Fractured reservoirs have always been a big favorable area for oil and gas reservoirs,so prediction of fractures is also a research hotspot in recent years.Due to the diversity of fracture development and the unclear ...Fractured reservoirs have always been a big favorable area for oil and gas reservoirs,so prediction of fractures is also a research hotspot in recent years.Due to the diversity of fracture development and the unclear development mechanism,fracture prediction has always been a major problem.Simple numerical simulation In this paper,seismic attribute is combined with numerical simulation,logging data and actual seismic profile are used as constraints,inversion impedance value and coherent attribute are combined,and finally a property model more in line with the actual geological conditions is established.The wave equation calculation and migration processing were used to obtain the numerical simulation profile,and the actual seismic profile,fracture detection profile and numerical simulation profile were combined for analysis:①The numerical simulation section under this modeling method can greatly correspond to the actual seismic section,and the reflected results can better reflect the changes of response characteristics.②The reliability and applicability of the fracture detection technology can be determined by comparing the forward simulation profile with the fracture detection profile.展开更多
Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some l...Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some limitations.To resolve these issues,we ascertained the relation between numerical simulations of tectonic stress and the predicted distribution of fractures from the perspective of geologic genesis,based on the characteristics of the shale reservoir in the Longmaxi Formation in Dingshan;the features of fracture development in this reservoir were considered.3 D finite element method(FEM)was applied in combination with rock mechanical parameters derived from the acoustic emissions.The paleotectonic stress field of the crack formation period was simulated for the Longmaxi Formation in the Dingshan area.The splitting factor in the study area was calculated based on the rock breaking criterion.The coefficient of fracture development was selected as the quantitative prediction classification criteria for the cracks.The results show that a higher coefficient of fracture development indicates a greater degree of fracture development.On the basis of the fracture development coefficient classification,a favorable area was identified for the development of fracture prediction in the study area.The prediction results indicate that the south of the Dingshan area and the DY3 well of the central region are favorable zones for fracture development.展开更多
Analytical solutions of thermal stresses in multilayered elastic system whose materials characteristics are dependent on temperature are derived by a transfer matrix and integral transformation method.The resulting fo...Analytical solutions of thermal stresses in multilayered elastic system whose materials characteristics are dependent on temperature are derived by a transfer matrix and integral transformation method.The resulting formulation is used to calculate thermal stresses in the low temperature cracking problem of asphalt pavement.Numerical simulations and analyses are performed using different structural combinations and material characteristics of base course.And fracture temperatures are predicted for a given flexible pavement constructed with three types of asphalt mixtures based on the calculated results and experimental data.This approach serves as a better model for real pavement structure as it takes into account the relationships between the material characteristics and temperature in the pavement system.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
BACKGROUND Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine,increasing the risk of fractures.Given its high incidence,especially among older populations,it...BACKGROUND Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine,increasing the risk of fractures.Given its high incidence,especially among older populations,it is critical to have accurate and effective predictive models for fracture risk.Traditionally,clinicians have relied on a combination of factors such as demographics,clinical attributes,and radiological characteristics to predict fracture risk in these patients.However,these models often lack precision and fail to include all potential risk factors.There is a need for a more comprehensive,statistically robust prediction model that can better identify high-risk individuals for early intervention.AIM To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.METHODS The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined.The patients were selected according to strict criteria and categorized into two groups:Those with fractures(n=40)and those without fractures(n=40).Demographics,clinical attributes,biochemical indicators,bone mineral density(BMD),and radiological characteristics were collected and compared.A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model.The area under the receiver operating characteristic curve(AUROC)was used to evaluate the model’s performance.RESULTS Factors significantly associated with fracture risk included age,sex,body mass index(BMI),smoking history,BMD,vertebral trabecular alterations,and prior vertebral fractures.The final risk-prediction model was developed using the formula:(logit[P]=-3.75+0.04×age-1.15×sex+0.02×BMI+0.83×smoking history+2.25×BMD-1.12×vertebral trabecular alterations+1.83×previous vertebral fractures).The AUROC of the model was 0.93(95%CI:0.88-0.96,P<0.001),indicating strong discriminatory capabilities.CONCLUSION The fracture risk-prediction model,utilizing accessible clinical,biochemical,and radiological information,offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis.The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.展开更多
