and penetration per rotation.The correlation ratios of the related regressions are typically>0.9.These two indices allow for the quantitative interpretation of DPM data into rock mechanics characteristics,such as uniaxial compressive strength,rock quality designation(RQD),and rock mass permeability,eliminating the need for subjective judgment normally involved in the currently used rock mass quality rating approaches.
