Volume 25: Accelerated Energy Innovations and Emerging Technologies

Research on Prediction of Drilling Energy Consumption Based on Mechanism and Data Hybrid Drive Kangping Gao, Xinxin Xu, Shengjie Jiao



Due to the harsh and changeable drilling environment and complex energy flow conditions, it is difficult to obtain an accurate and reliable energy consumption (prediction model. To make up for the above shortcomings, taking into account the advantages of accurate and convenie nt power system measurement, an EC prediction model driven by a combination of mechanism and data is proposed. Based on the deviation b etween actual EC results and theoretical mechanism model calculation results, the least square suppo rt vector machine (LSSVM) data compensation model is established. And the whale optimization algorithm based on von Neumann topology is used to optimize the parameters of the LSSVM model. The experimental results show that the prediction error of the proposed method is 1.69%. Compared with the prediction results of the mechanism model and the data data-driven model, the average prediction error of the proposed method is reduced by 0.27% and 2.9%.

Keywords drilling, energy consumption prediction, data compensation, hybrid drive

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