Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located...Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.展开更多
Accurate geomechanical parameters are key factors for stability evaluation, disaster forecasting, structural design, and supporting optimization. The intelligent back analysis method based on the monitored information...Accurate geomechanical parameters are key factors for stability evaluation, disaster forecasting, structural design, and supporting optimization. The intelligent back analysis method based on the monitored information is widely recognized as the most efficient and cost-effective technique for inverting parameters. To address the low accuracy of measured data, and the scarcity of comprehensive datasets, this study proposes an innovative back analysis framework tailored for small sample sizes. We introduce a multi-faceted back analysis approach that combines data augmentation with advanced optimization and machine learning techniques. The auxiliary classifier generative adversarial network (ACGAN)-based data augmentation algorithm is first employed to generate synthetic yet realistic samples that adhere to the underlying probability distribution of the original data, thereby expanding the dataset and mitigating the impact of small sample sizes. Subsequently, we harness the power of optimized particle swarm optimization (OPSO) integrated with support vector machine (SVM) to mine the intricate nonlinear relationships between input and output variables. Then, relying on a case study, the validity of the augmented data and the performance of the developed OPSO-SVM algorithms based on two different sample sizes are studied. Results show that the new datasets generated by ACGAN almost coincide with the actual monitored convergences, exhibiting a correlation coefficient exceeding 0.86. Furthermore, the superiority of the OPSO-SVM algorithm is also demonstrated by comparing the displacement prediction capability of various algorithms through four indices. It is also indicated that the relative error of the predicted displacement values reduces from almost 20% to 5% for the OPSO-SVM model trained with 25 samples and that trained with 625 samples. Finally, the inversed parameters and corresponding convergences predicted by the two OPSO-SVM models trained with different samples are discussed, indicating the feasibility of the combination application of ACGAN and OPSO-SVM in back analysis of geomechanical parameters. This endeavor not only facilitates the progression of underground engineering analysis in scenarios with limited data, but also serves as a pivotal reference for both researchers and practitioners alike.展开更多
Reservoir evaluation is important in identifying oil and gas sweet spots in sedimentary basins.This also holds true in the Tarim Basin,where the ultra-deep oil and gas-bearing formations have high present-day in situ ...Reservoir evaluation is important in identifying oil and gas sweet spots in sedimentary basins.This also holds true in the Tarim Basin,where the ultra-deep oil and gas-bearing formations have high present-day in situ stress and geothermal temperature in addition to their considerable depth as a result of multiple stages of tectonic evolution.Traditional reservoir evaluation methods are based mainly on analyses of reservoir parameters like porosity,permeability,and pore throat structure;these parameters can sometimes vary dramatically in areas with complex Structures.Geomechanics-based reservoir evaluations are favored as they adequately capture the impact of tectonic processes on reservoirs,especially those in the Tarim Basin.This study evaluates the ultra-deep clastic reservoirs in the Kuqa Depression of the Tarim Basin by integrating the geomechanical parameters including elastic modulus,natural fracture density,and present-day in situ stress into a 3D geological modeling-based reservoir evaluation.The entropy weight method is introduced to establish a comprehensive index(Q)for reservoir evaluation.The results show that the positive correlation of the daily gas production rate of representative wells in the study area with this indicator is an effective way of reservoir evaluation in ultra-deep areas with complex structures.展开更多
Slope failure triggered by heavy rainfall is very common in tropical and subtropical regions and a cause of major social and economic damage.Landslide susceptibility maps can be generated using geographical informatio...Slope failure triggered by heavy rainfall is very common in tropical and subtropical regions and a cause of major social and economic damage.Landslide susceptibility maps can be generated using geographical information systems(GIS)and limit equilibrium slope stability models coupled or not to hydrological equations.This study investigated the efficacy of four models used for slope stability analysis in predicting landslide-susceptible areas in a GIS environment.The selected models are the infinite slope,the shallow slope stability model(SHALSTAB),the stability index mapping(SINMAP),and the transient rainfall infiltration and grid-based regional slope-stability(TRIGRS).For comparisons,the authors(a)included the infinite slope equation in all models,(b)clearly defined input parameters and failure triggering mechanisms for each simulation(soil depth,water table height,rainfall intensity),(c)determined appropriate values for each model to obtain stability levels that represented similar hydrogeotechnical conditions,and(d)considered upper-third areas of landslide scars to estimate the reliability of susceptibility maps using validation indices.An intense rainfall event occurred in Serra do Mar,Brazil in January 2014 triggered hundreds of landslides and was used for back analysis and evaluation of the slope stability analysis models.When rainfall intensity is not considered,the four models produced very similar results.The most reliable landslide susceptibility map was generated using TRIGRS and considering the granite residual granite soils geological-geotechnical unit,subjected to a rainfall intensity of 210 mm for 2 h under unsaturated conditions.展开更多
