Abstract:
The non-stop intermittent pumping wells are good to improve the pumping efficiency and reduce the energy consumption, while in the process of determining the working system, there are many problems, such as the low operability of the actual determination method, the great influence of subjective factors, and the weak individualization of single well design. By analyzing the influencing factors of the working system, based on the optimization of 9 relatively independent factors, by using big data mining technology, taking the normal running time and running period as the analysis mining object, the adaptability of common data mining algorithms is compared and analyzed, and the algorithm with the strongest adaptability is selected. The results show that BPNN algorithm is better than R-SVM and MRA algorithm in regression calculation, C-SVM algorithm is better than BAYSD and NBAY algorithm in classification algorithm, and C-SVM-BPNN algorithm is better than BAYSD and NBAY algorithm in system efficiency and pump efficiency. The research results have a good guiding role in determining the optimal working system of non-stop intermittent pumping wells.