Seismic Facies Recognition of Ultra-Deep Carbonate Rocks Based on Convolutional Neural Network

Abstract

Seismic facies is a seismic reflection unit defined by specific seismic reflection characteristics, that is, the seismic responses of sedimentary facies or geological bodies, whose accuracy will directly affect the reliability of oil and gas exploration results. Currently, seismic facies is generally recognized depending upon the differences between certain single trace seismic attributes (waveform, frequency spectrum, and amplitude, etc.) and adjacent units to conduct cluster analysis. Such methods, however, have ambiguity in identifying special reflective structures with continuous waveforms (e.g. massive carbonate deposits). In order to solve this problem, this paper incorporates artificial intelligence (AI) technology into automatic recognition of seismic facies with special reflection structures. Firstly, a 2D seismic facies classification sample label set is designed and formed. Then, a seismic facies prediction model is designed and constructed using a multi-layer convolutional neural network (CNN). Finally, the trained model is used to automatically track the seismic facies in the study area. This method was applied to seismic facies recognition for the Sinian Dengying Formation in an area of the Sichuan Basin, and the seismic facies recognized were compared with artificially interpreted ones. It is confirmed that the proposed method provides a better effect than artificial interpretation, with greatly improved accuracy and efficiency.