Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times beca...Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.展开更多
To thoroughly understand the dynamic mechanism of hydrocarbon expulsion from deep source rocks,in this study,five types of hydrocarbon expulsion dynamics(thermal expansion,hydrocarbon diffusion,compaction,product volu...To thoroughly understand the dynamic mechanism of hydrocarbon expulsion from deep source rocks,in this study,five types of hydrocarbon expulsion dynamics(thermal expansion,hydrocarbon diffusion,compaction,product volume expansion,and capillary pressure difference(CPD))are studied.A model is proposed herein to evaluate the relative contribution of different dynamics for hydrocarbon expulsion using the principle of mass balance,and the model has been applied to the Cambrian source rocks in the Tarim Basin.The evaluation results show that during hydrocarbon expulsion from the source rocks,the relative contribution of CPD is the largest(>50%),followed by compaction(10%-40%),product volume expansion(5%-30%),and thermal expansion(2%-20%).The relative contribution of diffusion to hydrocarbon expulsion is minimal(<10%).These results demonstrate that CPD plays an important role in the hydrocarbon expulsion process of deep source rocks.The hydrocarbon expulsion process of source rocks can be categorized into three stages based on the contribution of different dynamics to the process:the first stage is dominated by compaction and diffusion to expel hydrocarbons,the second stage is dominated by product volume expansion and CPD,and the third stage is dominated by product volume expansion and CPD.This research offers new insights into hydrocarbon exploration in tight oil and gas reservoirs.展开更多
传统的静态分析方法大多不能准确处理脚本与网络交互的过程,且会引入不可达路径,动态分析则需要搭建实验环境和手工分析。针对上述问题,提出一种基于符号执行的Python攻击脚本分析平台Py Ex Z3+。通过对Python脚本的动态符号执行及路径...传统的静态分析方法大多不能准确处理脚本与网络交互的过程,且会引入不可达路径,动态分析则需要搭建实验环境和手工分析。针对上述问题,提出一种基于符号执行的Python攻击脚本分析平台Py Ex Z3+。通过对Python脚本的动态符号执行及路径探索,可以获得触发攻击的输入流量及相应的输出攻击载荷,以此实现对Python攻击脚本的自动化分析。采用循环识别及运行时解析等优化策略,使程序更快进入目标代码。实验结果表明,Py Ex Z3+相比CHEF,Py Ex Z3等符号执行工具,具有更高的路径覆盖率和执行效率,同时Py Ex Z3+能够对目标脚本程序进行动态检测,实现高效、可行的自动化分析。展开更多
In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refine...In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refinement action.A force Jacobian matrix is calibrated with only one demonstration,based on which stable and safe expert action can be generated.The deep deterministic policy gradients(DDPG)method is employed to learn the refinement action,which aims to improve the assembly efficiency.Second,an episode-step exploration strategy is developed,which uses the expert action as a benchmark and adjusts the exploration intensity dynamically.A safety-efficiency reward function is designed for the compliant insertion.Third,to improve the adaptability with different components,a skill saving and selection mechanism is proposed.Several typical components are used to train the skill models.And the trained models and force Jacobian matrices are saved in a skill pool.Given a new component,the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks.Fourth,a simulation environment is established under the guidance of the force Jacobian matrix,which avoids tedious training process on real robotic systems.Simulation and experiments are conducted to validate the effectiveness of the proposed methods.展开更多
基金funded by the National Natural Science Foundation of China (No.42074140)the Scientific Research and Technology Development Project of China National Petroleum Corporation (No.2021ZG02)。
文摘Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value.
基金This study is financially supported by the Joint Fund of the National Natural Science Foundation of China under grant number U19B6003-02-04the Science Foundation of China University of Petroleum,Beijing,under grant number 2462020BJRC005 and 2462022YXZZ007+1 种基金the National Natural Science Foundation of China under grant number 42102145the China National Petroleum Corporation's"14th Five-Year Plan"major scientific projecs under grant number 2021DJ0101.
文摘To thoroughly understand the dynamic mechanism of hydrocarbon expulsion from deep source rocks,in this study,five types of hydrocarbon expulsion dynamics(thermal expansion,hydrocarbon diffusion,compaction,product volume expansion,and capillary pressure difference(CPD))are studied.A model is proposed herein to evaluate the relative contribution of different dynamics for hydrocarbon expulsion using the principle of mass balance,and the model has been applied to the Cambrian source rocks in the Tarim Basin.The evaluation results show that during hydrocarbon expulsion from the source rocks,the relative contribution of CPD is the largest(>50%),followed by compaction(10%-40%),product volume expansion(5%-30%),and thermal expansion(2%-20%).The relative contribution of diffusion to hydrocarbon expulsion is minimal(<10%).These results demonstrate that CPD plays an important role in the hydrocarbon expulsion process of deep source rocks.The hydrocarbon expulsion process of source rocks can be categorized into three stages based on the contribution of different dynamics to the process:the first stage is dominated by compaction and diffusion to expel hydrocarbons,the second stage is dominated by product volume expansion and CPD,and the third stage is dominated by product volume expansion and CPD.This research offers new insights into hydrocarbon exploration in tight oil and gas reservoirs.
文摘传统的静态分析方法大多不能准确处理脚本与网络交互的过程,且会引入不可达路径,动态分析则需要搭建实验环境和手工分析。针对上述问题,提出一种基于符号执行的Python攻击脚本分析平台Py Ex Z3+。通过对Python脚本的动态符号执行及路径探索,可以获得触发攻击的输入流量及相应的输出攻击载荷,以此实现对Python攻击脚本的自动化分析。采用循环识别及运行时解析等优化策略,使程序更快进入目标代码。实验结果表明,Py Ex Z3+相比CHEF,Py Ex Z3等符号执行工具,具有更高的路径覆盖率和执行效率,同时Py Ex Z3+能够对目标脚本程序进行动态检测,实现高效、可行的自动化分析。
基金supported by National Key Research and Development Program of China(No.2018AAA0103005)National Natural Science Foundation of China(No.61873266)。
文摘In this paper,an efficient skill learning framework is proposed for robotic insertion,based on one-shot demonstration and reinforcement learning.First,the robot action is composed of two parts:expert action and refinement action.A force Jacobian matrix is calibrated with only one demonstration,based on which stable and safe expert action can be generated.The deep deterministic policy gradients(DDPG)method is employed to learn the refinement action,which aims to improve the assembly efficiency.Second,an episode-step exploration strategy is developed,which uses the expert action as a benchmark and adjusts the exploration intensity dynamically.A safety-efficiency reward function is designed for the compliant insertion.Third,to improve the adaptability with different components,a skill saving and selection mechanism is proposed.Several typical components are used to train the skill models.And the trained models and force Jacobian matrices are saved in a skill pool.Given a new component,the most appropriate model is selected from the skill pool according to the force Jacobian matrix and directly used to accomplish insertion tasks.Fourth,a simulation environment is established under the guidance of the force Jacobian matrix,which avoids tedious training process on real robotic systems.Simulation and experiments are conducted to validate the effectiveness of the proposed methods.
