Data-driven early warning model for screenout scenarios in shale gas fracturing operation

Abstract

In shale gas fracturing operation, proppant screenout is generally recognized as a hazardous operational issue. It affects the performance of hydraulic fracturing horizontal well completion and may lead to downhole accidents. This paper proposes a data-driven early warning method for screenout scenarios based on multi-step forward prediction. Two key contribution of the present work are: development of a prediction model for fracturing pressure by Locally Weighted Linear Regression (LWLR) approach, which parameters are optimised by the integrated PF-ARMA model combining the particle filter (PF) algorithm and the autoregressive moving average (ARMA) model together; proposing a delicate early warning scheme of fracturing screenout event(s) for practical application in the field. The proposed method is tested and fully validated to predict screenout events with satisfying results, which helps to extend the response time for screenout treatment and ensure the long-term safety and integrity of shale gas fracturing operation.