摘要：Accurate, quick, reliable event detection is essential to Intelligent hydraulic fracturing. The real-time detection of events mostly relies on the expert knowledge. Although the machine learning algorithms had been used for events detection, the accuracy is still not stable that depending on the data quality and amount. This paper proposed an alternatively threshold-based algorithms with special sliding windows by integrating feature mining with expert knowledge from time-series data. The 378 stages of historical fracturing data were collected. More than 5 experts were organized to divide and label the six types of fracturing events involving wellhead pressure testing, plug ball seating, pad stage, proppant laden stage, flushing, and fracture closure stage from second-point data. The correlation between fracturing events and data features was built by using 10-seconds sliding window and forward difference to extract the slope features of pumping curves and their corresponding threshold values of pumping pressure, rate, and proppant concentration. Furthermore, the wavelet analysis was used to identify the fracture closure time from wellhead pressure after the fracturing is completed. Finally, the threshold-based algorithms by integrating feature mining with expert knowledge from time-series data were proposed to handle the fast detection of fracturing events. The model was tested on 200 stages of data and performed a high accuracy in fracturing events detection. Particularly, the accuracy of fracturing events detection ranges from 92.7% to 99.6%. The accuracy of Fracture Closure Pressure were 93.8%. Copyright 2025, International Petroleum Technology Conference.

ISSN号：9781959025436

卷、期、页：/

发表日期：2025-01-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：International Petroleum Technology Conference, IPTC 2025

参与作者：何建

通讯作者：张帅,孟磊峰,柏侃侃

第一作者：盛茂,田守嶒

论文类型：会议论文

论文概要：张帅,盛茂,孟磊峰,柏侃侃,何建,田守嶒，Automatic Detection of Hydraulic Fracturing Events by Real-Time Data Mining，International Petroleum Technology Conference, IPTC 2025，2025，/

论文题目：Automatic Detection of Hydraulic Fracturing Events by Real-Time Data Mining