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Intelligent risk identification of gas drilling based on nonlinear classification network

Petroleum Science 2023年第5期20卷 3074-3084页

作者： Wen-He Xia Zong-Xu Zhao Cheng-Xiao Li Gao Li Yong-Jie Li Xing Ding Xiang-Dong Chen School of Electrical Engineering and Information Southwest Petroleum UniversityChengdu 610500SichuanChina CCDC drilling&Production Engineering Technology Research Institute Deyang 618500SichuanChina School of Oil&Natural gas Engineering Southwest Petroleum UniversityChengdu 610500SichuanChina School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031SichuanChina

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

关键词： gas drilling Intelligent identification of drilling risk Nonlinear classification RBF Neural Network K-means algorithm

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An intelligent identification method of safety risk while drilling in gas drilling

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Petroleum Exploration and Development 2022年第2期49卷 428-437页

作者： HU Wanjun XIA Wenhe LI Yongjie JIANG Jun LI Gao CHEN Yijian School of Electrical Engineering and Information Southwest Petroleum UniversityChengdu 610500China State Key Laboratory of Oil and gas Reservoir Geology and Exploitation Chengdu 610500China School of Oil&gas Engineering Southwest Petroleum UniversityChengdu 610500China School of Computer Science Southwest Petroleum UniversityChengdu 610500China

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

关键词： gas drilling safety risk intelligent risk identification few-shot learning convolution neural network measurement while drilling

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Distribution features of cuttings bed and sensitivity analysis of major drilling parameters for cuttings transport in gas drilling horizontal wells

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Journal of Hydrodynamics 2015年第6期27卷 884-893页

作者：祝效华易静刘清友 School of Mechatronic Engineering Southwest Petroleum University

In the current engineering methods for the gas horizontal drilling, the distribution features of cuttings bed remain an issue to be cleared, and the gas horizontal drilling is still in early stages of development. For on-site drilling, a 3-D transient model is established in this paper to simulate the distribution features and the transport mechanism of the cuttings bed, based on the gas-solid two-phase flow theory. The effects of major drilling parameters, such as the gas velocity, the drill pipe rotation, the cutting size and the eccentricity, on the cuttings transport efficiency are analyzed. The major findings of this study include that the cuttings begin to settle down and build up a fixed cuttings bed, in the most evident regions in front and behind the connector, the dominant parameter of the wellbore cleaning is the gas velocity, and, as the cutting size is increased, the thickness of the cuttings bed developed in the wellbore increases significantly. In addition, the eccentricity has some influence on the cuttings transport, and the drill pipe rotation has little effect on the cuttings transport.

关键词： gas drilling horizontal well cuttings transport sensitivity analysis

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