Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape iden...Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.展开更多
Geochemistry is a powerful tool to help characterize the tectonic setting of igneous rocks associations.However,when continental mafic dykes and flood basalts are the target most of the proposed geochemical discrimina...Geochemistry is a powerful tool to help characterize the tectonic setting of igneous rocks associations.However,when continental mafic dykes and flood basalts are the target most of the proposed geochemical discrimination diagrams fail to correctly classify them,i.e.many mafic展开更多
Located at the National University in Bogotá,Colombia,this paper presents the development of the integration of kinematics,simplified kinetic and zero-dimensional models of indicator diagram of a double-acting,si...Located at the National University in Bogotá,Colombia,this paper presents the development of the integration of kinematics,simplified kinetic and zero-dimensional models of indicator diagram of a double-acting,single-cylinder steam engine;the model integration is done for two configurations of the distribution system to simulate the instantaneous torque and the average.Two simulations are carried out at different steam entry conditions and an advance angle of 0°for the distribution system,fed with the data obtained from the characterization and metrological survey of the VE(vapor engine)parts in a CAD(computer-aided design)system.It is verified that integrating the models constitutes a simulation tool that allows for the instantaneous torque deliveries and average torque of the accurate operation’s VE.展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
文摘Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.
基金The Brazilian Sao Paulo State Research Foundation(FAPESP)partially supported this research(grants 2012/15824-6 and 2012/07243-3)
文摘Geochemistry is a powerful tool to help characterize the tectonic setting of igneous rocks associations.However,when continental mafic dykes and flood basalts are the target most of the proposed geochemical discrimination diagrams fail to correctly classify them,i.e.many mafic
文摘Located at the National University in Bogotá,Colombia,this paper presents the development of the integration of kinematics,simplified kinetic and zero-dimensional models of indicator diagram of a double-acting,single-cylinder steam engine;the model integration is done for two configurations of the distribution system to simulate the instantaneous torque and the average.Two simulations are carried out at different steam entry conditions and an advance angle of 0°for the distribution system,fed with the data obtained from the characterization and metrological survey of the VE(vapor engine)parts in a CAD(computer-aided design)system.It is verified that integrating the models constitutes a simulation tool that allows for the instantaneous torque deliveries and average torque of the accurate operation’s VE.
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