The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata ...The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.展开更多
Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs.Previous studies have proven that abundant oil resources are present in the Upper Triassic...Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs.Previous studies have proven that abundant oil resources are present in the Upper Triassic Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin,which are accumulated in typical low-permeability shale reservoirs.Natural fractures are important storage spaces and flow pathways for shale oil.In this study,characteristics of natural fractures in the Chang 7 oil-bearing layer are first analyzed.The results indicate that most fractures are shear fractures in the Heshui region,which are characterized by high-angle,unfilled,and ENE-WSW-trending strike.Subsequently,natural fracture distributions in the Yanchang Formation Chang 7 oil-bearing layer of the study area are predicted based on the R/S analysis approach.Logs of AC,CAL,ILD,LL8,and DEN are selected and used for fracture prediction in this study,and the R(n)/S(n)curves of each log are calculated.The quadratic derivatives are calculated to identify the concave points in the R(n)/S(n)curve,indicating the location where natural fracture develops.Considering the difference in sensitivity of each log to natural fracture,gray prediction analysis is used to construct a new parameter,fracture prediction indicator K,to quantitatively predict fracture development.In addition,fracture development among different wells is compared.The results show that parameter K responds well to fracture development.Some minor errors may probably be caused by the heterogeneity of the reservoir,limitation of core range and fracture size,dip angle,filling minerals,etc.展开更多
Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction metho...Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.展开更多
Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much research attention in recent years....Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much research attention in recent years. A shale's key geomechanical properties help to identify its "fracibility" its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial reserves. The models and the information they provide, in turn, enable engineers to design drilling patterns, fracture-stimulation programs and materials selection that will avoid formation damage and optimize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales(nano to macro). Well-log data, and its petrophysical interpretation to calibrate many geomechanical metrics to those measured in rock samples by laboratory techniques plays a key role in providing affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, microseismic data helps to match fracture density and propagation observed on a reservoir scale with predictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture network models. Shales complex wettability, adsorption and water imbibition characteristics have a significant influence on potential formation damage during stimulation and the short-term and longterm flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into account the multiple flow mechanisms involved(e.g., desorption, diffusion, slippage and viscous flow operating at multiple porosity levels from nano-to macro-scales). Fitting historical production data and well decline curves to mo del predictions helps to verify whether model's geomechanical assum ptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous outstanding questions associated with them.展开更多
文摘Fractured reservoirs have always been a big favorable area for oil and gas reservoirs,so prediction of fractures is also a research hotspot in recent years.Due to the diversity of fracture development and the unclear development mechanism,fracture prediction has always been a major problem.Simple numerical simulation In this paper,seismic attribute is combined with numerical simulation,logging data and actual seismic profile are used as constraints,inversion impedance value and coherent attribute are combined,and finally a property model more in line with the actual geological conditions is established.The wave equation calculation and migration processing were used to obtain the numerical simulation profile,and the actual seismic profile,fracture detection profile and numerical simulation profile were combined for analysis:①The numerical simulation section under this modeling method can greatly correspond to the actual seismic section,and the reflected results can better reflect the changes of response characteristics.②The reliability and applicability of the fracture detection technology can be determined by comparing the forward simulation profile with the fracture detection profile.
