The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sand...The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sands were reproduced and the slump test and the direct shear test of the conditioned sands were implemented. A DEM equivalent model that can simulate the macro mechanical characteristic of the conditioned sands was proposed,and the corresponding numerical models of the slump test and the shear test were established. By selecting proper DEM model parameters,the errors of the slump values between the simulation results and the test results are in the range of 10.3%-14.3%,and the error of the curves between the shear displacement and the shear stress calculated with the DEM simulation is 4.68%-16.5% compared with that of the laboratory direct shear test. This illustrates that the proposed DEM equivalent model can approximately simulate the mechanical characteristics of the conditioned sands,which provides the basis for further simulation of the interaction between the conditioned soil and the chamber pressure system of the EPB machine.展开更多
Severe gas disasters in deep mining areas are increasing,and traditional protective coal seam mining is facing significant challenges.This paper proposes an innovative technology using soft rock as the protective seam...Severe gas disasters in deep mining areas are increasing,and traditional protective coal seam mining is facing significant challenges.This paper proposes an innovative technology using soft rock as the protective seam in the absence of an appropriate coal seam.Based on the geological engineering conditions of the new horizontal first mining area of Luling Coal Mine in Huaibei,China,the impacts of different mining parameters of the soft-rock protective seam on the pressure-relief effect of the protected coal seam were analyzed through numerical simulation.The unit stress of the protected coal seam,which was less than half of the primary rock stress,was used as the mining stress pressure-relief index.The optimized interlayer space was found to be 59 m for the first soft-rock working face,with a 2 m mining thickness and 105 m face length.The physicochemical characteristics of the orebody were analyzed,and a device selection framework for the soft-rock protective seam was developed.Optimal equipment for the working face was selected,including the fully-mechanized hydraulic support and coal cutter.A production technology that combined fully-mechanized and blasting-assisted soft-rock mining was developed.Engineering practices demonstrated that normal circulation operation can be achieved on the working face of the soft-rock protective seam,with an average advancement rate of 1.64 m/d.The maximum residual gas pressure and content,which were measured at the cut hole position of the protected coal seams(Nos.8 and 9),decreased to 0.35 MPa and 4.87 m^3/t,respectively.The results suggested that soft-rock protective seam mining can produce a significant gas-control effect.展开更多
The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction...The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.展开更多
Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when f...Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.展开更多
基金Project (2007CB714006) supported by the National Basic Research Program of China
文摘The discrete element method (DEM) was used to simulate the flow characteristic and strength characteristic of the conditioned sands in the earth pressure balance (EPB) tunneling. In the laboratory the conditioned sands were reproduced and the slump test and the direct shear test of the conditioned sands were implemented. A DEM equivalent model that can simulate the macro mechanical characteristic of the conditioned sands was proposed,and the corresponding numerical models of the slump test and the shear test were established. By selecting proper DEM model parameters,the errors of the slump values between the simulation results and the test results are in the range of 10.3%-14.3%,and the error of the curves between the shear displacement and the shear stress calculated with the DEM simulation is 4.68%-16.5% compared with that of the laboratory direct shear test. This illustrates that the proposed DEM equivalent model can approximately simulate the mechanical characteristics of the conditioned sands,which provides the basis for further simulation of the interaction between the conditioned soil and the chamber pressure system of the EPB machine.
文摘Severe gas disasters in deep mining areas are increasing,and traditional protective coal seam mining is facing significant challenges.This paper proposes an innovative technology using soft rock as the protective seam in the absence of an appropriate coal seam.Based on the geological engineering conditions of the new horizontal first mining area of Luling Coal Mine in Huaibei,China,the impacts of different mining parameters of the soft-rock protective seam on the pressure-relief effect of the protected coal seam were analyzed through numerical simulation.The unit stress of the protected coal seam,which was less than half of the primary rock stress,was used as the mining stress pressure-relief index.The optimized interlayer space was found to be 59 m for the first soft-rock working face,with a 2 m mining thickness and 105 m face length.The physicochemical characteristics of the orebody were analyzed,and a device selection framework for the soft-rock protective seam was developed.Optimal equipment for the working face was selected,including the fully-mechanized hydraulic support and coal cutter.A production technology that combined fully-mechanized and blasting-assisted soft-rock mining was developed.Engineering practices demonstrated that normal circulation operation can be achieved on the working face of the soft-rock protective seam,with an average advancement rate of 1.64 m/d.The maximum residual gas pressure and content,which were measured at the cut hole position of the protected coal seams(Nos.8 and 9),decreased to 0.35 MPa and 4.87 m^3/t,respectively.The results suggested that soft-rock protective seam mining can produce a significant gas-control effect.
文摘The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.
文摘Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.