Displacement-monitoring-based back analysis is a popular method for geomechanical parameter estimation.However,due to the delayed installation of multi-point extensometers,the monitoring curve is only a part of the ov...Displacement-monitoring-based back analysis is a popular method for geomechanical parameter estimation.However,due to the delayed installation of multi-point extensometers,the monitoring curve is only a part of the overall one,leading to displacement loss.Besides,the monitoring and construction time on the monitoring curve is difficult to determine.In the literature,the final displacement was selected for the back analysis,which could induce unreliable results.In this paper,a displacement-based back analysis method to mitigate the influence of displacement loss is developed.A robust hybrid optimization algorithm is proposed as a substitute for time-consuming numerical simulation.It integrates the strengths of the nonlinear mapping and prediction capability of the support vector machine(SVM)algorithm,the global searching and optimization characteristics of the optimized particle swarm optimization(OPSO)algorithm,and the nonlinear numerical simulation capability of ABAQUS.To avoid being trapped in the local optimum and to improve the efficiency of optimization,the standard PSO algorithm is improved and is compared with other three algorithms(genetic algorithm(GA),simulated annealing(SA),and standard PSO).The results indicate the superiority of OPSO algorithm.Finally,the hybrid optimization algorithm is applied to an engineering project.The back-analyzed parameters are submitted to numerical analysis,and comparison between the calculated and monitoring displacement curve shows that this hybrid algorithm can offer a reasonable reference for geomechanical parameters estimation.展开更多
Among the several activities involved in oil exploration are the determination of hydrocarbon in-place and mechanical competency of the oil reservoir.The pressure regimes of the formation have also become vital proper...Among the several activities involved in oil exploration are the determination of hydrocarbon in-place and mechanical competency of the oil reservoir.The pressure regimes of the formation have also become vital properties which must be well known to ensure preliminary awareness of the hydraulic fracturing.This study seeks to adopt a prediction strategy of the overall geo-mechanical competency and strength of the formation,using a less stressful computational process and an empirical analysis,developed using three wells from ED BON area in parts of Niger Delta.Elastic constants such as Poisson Ratio,Young's,Shear and Bulk moduli which are the parameters for characterizing rock mechanical properties were estimated,as well as the subsurface formation pressures and the associated fracture gradient using P-wave sonic and density logs.The results from the analysis showed that there is correlation between elastic strength and fracture pressure.展开更多
基金supported by the "Light of West China" Program of Chinese Academy of Sciences (Grant No.Y6R2250250)the National Basic Research Program of China (973 Program, Grant No.2013CB733201)+2 种基金the One-Hundred Talents Program of Chinese Academy of Sciences (LijunSu)the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No.QYZDB-SSW-DQC010)the Youth Fund of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (Grant No. Y6K2110110)
文摘Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.
基金supported by the National Natural Science Foundation of China(Grant Nos.51991392 and 51922104).
文摘Accurate geomechanical parameters are key factors for stability evaluation, disaster forecasting, structural design, and supporting optimization. The intelligent back analysis method based on the monitored information is widely recognized as the most efficient and cost-effective technique for inverting parameters. To address the low accuracy of measured data, and the scarcity of comprehensive datasets, this study proposes an innovative back analysis framework tailored for small sample sizes. We introduce a multi-faceted back analysis approach that combines data augmentation with advanced optimization and machine learning techniques. The auxiliary classifier generative adversarial network (ACGAN)-based data augmentation algorithm is first employed to generate synthetic yet realistic samples that adhere to the underlying probability distribution of the original data, thereby expanding the dataset and mitigating the impact of small sample sizes. Subsequently, we harness the power of optimized particle swarm optimization (OPSO) integrated with support vector machine (SVM) to mine the intricate nonlinear relationships between input and output variables. Then, relying on a case study, the validity of the augmented data and the performance of the developed OPSO-SVM algorithms based on two different sample sizes are studied. Results show that the new datasets generated by ACGAN almost coincide with the actual monitored convergences, exhibiting a correlation coefficient exceeding 0.86. Furthermore, the superiority of the OPSO-SVM algorithm is also demonstrated by comparing the displacement prediction capability of various algorithms through four indices. It is also indicated that the relative error of the predicted displacement values reduces from almost 20% to 5% for the OPSO-SVM model trained with 25 samples and that trained with 625 samples. Finally, the inversed parameters and corresponding convergences predicted by the two OPSO-SVM models trained with different samples are discussed, indicating the feasibility of the combination application of ACGAN and OPSO-SVM in back analysis of geomechanical parameters. This endeavor not only facilitates the progression of underground engineering analysis in scenarios with limited data, but also serves as a pivotal reference for both researchers and practitioners alike.
基金founded by China National Petroleum Corporation Major Science and Technology Project“Research and Application of Key Technologies for the Development of Ultra-Deep Oil and Gas Reservoirs”(2023ZZ14-03).