基金supported by the Open Fund (PLN 201718) of State Key Laboratory of Oil and Gas Reservoir Geology and ExploitationSouthwest Petroleum University and the Open Fund (SEC-2018-04) of Collaborative Innovation Center of Shale Gas Resources and EnvironmentSouthwest Petroleum University and the National Science and Technology Major Project of China (2017ZX05036003-003)
文摘Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some limitations.To resolve these issues,we ascertained the relation between numerical simulations of tectonic stress and the predicted distribution of fractures from the perspective of geologic genesis,based on the characteristics of the shale reservoir in the Longmaxi Formation in Dingshan;the features of fracture development in this reservoir were considered.3 D finite element method(FEM)was applied in combination with rock mechanical parameters derived from the acoustic emissions.The paleotectonic stress field of the crack formation period was simulated for the Longmaxi Formation in the Dingshan area.The splitting factor in the study area was calculated based on the rock breaking criterion.The coefficient of fracture development was selected as the quantitative prediction classification criteria for the cracks.The results show that a higher coefficient of fracture development indicates a greater degree of fracture development.On the basis of the fracture development coefficient classification,a favorable area was identified for the development of fracture prediction in the study area.The prediction results indicate that the south of the Dingshan area and the DY3 well of the central region are favorable zones for fracture development.
基金Sponsored by the Natural Science Foundation of Shandong Province of China(Grant No.ZR2009FM010)
文摘Analytical solutions of thermal stresses in multilayered elastic system whose materials characteristics are dependent on temperature are derived by a transfer matrix and integral transformation method.The resulting formulation is used to calculate thermal stresses in the low temperature cracking problem of asphalt pavement.Numerical simulations and analyses are performed using different structural combinations and material characteristics of base course.And fracture temperatures are predicted for a given flexible pavement constructed with three types of asphalt mixtures based on the calculated results and experimental data.This approach serves as a better model for real pavement structure as it takes into account the relationships between the material characteristics and temperature in the pavement system.
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.
文摘BACKGROUND Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine,increasing the risk of fractures.Given its high incidence,especially among older populations,it is critical to have accurate and effective predictive models for fracture risk.Traditionally,clinicians have relied on a combination of factors such as demographics,clinical attributes,and radiological characteristics to predict fracture risk in these patients.However,these models often lack precision and fail to include all potential risk factors.There is a need for a more comprehensive,statistically robust prediction model that can better identify high-risk individuals for early intervention.AIM To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis.METHODS The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined.The patients were selected according to strict criteria and categorized into two groups:Those with fractures(n=40)and those without fractures(n=40).Demographics,clinical attributes,biochemical indicators,bone mineral density(BMD),and radiological characteristics were collected and compared.A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model.The area under the receiver operating characteristic curve(AUROC)was used to evaluate the model’s performance.RESULTS Factors significantly associated with fracture risk included age,sex,body mass index(BMI),smoking history,BMD,vertebral trabecular alterations,and prior vertebral fractures.The final risk-prediction model was developed using the formula:(logit[P]=-3.75+0.04×age-1.15×sex+0.02×BMI+0.83×smoking history+2.25×BMD-1.12×vertebral trabecular alterations+1.83×previous vertebral fractures).The AUROC of the model was 0.93(95%CI:0.88-0.96,P<0.001),indicating strong discriminatory capabilities.CONCLUSION The fracture risk-prediction model,utilizing accessible clinical,biochemical,and radiological information,offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis.The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.
基金funded by the Natural Science Foundation of Shandong Province (ZR202103050722)National Natural Science Foundation of China (41174098)。
文摘The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.
