Abnormal pattern recognition and early warning of water flooding in fractured-vuggy reservoir based on LSTM

Abstract

The existence of large fractures and caves and frequent working system adjustments have resulted in the diverse characteristics of water cut in the fractured-vuggy reservoir, which makes it difficult to early warn the water flooding.Aiming at the problem of time delay of traditional early warning methods, this paper uses K-line theory to describe the change trend of water cut production indicators, and summarizes the pre-flooding abnormal patterns such as abundant type, breakthrough type and reversal type.Since the recurrent neural network can memorize the long-term correlation between production data, LSTM is used to automatically identify the features of abnormal pattern to realize early warning of water flooding.The experimental results show that by transforming the data scale, the proposed abnormal pattern recognition model based on LSTM can successfully extract the overall trend of data before water flooding.The recognition accuracy of the proposed model is significantly higher than that of support vector machine and naïve Bayes and other models.Various kinds of abnormal patterns described by K-line can effectively solve the traditional problem of prediction delay.The proposed model realizes early warning of water flooding one to three weeks in advance, and provides new ideas for early warning of water flooding.

Keywords

• fractured-vuggy reservoir
• early warning of water flooding
• k-line theory
• abnormal pattern
• lstm