Publications

84th EAGE Annual Conference & Exhibition Summary This research introduces a new approach, Image-to-image translation (I2I) using GAN as the foundation to solve the problem of missing traces in seismic data caused by shallow gas or other factors. I2I uses a zero-sum game between a generator model and a discriminator model. The workflow begins with conditioning and preparation of input and output datasets. The data is then normalized and used to train the model for 1500 epochs or 25 minutes using a GPU....

Asia Petroleum Geoscience Conference and Exhibition (APGCE) Summary Understanding the distribution of facies in a reservoir is important for reservoir characterization and can be used to aid in identifying the locations of hydrocarbons. However, due to the complexity of the earth’s subsurface, predicting the facies distribution information is often a very challenging task. Recently, the growth of computing power has enabled the application of Machine Learning models in service of studying many geoscience challenges, including the classification of facies using seismic data....

Applied Science MDPI Abstract A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the complexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning....

Journal Geofisika Abstract Artificial Intelligence method has been widely used recently in many aspects to understand big data. Fundamentally, the purpose of Artificial Intelligence is to solve nonlinear problem. Most methods are trying to optimize an output from one or many inputs parameter by identifying any potential patterns that fit or using a statistical data. In Oil & Gas industry, one of the main challenges that can be solved by Artificial Intelligence is estimating facies from well log or seismic data....

The 13th SEGJ International Symposium, Tokyo, Japan, 12-14 November 2018 Abstract Machine learning is getting more popularity nowadays since it has potential to improve the quality of our life in many fields through data learning and optimization. In a short explanation, machine learning can be seen as non-linear equation optimization that try to predict one output from several input parameters although it may not have any logical relationships. In the world of geoscience work, one challenging problem is lithology estimation from several logs input and/or seismic data....