页岩气压裂井下工况多步预测方法研究

Research on a multi-step method for prediction of shale gas fracturing well conditon

为实现页岩气压裂井下工况预测,及时预控异常工况,基于粒子滤波（PF）算法与自回归滑动平均（ARMA）模型,提出优化的局部加权线性回归（LWLR）模型的方法。该方法以ARMA模型与PF算法构建PFARMA模型,并用该模型预测井口压力的变化,再将预测效果作为优化LWLR模型参数的依据,得到最优压力参数的LWLR模型,最后以某段页岩气压裂压力数据为例,比较优化的LWLR模型与传统模型的预测结果。结果表明：优化的LWLR模型预测精度有所提高,并且更能准确描述数据的变化趋势及幅度。

In order to realize the prediction of downhole conditions of shale gas fracturing,prevent and control the abnormal conditions in time,a method of building optimized LWLR algorithm based on PF and ARMA model can worked out. The method uses the ARMA model and PF to build a PFARMA model and the PF ARMA model can be used to predict pressure,and the prediction results can be used as the optimization basis for the LWLR model. Finally,LWLR model of optimal pressure parameter was obtained.And a comparison was made between the prediction result by optimized LWLR model and that by the traditional model. The model was used to analyze a shale gas fracturing operation curve. The result shows that the prediction accuracy by the optimized LWLR model is higher than that by any traditional model,and that the change trend and amplitude of the data could be described more accurately by the optimized LWLR model than any traditional model.