Detection of potential gas accumulations in 2D seismic images using spatio-temporal, PSO, and convolutional LSTM approaches. (1st April 2023) - Júnior, Domingos Alves Dias; Batista da Cruz, Luana; Otávio Bandeira Diniz, João; Silva, Aristófanes Corrêa; Cardoso de Paiva, Anselmo; Gattass, Marcelo; Rodriguez, Carlos; Quispe, Roberto; Ribeiro, Roberto; Riguete, Vinicius
Highlights: A new computational method is proposed for detection of potential gas accumulations. Proposed method uses the dataset composed of 380 sections of 2D seismic images. The method uses spatio-temporal, PSO, and convolutional LSTM approaches. The method achieved an F1-score of 84.22% and a sensitivity of 98.06%. Abstract: Seismic reflection is one of the most widely used geophysical methods in the oil and gas (O&G) industry for hydrocarbon prospecting. In particular, for some Brazilian onshore fields, this method has been used to estimate the location and volume of gas accumulations. However, the analysis and interpretation of seismic data are time-consuming due to the large amount of information and noisy nature of the acquisitions. To help geoscientists with these tasks, computational tools based on machine learning have been proposed considering direct hydrocarbon indicators. In this study, we present a methodology for detecting gas accumulation based on the convolutional long short-term memory model and particle swarm optimization scheme. In the best scenario, the proposed method achieved an F1-score of 84.22%, sensitivity of 98.06%, specificity of 99.44%, and accuracy of 99.42%. We present tests performed on the Parnaíba Basin, indicating that the proposed method is promising for gas exploration.