基于PSO-ELM的水平井自喷期“多段式”产量预测方法——以玛湖油田百口泉组致密砾岩油藏为例

准确预测油气井动态产量对油田高效开发意义重大，是单井累产油预测以及部署政策优化的关键。玛瑚油田百口泉组致密砾岩油藏水平井自喷期产量呈“多段式”特征，在实际生产过程中，油气井产量受储层物性、压裂工艺参数等多种因素综合影响，传统产量预测方法及数值模拟法考虑影响因素有限，预测方法适用性差。在产量特征认识基础之上，利用主成分分析法优选油层厚度、地层压力、总砂量、渗透率、压裂簇数及含油饱和度六个主控因素，采用粒子群算法优化ELM的输入权值与隐含层偏置，建立了玛湖油田水平井产量预测模型。预测结果表明，PSO-ELM对比传统预测模型具有计算速度快、泛化能力强、预测精度高的优点，利用该方法预测了5口水平井的单井产量，平均误差在2.14%~5.28%，与实际产量吻合良好。

A PSO-ELM-based "multi-stage" production prediction method for horizontal wells with self-injection period --Example of a dense conglomerate reservoir in the Baikouquan Formation of the Mahu Oilfield

Accurate prediction of dynamic production of oil and gas wells is of great significance to the efficient development of oil fields, and is the key to the prediction of cumulative oil production of single wells and optimization of deployment policies. In the actual production process, the production of oil and gas wells is influenced by various factors such as reservoir properties and fracturing process parameters, etc. Traditional production prediction methods and numerical simulation methods have limited consideration of the influencing factors and poor applicability of the prediction methods. Based on the understanding of production characteristics, the six main control factors of formation thickness, formation pressure, total sand volume, permeability, number of fracture clusters and oil saturation are selected by using principal component analysis, and the input weights of ELM and hidden layer bias are optimized by using particle swarm algorithm to establish a horizontal well production prediction model in Mahu oilfield. The prediction results show that PSO-ELM has the advantages of fast calculation speed, strong generalization ability and high prediction accuracy compared with the traditional prediction model, and the single well production of five horizontal wells was predicted by this method with an average error of 2.14%~5.28%, which is in good agreement with the actual production.