First Break, Volume 42, Issue 2, Feb 2024, p. 49 - 56
Summary We describe a series of experiments designed to improve fault likelihood predictions of a pre-trained machine learning model on an unseen dataset with real fault interpretations. The goal is to establish a best practice workflow for tuning fault prediction models. The model is a 3D model that is tuned by training with a Masked Dice loss function on 2D interpretations....
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....