Direct hydrocarbon indicators based on long short-term memory neural network
Luiz Fernando Santos, Reinaldo Mozart Gama E Silva, Marcelo Gattass, Aristófanes Corrêa Silva · 2019
Direct hydrocarbon indicators (DHIs) are a seismic anomaly that may indicate a possible reservoir. However, the task of finding a DHI anomaly in the seismic data is arduous considering that in most of the cases, this indicator is hard to sight in a large seismic data. Deep neural networks algorithms have been solving similar human-intensive and time-consuming problems with increasing speed and accuracy. In this paper, we propose a novel method to detect possible DHIs on seismic data applying a Long Short-Term Memory (LSTM) neural network on the seismic trace scale. To test our proposal, we use the Netherlands Offshore F3-Block, a known open 3D seismic data with a significant amount of gas. The results show that the proposed DHIs achieving a 97% accuracy, 95% sensitivity, 97% specificity, and 99% AUC. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 8:30 AM Presentation Time: 8:55 AM Location: 221D Presentation Type: Oral