CO2 Gas Layer Recognition Method Based on Long Short-Term Memory Network
CO2 monitoring is a crucial part of oil and gas extraction, and traditional methods for monitoring CO2 face many challenges. With the gradual rise of artificial intelligence, deep learning technology is widely used in geophysical logging. Due to the development of deep CO2 gas layer in Enping sag, the Pearl River Mouth basin, traditional logging methods cannot accurately evaluate reservoir fluids. CO2 gas layer recognition model based on Long Short-Term Memory Network (LSTM) is constructed, and CO2 sensitive logging parameters are optimized through m × 2 regularized cross validation to train the model. This model is used to identify the CO2 gas layer of well L2 in Enping sag, the Pearl River Mouth basin, and is compared with the recognition results of support vector machine and K nearest neighbor algorithm. The results show that the three deep learning algorithms have good recognition effects on CO2 gas layer, among which the LSTM algorithm has the best identification effect on CO2 gas layer, with an accuracy of 93.4％, providing new ideas for deep CO2 gas layer recognition work.
CO2 gas layer recognitionLong Short-Term Memory Network (LSTM)deep learningPearl River Mouth basin
万方数据
