Dr. Tom Smith, President and CEO of Geophysical Insights, shares his predictions on the evolution of digital transformation in oil and gas with the AAPG Explorer.
Machine learning is enabling a new world of seismic interpretation as oil and gas activity pivots toward a new phase of unconventional reservoir development.
Distributed acoustic sensors (DAS) record continuous seismic data very cheaply, taking another quantum step in the amount of data coming from the reservoir during exploration, development and production.
Seismic attributes help identify numerous geologic features in conventional seismic data. Applying principal component analysis can help interpreters identify seismic attributes that show the most variance in the data for a given geologic setting, and help them determine which attributes to use in a multi-attribute analysis using self-organizing maps.
Dr. Thomas A. Smith founded Seismic Micro-Technology (SMT) in 1984 and led the development of the widely adopted Kingdom Suite software suite for seismic interpretation. He went on to found Geophysical Insights in 2008, which launched Paradise™ at SEG 2013.
Utilizing machine learning via Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) interpretation techniques to help identify sweet spots.
Geophysical Insights’ Paradise™ analytic software platform applies automated pattern recognition to analyze seismic attributes in almost infinite combinations to delineate anomalies in unconventional plays. The workflows use unsupervised neural networks and principal component analysis while taking advantage of high-power multi-core processing using large-scale parallelism to accelerate performance.
Risks and rewards are evenly poised in the hydrocarbons industry, and along with oil and gas companies, probably no one understands the nuances better than Tom Smith, founder and president of Geophysical Insights.
New technology for the Big Data problem of the seismic volume using Unsupervised Neural Networks for the interpretation of Greenfield projects in a modular, affordable platform.
Process identifies anomalies from original data without bias using Unsupervised Neural Networks in Greenfield Exploration.