Geophysical Insights B2B Case Studies & Customer Successes

Geophysical Insights was founded in 2008 in Houston, Texas, by Dr. Tom Smith with the vision of applying machine learning to seismic interpretation to reduce the risk of oil and gas exploration and the cost of field development. Shortly after launching the company, Dr. Smith assembled a team of geoscientists and interpretation software specialist to develop the next generation of seismic interpretation software. The group’s mission was to apply machine learning to the problem of seismic interpretation and deliver the new capability in an off-the-shelf, commercial software product that could be used by any geoscientist.

Case Studies

Showing 14 Geophysical Insights Customer Success Stories

Applying Unsupervised Multi-attribute Machine Learning for 3D Stratigraphic Facies Classification in a Carbonate Field, Offshore Brazil

Petrobras

Stratigraphic and Structural Resolution using Instantaneous Attributes on Spectral Decomp Sub-bands, Buda and Austin Chalk formations

Seitel, Inc.

Using Self-Organizing-Maps to Explore the Yegua in the Texas Gulf Coast

Texas Gulf Coast

An Integrated Machine Learning-based Fault Classification Workflow

The University of Oklahoma

Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

URTeC

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

URTeC

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Geophysical Insights B2B Case Studies & Customer Successes

Case Studies

First Steps in the Sub-seismic Resolution of the Eagle Ford

Detailed Sub-seismic Resolution in the Eagle Ford Shale and Identification of Under-explored Geobodies

Resolution of Faults in the Eagle Ford

Attribute Analysis in Unconventional Resource Plays Using Unsupervised Neural Networks

Visualization and Characterization of Paleozoic (Ordovician – Devonian) Tight Carbonate Reservoirs, Oklahoma – Part I

Visualization and Characterization of Paleozoic (Ordovician – Devonian) Tight Carbonate Reservoirs, Oklahoma – Part II

Using Self-Organizing-Maps to Define Seismic Facies

Using Self-Organizing-Maps to Expose DHIs

Applying Unsupervised Multi-attribute Machine Learning for 3D Stratigraphic Facies Classification in a Carbonate Field, Offshore Brazil

Stratigraphic and Structural Resolution using Instantaneous Attributes on Spectral Decomp Sub-bands, Buda and Austin Chalk formations

Using Self-Organizing-Maps to Explore the Yegua in the Texas Gulf Coast

An Integrated Machine Learning-based Fault Classification Workflow

Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

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