Tailored Solutions, Built To Accelerate Your Most Valuable Workflows

We enable a new generation of workflows. Through expert efficiency, results consistency, and fundamental understanding, business performance thrives. 

Why Enthought

Through collaborative engagement and deep industry experience, Enthought builds tailored, results-driven solutions in the language of energy. Together, we deliver faster ways to reach critical insights and decisions via data and analytics.

Enthought brings together scientific understanding with computational excellence. With our cross functional expertise, we can solve problems more effectively and holistically to achieve better business outcomes.  


Energy Expertise


Unlocking the potential of deep learning in seismic interpretation starts with data – the ability to categorize, model and manage it quickly and consistently. Further, geophysicists need to be highly efficient at producing labeled data to train deep learning models to deliver high quality interpretations in a fraction of the time it typically takes. Enthought’s Subsurface AI Seismic brings this to life. With as few as three interpreted seismic lines, the pattern recognition-based deep learning models enable you to create robust predictions across the entire seismic volume.


Frac operations are rich with potentially valuable data. However, integrating it and using it to improve performance can be challenging. Enthought scientists’ oilfield domain expertise, analytical software development and infrastructure skills, enable collaboration to identify the opportunities and develop the right software technologies.

Core and Thin Sections

The full capabilities of today’s machine learning are now available for thin section image interpretation. With the VirtualCore custom deep learning application, geoscientists can easily label, annotate, and interpret a small subset of the thin section data. Customized artificial intelligence and machine learning models learn from the geoscientist, providing suggestions and analyzing the entire image.

Cheat Sheet | Large Language Models+ For Scientific Research

Large Language Models+ For Scientific Research Updated August 2023 LLMs and Tools for R&D To help scientists and researchers navigate the increasing number of advanced…

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WEBINAR: What Every R&D Leader Needs to Know About ChatGPT and LLMs

View Webinar-on-Demand Live webinar held on June 27, 2023 Overview ChatGPT and the explosion of advanced Large Language Models (LLMs) are disrupting every industry. We…

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5 Tips to Kickstart Your Journey to the Future-Proofed R&D Lab

5 Tips to Kickstart Your Journey to the Future-Proofed R&D Lab   Despite an increase in digital transformation efforts across all industries, 70% fall short…

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10X Efficiency Gain and Improved Classification Using Deep Learning

Use AI techniques to efficiently extract mineralogy and grain size statistics from thin sections Thin sections provide the closest examination of in situ rock properties,…

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Integrating Weather and Renewable Energy Sources Data for 2.5 Million Viewers

Educating and developing a culture of responsible energy consumption The EnergizAIR project is presented both to show the innovative technology and methodology to solve the…

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Manual Processes for CSEM Replaced by Computational Tools and Strategies

Shell needed a way to effectively visualize a new scientific measurement Controlled-Source Electromagnetic sounding (CSEM) is a new tool for marine oil exploration. Sensitive electric…

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Predicting Pore Pressure From Very Limited Data Sets

ConocoPhilips wanted to make pore pressure predictions with limited data sets Logs from two wells and a limited set of 2D and 3D seismic were…

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AI/Machine Learning Techniques Quantify AVA Seismic Analysis Uncertainty

Help ConocoPhillips apply advanced AI techniques to Amplitude Versus Angle (AVA), seismic analysis Amplitude Versus Angle (AVA), seismic analysis is a well-established oil exploration tool….

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Model-Based Approach to Rock Physics Improves Predictions

ConocoPhillips wanted a more flexible way to model rock physics An important activity in characterizing reservoirs  is constructing models of rock physics, which represent how…

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Applying Machine Learning to Subsurface Data Reduces Uncertainty

Overcome slow and tedious nature of core analysis Core analysis provides ground truth data of rock properties, and can be time consuming and expensive to…

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Case Study: 10X Efficiency Gain and Improved Classification Using Deep Learning

Modern microscopes generate high-resolution images of thin sections at multiple polarization angles. These gigapixel images provide a massive dataset of texture and color features that can be used to extract mineralogy and grain size statistics— but the size of this data makes it impossible to assimilate manually.

The Enthought team was challenged to create an AI-based assistant that gives petrophysicists the ability to quickly visualize and analyze hundreds of thin section images. Here's how we did it.

Behzad Eftekhari

Consulting Manager

Robert Kern

Principal Engineer, Algorithms and Machine Learning

Mehran Mehrabi

Scientific Software Developer

Rafael Pinto

Scientific Software Developer

Siddhant Wahal

Senior Scientific Software Developer