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Data-driven early warning model for screenout scenarios in shale gas fracturing operation
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2020-09-30 , DOI: 10.1016/j.compchemeng.2020.107116
Jinqiu Hu , Faisal Khan , Laibin Zhang , Siyun Tian

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.



中文翻译:

页岩气压裂作业筛选情景的数据驱动预警模型

在页岩气压裂作业中,支撑剂的筛分通常被认为是危险的作业问题。它会影响水力压裂水平井完井的性能,并可能导致井下事故。提出了一种基于多步前向预测的数据驱动的筛选场景预警方法。当前工作的两个主要贡献是:通过局部加权线性回归(LWLR)方法开发了压裂压力预测模型,该模型通过结合粒子滤波(PF)算法和自回归移动的集成PF-ARMA模型对参数进行了优化。平均(ARMA)模型;提出了一种精细的压裂筛分事件预警方案,以供实际应用。

更新日期:2020-10-17
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