Simulation Calculation of Pipeline Network Yield Based on Spatio-temporal Graph Convolutional Neural Network
Flowmeter measurement values have a large deviation in crude oil gathering and transmission pipeline network,and the manual correction of simulation software is cumbersome with poor adaptive.To solve these problems,this study proposes an adaptive spatio-temporal graphic convolutional neural network production calculation method to realize the simulation calculation of crude oil gathering and transmission pipeline network production.The topology of the pipeline network is constructed with the submerged oil electric pump wells as nodes and the oil pipelines as edges.The study utilizes the graph convolutional neural network to extract the spatial information of well distribution and the temporal convolutional neural network to obtain the time series characteristics of the production data,so as to calculate the accurate production simulation results.The experimental validation is carried out on the crude oil gathering and transmission pipeline network system of an oil field.The results show that the proposed method can accurately calculate the production of each electric pump well in the pipeline network system.Compared with other baseline network models,the error indexes are reduced:the average absolute error is reduced to 0.87;the average absolute percentage error is reduced to 4.45%;the mean square error is reduced to 0.84,which proves the validity and accuracy of the proposed method.
oil and gas gathering and transportationproduction calculationgraph convolutional neural networktemporal convolutional neural networkspatio-temporal correlation
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