Defesa de Tese de Doutorado do aluno Luis Fernando Marin Sepulveda.
Título da Tese: GeneralizationoftheDeep Learning Model for Natural Gas Indication in 2D Seismic Image Based on the Training Dataset and the Operational Hyper Parameters Recommendation
Resumo: Interpreting seismic images is an essential task in diverse fields of geosciences, and it’s 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 an increasing number of Deep Learning, DL, methods to detect horizons, faults, and potential hydrocarbon reservoirs, nevertheless, the models to detect gas reservoirs present generalization performance difficulties, i.e., performance is compromised when used in seismic images from new exploration campaigns. This problem is especially true for 2D land surveys where the acquisition process varies, and the images are very noisy. This work presents three methods to improve the generalization performance of DL models of natural gas indication in 2D seismic images, for this task, approaches that come from Machine Learning, ML, and DL are used. The research focuses on data analysis to recognize patterns within the seismic images to enable the selection of training sets for the gas inference model based on patterns in the target images. This approach allows a better generalization of performance without altering the architecture of the gas inference DL model or transforming the original seismic traces. The experiments were carried out using the database of different exploitation fields located in the Parnaíba basin, in northeastern Brazil. The results show an increase of up to 39\% in the correct indication of natural gas according to the recall metric. This improvement varies in each field and depends on the proposed method used and the existence of representative patterns within the training set of seismic images. These results conclude with an improvement in the generalization performance of the DL gas inference model that varies up to 21\% according to the F1 score and up to 15\% according to the IoU metric. These results demonstrate that it is possible to find patterns within the seismic images using an unsupervised approach, and these can be used to recommend the DL training set according to the pattern in the target seismic image; Furthermore, it demonstrates that the training set directly affects the generalization performance of the DL model for seismic images.
Orientador: Prof. Dr. Marcelo Gattass
Co-orientador: Prof. Dr. Aristófanes Corrêa Silva
Banca: Prof. Dr. Raul Queiroz Feitosa | Prof. Dr. Jan Jose Hurtado Jauregui | Prof. Dr. Kelson Romulo Teixeira Aires | Prof. Dr. António Manuel Trigueiros da Silva Cunha
Assista a defesa pelo link: https://puc-rio.zoom.us/j/93188594035?pwd=NWhtamxYNGZvamZNdmQ4V0wrNVBOZz09