基金supports are from the National Natural Science Foundation of China(Grant Nos.41702130,41971335)Natural Science Foundation of Jiangsu Province,China(BK20201349)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs.Previous studies have proven that abundant oil resources are present in the Upper Triassic Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin,which are accumulated in typical low-permeability shale reservoirs.Natural fractures are important storage spaces and flow pathways for shale oil.In this study,characteristics of natural fractures in the Chang 7 oil-bearing layer are first analyzed.The results indicate that most fractures are shear fractures in the Heshui region,which are characterized by high-angle,unfilled,and ENE-WSW-trending strike.Subsequently,natural fracture distributions in the Yanchang Formation Chang 7 oil-bearing layer of the study area are predicted based on the R/S analysis approach.Logs of AC,CAL,ILD,LL8,and DEN are selected and used for fracture prediction in this study,and the R(n)/S(n)curves of each log are calculated.The quadratic derivatives are calculated to identify the concave points in the R(n)/S(n)curve,indicating the location where natural fracture develops.Considering the difference in sensitivity of each log to natural fracture,gray prediction analysis is used to construct a new parameter,fracture prediction indicator K,to quantitatively predict fracture development.In addition,fracture development among different wells is compared.The results show that parameter K responds well to fracture development.Some minor errors may probably be caused by the heterogeneity of the reservoir,limitation of core range and fracture size,dip angle,filling minerals,etc.
基金This work was supported by National Natural Science Foundation of China(52130409,52121003,52004291,51874314).
文摘Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams.However,few studies focused on methods to increase permeability,and there are no suitable prediction methods for engineering applications.In this work,PFC2D software was used to simulate coal seam hydraulic fracturing.The results were used in a coupled mathematical model of the interaction between coal seam deformation and gas flow.The results show that the displacement and velocity of particles increase in the direction of minimum principal stress,and the cracks propagate in the direction of maximum principal stress.The gas pressure drop rate and permeability increase rate of the fracture model are higher than that of the non-fracture model.Both parameters decrease rapidly with an increase in the drainage time and approach 0.The longer the hydraulic fracturing time,the more complex the fracture network is,and the faster the gas pressure drops.However,the impact of fracturing on the gas drainage effect declines over time.As the fracturing time increases,the difference between the horizontal and vertical permeability increases.However,this difference decreases as the gas drainage time increases.The higher the initial void pressure,the faster the gas pressure drops,and the greater the permeability increase is.However,the influence of the initial void pressure on the permeability declines over time.The research results provide guidance for predicting the anti-reflection effect of hydraulic fracturing in underground coal mines.
基金the Department of Science & Technology (DST Ministry of Science & Technology, Government of India), for providing funding for his research through the DST-Inspire Assured Opportunity of Research Career (AORC) scheme
文摘Modeling geomechanical properties of shales to make sense of their complex properties is at the forefront of petroleum exploration and exploitation application and has received much research attention in recent years. A shale's key geomechanical properties help to identify its "fracibility" its fluid flow patterns and rates, and its in-place petroleum resources and potential commercial reserves. The models and the information they provide, in turn, enable engineers to design drilling patterns, fracture-stimulation programs and materials selection that will avoid formation damage and optimize recovery of petroleum. A wide-range of tools, technologies, experiments and mathematical techniques are deployed to achieve this. Characterizing the interconnected fracture, permeability and porosity network is an essential step in understanding a shales highly-anisotropic features on multiple scales(nano to macro). Well-log data, and its petrophysical interpretation to calibrate many geomechanical metrics to those measured in rock samples by laboratory techniques plays a key role in providing affordable tools that can be deployed cost-effectively in multiple well bores. Likewise, microseismic data helps to match fracture density and propagation observed on a reservoir scale with predictions from simulations and laboratory tests conducted on idealised/simplified discrete fracture network models. Shales complex wettability, adsorption and water imbibition characteristics have a significant influence on potential formation damage during stimulation and the short-term and longterm flow of petroleum achievable. Many gas flow mechanisms and models are proposed taking into account the multiple flow mechanisms involved(e.g., desorption, diffusion, slippage and viscous flow operating at multiple porosity levels from nano-to macro-scales). Fitting historical production data and well decline curves to mo del predictions helps to verify whether model's geomechanical assum ptions are realistic or not. This review discusses the techniques applied and the models developed that are relevant to applied geomechanics, highlighting examples of their application and the numerous outstanding questions associated with them.
