An Identification Method for Intelligent Prediction of Drilling Overflow Based on BiLSTM-BP Neural Network

Abstract

The prevention and control of overflow is an important task to ensure the safe and efficient development of oil and gas fields, and the traditional overflow monitoring methods have the defects of insufficient real-time and low reliability. This paper proposes an early overflow intelligent identification method based on BiLSTM-BP neural network to address the problems of insufficient real-time and low reliability of traditional monitoring means; the lack of downhole drilling measurement data affects the prediction accuracy of multi-source data fusion. Firstly, the input parameters of the model are optimized through correlation analysis; secondly, the hidden features and complex change laws in the overflow monitoring data are learned by using bi-directional long and short-term memory network to realize the repair of downhole missing data; finally, the BP neural network realizes the early overflow intelligent identification. The test set data and field test results of a test well in Bozhong oilfield show that the early overflow intelligent identification method based on BiLSTM-BP neural network constructed in this paper can realize the intelligent identification of early overflow using multiple monitoring parameters of surface integrated logging, high-precision flowmeter at the wellhead and downhole drilling, and the identification accuracy reaches 97.02%, which is 1.23% better than the existing model and It can identify the overflow more quickly while ensuring higher accuracy. It provides a key theoretical model for early overflow identification technology from traditional identification to intelligent identification.