Seismic data classification for natural gas detection using training dataset recommendation and deep learning

Abstract

Interpretation of seismic data is an essential task in diverse fields of geosciences, and it is a widely used method in hydrocarbon exploration. However, its interpretation requires a significant investment of resources, and obtaining a satisfactory result is not always possible. The literature shows increasing Deep Learning (DL) methods to detect horizons, faults, and potential hydrocarbon reservoirs. However, the models to detect gas reservoirs present generalization difficulties, i.e., the performance of the methods developed based on DL is compromised when used on seismic data coming from new exploration campaigns. This implies that the new seismic data has features that differ from those that the DL model learned to identify based on the training data. The generalization problem is especially true for 2D land surveys where the acquisition process varies, and the data is very noisy. This work proposes a Domain Adaptation method for natural gas detection in 2D seismic data based on comparing seismic features. Whose aim is to allow a better performance of the DL model, when it is used in data that comes from several exploration campaigns, and which are carried out by several teams, in different areas and dates. The proposed method does not require the modification of the training seismic data or the DL model architecture, it focuses on the analysis of the training data to recognize patterns that allow comparing and clustering the seismic data that are similar. Consequently, the resulting clusters contain representative seismic data for specific domains. This work uses seismic data from nine exploration fields of the Paleozoic Basin of Parnaíba in Brazil as the object of study. The proposed method has two different variants both use an LSTM-based DL model to perform gas inference on 2D seismic data., the proposed method results are compared with those obtained using the same DL model, but being trained with all available seismic data. The first variant, called “Cluster Training per Field”, requires only 23% of the original training data and can improve by 3% in Precision, 4% in Recall, 2% in F1, and 2% in Intersection Over Union (IoU). The second variant, called the “Sample Training Cluster Recommendation Method”, achieved a 4% improvement in Precision, 10% in the recall, 7% in F1, and 8% in IoU.