In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump...In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.展开更多
In this paper, on the basis of the observational hydrographic data obtained from the eighth cruise of PRC-USA bilateral air-sea interaction program, and combined with the sea surface temperature (SST) charts provided ...In this paper, on the basis of the observational hydrographic data obtained from the eighth cruise of PRC-USA bilateral air-sea interaction program, and combined with the sea surface temperature (SST) charts provided by NOAA, the data obtained from moored thermistor chains supplied by L. J. Mangum and sea level data provided by K. Wyrtki, the ocean conditions since October, 1989 in the western tropical Pacific are exposed, which indicate that 1990 is a year with weak El Nino event similar to the 1980 El Nino event, and the North Equatorial Countercurrent (NECC) has made a good contribution to the propagation of warm water from the Western to the Central and Eastern Pacific, a characteristic similar to that of the 1976 El Nino event. The 1990 weak El Nino event will soon fall into decay.展开更多
Timely anomaly detection is critical for optimizing gas production in plunger lift systems,where equipment failures and operational issues can cause significant disruptions.This paper introduces a two-dimensional conv...Timely anomaly detection is critical for optimizing gas production in plunger lift systems,where equipment failures and operational issues can cause significant disruptions.This paper introduces a two-dimensional convolutional neural network(2D-CNN)model designed to diagnose abnormal operating conditions in gas wells utilizing plunger lift technology.The model was trained using an extensive dataset comprising casing and tubing pressure measurements gathered from multiple wells experiencing both normal and anomalous operations.Input data underwent a rigorous preprocessing pipeline involving cleaning,ratio calculation,window segmentation,and matrix transformation.Employing separate pre-training and transfer learning methods,the model's efficacy was validated through stringent testing on new,previously unseen field data.Results demonstrate the model's acceptable performance and strong diagnostic capabilities on this novel data from various wells within the operational block.This confirms its potential to fulfill practical field requirements by offering guidance for adjusting production systems in plunger lift-assisted wells.Ultimately,this data-driven,automated diagnostic approach provides valuable theoretical insights and technical support for sustaining gas well production rates.展开更多
Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification a...Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.展开更多
文摘In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
文摘In this paper, on the basis of the observational hydrographic data obtained from the eighth cruise of PRC-USA bilateral air-sea interaction program, and combined with the sea surface temperature (SST) charts provided by NOAA, the data obtained from moored thermistor chains supplied by L. J. Mangum and sea level data provided by K. Wyrtki, the ocean conditions since October, 1989 in the western tropical Pacific are exposed, which indicate that 1990 is a year with weak El Nino event similar to the 1980 El Nino event, and the North Equatorial Countercurrent (NECC) has made a good contribution to the propagation of warm water from the Western to the Central and Eastern Pacific, a characteristic similar to that of the 1976 El Nino event. The 1990 weak El Nino event will soon fall into decay.
基金the support of the National Natural Science Foundation of China(Grant No.52474064)Frontier Interdisciplinary Exploration Research Program of China University of Petroleum,Beijing(Grant No.2462024XKQY005).
文摘Timely anomaly detection is critical for optimizing gas production in plunger lift systems,where equipment failures and operational issues can cause significant disruptions.This paper introduces a two-dimensional convolutional neural network(2D-CNN)model designed to diagnose abnormal operating conditions in gas wells utilizing plunger lift technology.The model was trained using an extensive dataset comprising casing and tubing pressure measurements gathered from multiple wells experiencing both normal and anomalous operations.Input data underwent a rigorous preprocessing pipeline involving cleaning,ratio calculation,window segmentation,and matrix transformation.Employing separate pre-training and transfer learning methods,the model's efficacy was validated through stringent testing on new,previously unseen field data.Results demonstrate the model's acceptable performance and strong diagnostic capabilities on this novel data from various wells within the operational block.This confirms its potential to fulfill practical field requirements by offering guidance for adjusting production systems in plunger lift-assisted wells.Ultimately,this data-driven,automated diagnostic approach provides valuable theoretical insights and technical support for sustaining gas well production rates.
基金support from the Key Project of the National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expenses of Ministry of Education (N100604001)
文摘Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.
