Extraction of hydrocarbon microseepage information from Sentinel-2 remote sensing image using machine learning:A case of Marsel region
Hydrocarbon microseepage is a common phenomenon occurring in oil and gas field.Remote sensing technology provides an efficient and fast method for surface detection of hydrocarbon microseepage.The traditional mechanism-based methods detect the surface anomalies(vegetation,altered minerals,etc.)caused by hydrocarbon microseepage and extract the related information.The method is simple and easy to operate,but the result is some of ambiguity.This paper takes the Marsel area in Kazakhstan as a case,and proposes an machine learning-based method to extract hydrocarbon microseepage from remote sensing images.Firstly,the training samples were made based on the result of surface microorganism detection in the study area.In order to compare the learning results of different samples sets,two training sample datesets,i.e.the patch and pixel dataset were built.Subsequently,logistic regression,support vector machine,random forest,LeNet,AlexNet,GoogLeNet and ResNet algorithms were used to construct learning models for the above two kinds of datasets.The results show that the classical machine learning algorithm has the highest accuracy of 0.833 for patch samples and 0.771 for pixel samples;the deep learning algorithm has the highest accuracy of 0.782 for patch samples and 0.914 for pixel samples.Finally,the four algorithmic models with the highest accuracy were applied to the Marsel area in Kazakhstan and compared with the geological seismic data.It was found that the prediction results of ResNet-18-1D for the pixel samples corresponded best to the seismic geological analysis data with accuracy of 0.914 and Cohen's kappa coefficient of 0.892.
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