English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Carbonate reservoirs became the center of attention for the Brazilian offshore oil industry after the oil discoveries in the Santos Pre-salt layer, motivating efforts to development of new methods to characterization of these rocks. Artificial neural network structures were developed based on the human brain. The processing units, called artificial neurons, are simplified models of biological neurons. In this same sense, convolutional neural networks were elaborated to serve as the computer vision. Despite the potential of this technology, the convolutional networks have not been successfully adapted to work with seismic data. The main goal of this work is to develop a new methodology, focusing in data preparation; conditioning and attribute generation, to perform facies classification through convolutional neural networks technology in 3D seismic data volume, concentrating in characterize oil reservoir zones in Brazilian Pre-salt interval. Using this new methodology, the 2-Facies model was generated, classifying oil reservoir zones from Pre-salt seismic data providing a valuable information about lateral distribution of these reservoirs and allowing to identify new targets in the area of interest.

Facies Classification in 3D Seismic Data Volume of the Brazilian Pre-salt through Convolutional Neural Networks Technology