文摘Reservoir evaluation is important in identifying oil and gas sweet spots in sedimentary basins.This also holds true in the Tarim Basin,where the ultra-deep oil and gas-bearing formations have high present-day in situ stress and geothermal temperature in addition to their considerable depth as a result of multiple stages of tectonic evolution.Traditional reservoir evaluation methods are based mainly on analyses of reservoir parameters like porosity,permeability,and pore throat structure;these parameters can sometimes vary dramatically in areas with complex Structures.Geomechanics-based reservoir evaluations are favored as they adequately capture the impact of tectonic processes on reservoirs,especially those in the Tarim Basin.This study evaluates the ultra-deep clastic reservoirs in the Kuqa Depression of the Tarim Basin by integrating the geomechanical parameters including elastic modulus,natural fracture density,and present-day in situ stress into a 3D geological modeling-based reservoir evaluation.The entropy weight method is introduced to establish a comprehensive index(Q)for reservoir evaluation.The results show that the positive correlation of the daily gas production rate of representative wells in the study area with this indicator is an effective way of reservoir evaluation in ultra-deep areas with complex structures.
基金supported by grants2017/26081-8,S?o Paulo Research Foundation(FAPESP)130594/2017-2,Brazilian National Council for Scientific and Technological Development(CNPq)。
文摘Slope failure triggered by heavy rainfall is very common in tropical and subtropical regions and a cause of major social and economic damage.Landslide susceptibility maps can be generated using geographical information systems(GIS)and limit equilibrium slope stability models coupled or not to hydrological equations.This study investigated the efficacy of four models used for slope stability analysis in predicting landslide-susceptible areas in a GIS environment.The selected models are the infinite slope,the shallow slope stability model(SHALSTAB),the stability index mapping(SINMAP),and the transient rainfall infiltration and grid-based regional slope-stability(TRIGRS).For comparisons,the authors(a)included the infinite slope equation in all models,(b)clearly defined input parameters and failure triggering mechanisms for each simulation(soil depth,water table height,rainfall intensity),(c)determined appropriate values for each model to obtain stability levels that represented similar hydrogeotechnical conditions,and(d)considered upper-third areas of landslide scars to estimate the reliability of susceptibility maps using validation indices.An intense rainfall event occurred in Serra do Mar,Brazil in January 2014 triggered hundreds of landslides and was used for back analysis and evaluation of the slope stability analysis models.When rainfall intensity is not considered,the four models produced very similar results.The most reliable landslide susceptibility map was generated using TRIGRS and considering the granite residual granite soils geological-geotechnical unit,subjected to a rainfall intensity of 210 mm for 2 h under unsaturated conditions.
基金by the National Natural Science Foundation of China(Grant No.51991392)the National Natural Science Foundation of China(Grant No.51922104).
文摘Displacement-monitoring-based back analysis is a popular method for geomechanical parameter estimation.However,due to the delayed installation of multi-point extensometers,the monitoring curve is only a part of the overall one,leading to displacement loss.Besides,the monitoring and construction time on the monitoring curve is difficult to determine.In the literature,the final displacement was selected for the back analysis,which could induce unreliable results.In this paper,a displacement-based back analysis method to mitigate the influence of displacement loss is developed.A robust hybrid optimization algorithm is proposed as a substitute for time-consuming numerical simulation.It integrates the strengths of the nonlinear mapping and prediction capability of the support vector machine(SVM)algorithm,the global searching and optimization characteristics of the optimized particle swarm optimization(OPSO)algorithm,and the nonlinear numerical simulation capability of ABAQUS.To avoid being trapped in the local optimum and to improve the efficiency of optimization,the standard PSO algorithm is improved and is compared with other three algorithms(genetic algorithm(GA),simulated annealing(SA),and standard PSO).The results indicate the superiority of OPSO algorithm.Finally,the hybrid optimization algorithm is applied to an engineering project.The back-analyzed parameters are submitted to numerical analysis,and comparison between the calculated and monitoring displacement curve shows that this hybrid algorithm can offer a reasonable reference for geomechanical parameters estimation.
文摘Among the several activities involved in oil exploration are the determination of hydrocarbon in-place and mechanical competency of the oil reservoir.The pressure regimes of the formation have also become vital properties which must be well known to ensure preliminary awareness of the hydraulic fracturing.This study seeks to adopt a prediction strategy of the overall geo-mechanical competency and strength of the formation,using a less stressful computational process and an empirical analysis,developed using three wells from ED BON area in parts of Niger Delta.Elastic constants such as Poisson Ratio,Young's,Shear and Bulk moduli which are the parameters for characterizing rock mechanical properties were estimated,as well as the subsurface formation pressures and the associated fracture gradient using P-wave sonic and density logs.The results from the analysis showed that there is correlation between elastic strength and fracture pressure.
