Reservoir Evaluation and Development ›› 2018, Vol. 8 ›› Issue (6): 28-32.

• Reservoir Evaluation • Previous Articles     Next Articles

Prediction of wellbore flow condition based on RBF neural network

Wang Haoru,Li Zuyou,Lu Guangliang,Tang Lei   

  1. No.1 Gas Production Plant, Southwest oil and gas Company, SINOPEC, Deyang, Sichuan 618000, China
  • Received:2018-01-07 Online:2018-12-26 Published:2018-12-13


Water generally exist in the gas well in west Sichuan gas field, which has a great influence on the steady production of gas wells. The timely grasp of the flow conditions of the gas wellbore can judge the fluid accumulation in gas well and guide the formulation of the drainage gas recovery measures. By using the pressure meter to judge the downhole conditions of the gas well had the disadvantages of poor timeliness and high cost, and the prediction of two phase flow theory often had deviations from the actual existence. So we used a large number of gas well production data and flow pressure test data to establish the prediction model of wellbore flow condition based on RBF neural network. The RBF network had the self adaptability and its output do not dependent on the initial weight. The coincidence rate between prediction results and actual results among 15 wells was 86.67 %, which indicated that the neural network model was reliable for predicting the wellbore flow conditions of gas well, and could be used to guide production.

Key words: neural network, flow condition, gas well liquid loading, drainage gas recovery

CLC Number: 

  • TE319