An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System

Abstract

In recent years, data mining technology has been widely used in different fields. For petroleum industry, the application of data mining method not only can analyze the data faster and more accurately but also can reduce the defects such as misjudgment and missed judgment caused by relying on artificial monitoring. Fracturing is one of the key techniques for increasing petroleum production. Due to the contingency of the abnormal variation of the fracturing construction curve, there is also a large lag and contingency by using the construction curve for sand block warning. Frequent false alarms and delayed alarms often occur. Therefore, the paper proposes an early-warning method of the sand plug of fracturing based on data mining. First, an early warning model of double logarithmic curve sand plug of fracturing is established, and the time series analysis algorithm is used to predict the oil pressure and casing pressure in the double logarithmic curve sand plug of fracturing risk warning model, so the early warning accuracy is improved. And then the general regression neural network (GRNN) algorithm is designed to optimize the prediction results of the time domain analysis. The improved affinity propagation (AP) clustering algorithm is used to cluster the monitoring data to help to improve the accuracy of the subsequent slope calculation, so as to improve the coincidence rate of the fracturing and sand plug risk. Finally, the risk warning model is applied and analyzed on site, and the validity and accuracy of the model are verified. The model is embedded in the remote monitoring system to realize online remote intelligent monitoring of risks by urban office workers. INDEX TERMS AP clustering, data mining, early warning, sand plug of fracturing, GRNN algorithm.

A State of Art Survey for Intelligent Energy Monitoring Systems

“…In the remote system implementation, the proposed early warning model allows city officials to control on a remote basis. [51]They offer smart, heterogeneous PV panel systems with the perfect precision of forecasts. They also addressed hardware design and monitoring systems implementation for lowcost microcontrollers.…”

“…In the same year, Liang H. et al [51] proposed early-alert data mining-based sand plug fracturing plugs. First, an early warning model is developed for double logarithm curve fracturing sand plugs, and algorithms for time analysis have been used to predict oils and cause pressure in the two logarithmic curve sand plugs of fracture risk advisory models, thus improving early warning accuracy.…”

Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks

“…(H. Liang, 2019) [6] Data mining sand plug fracturing was proposed in this research as an early warning tool. Time series analysis and a precocious alerting model of a double logarithm sand plug are established, and a fracturing hazard warning model's early warning accuracy is improved by using the algorithm of time series analysis.…”

Research on routing optimization of WSNs based on improved LEACH protocol

“…With the advancement of technology, WSNs are widely applicate in society and playing a vital role such as environment monitoring [1], weather forecasting [2], precision agriculture [3], petroleum drilling [4], natural disaster prevention [5], urban transportation [6], diagnose wall collapse [7], and indoor positioning [8]. The outperformance of WSNs has many practical applications because of its low cost, low power, high integration, and high sensitivity [9].…”