Category Archives: Geoscience

Enthought at the 2017 Society of Exploration Geophysicists (SEG) Conference

2017 will be Enthought’s 11th year at the SEG (Society of Exploration Geophysicists) Annual Meeting, and we couldn’t be more excited to be at the leading edge of the digital transformation in oil & gas being driven by the capabilities provided by machine learning and artificial intelligence.

Now in its 87th year, the Annual SEG (Society of Exploration Geophysicists) Meeting will be held in Houston, Texas on September 24-27, 2017 at the George R. Brown Convention Center. The SEG Annual Meeting will be the first large conference to take place in Houston since Hurricane Harvey and its devastating floods, and we’re so pleased to be a small part of getting Houston back “open for business.”

Pre-Event Kickoff: The Machine Learning Geophysics Hackathon

We had such a great experience at the EAGE Subsurface Hackathon in Paris in June that when we heard our friends at Agile Geoscience were planning a machine learning in geophysics hackathon for the US, we had to join! Brendon Hall, Enthought’s Energy Solutions Group Director will be there as a participant and coach and Enthought CEO Eric Jones will be on the judging panel.

Come Meet Us on the SEG Expo Floor & Learn About Our AI-Enabled Solutions for Oil & Gas

Presentations in Enthought Booth #318 (just to the left from the main entrance before the main aisle):

  • Monday, Sept 25, 12-12:45 PM: Lessons Learned From the Front Line: Moving AI From Research to Application
  • Tues, Sept 26, 1-1:45 PM: Canopy Geoscience: Building Innovative, AI-Enabled Geoscience Applications
  • Wed, Sept 27, 12-12:45 PM: Applying Artificial Intelligence to CT, Photo, and Well Log Analysis with Virtual Core

Hart Energy’s E&P Magazine Features Canopy Geoscience

Canopy Geoscience, Enthought’s cross-domain AI platform for oil & gas, was featured in the September 2017 edition of E&P magazine. See the coverage in the online SEG Technology Showcase, in the September print edition, or in the online E&P Flipbook.


Enthought's Canopy Geoscience featured in E&P's September 2017 edition

Webinar- Get More From Your Core: Applying Artificial Intelligence to CT, Photo, and Well Log Analysis with Virtual Core

What: Presentation, demo, and Q&A with Brendon Hall, Geoscience Product Manager, Enthought

Who should watch this webinar:

  • Oil and gas industry professionals who are looking for ways to extract more value from expensive science wells
  • Those interested in learning how artificial intelligence and machine learning techniques can be applied to core analysis

VIEW 


Geoscientists and petroleum engineers rely on accurate core measurements to characterize reservoirs, develop drilling plans and de-risk play assessments. Whole-core CT scans are now routinely performed on extracted well cores, however the data produced from these scans are difficult to visualize and integrate with other measurements.

Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.

In this webinar and demo, we’ll start by introducing the Clear Core processing pipeline, which automatically removes unwanted artifacts (such as tubing) from the CT image. We’ll then show how the machine learning capabilities in Virtual Core can be used to describe the core, extracting features such as bedding planes and dip angle. Finally, we’ll show how the data can be viewed and analyzed alongside other core data, such as photographs, wellbore images, well logs, plug measurements, and more.

What You’ll Learn:

  • How core CT data, photographs, well logs, borehole images, and more can be integrated into a digital core workshop
  • How digital core data can shorten core description timelines and deliver business results faster
  • How new features can be extracted from digital core data using artificial intelligence
  • Novel workflows that leverage these features, such as identifying parasequences and strategies for determining net pay

VIEW 

Presenter:

Brendon Hall, Geoscience Applications Engineer, Enthought Brendon Hall, Enthought
Geoscience Product Manager and Application Engineer

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Scientists Use Enthought’s Virtual Core Software to Study Asteroid Impact

Chicxulub Impact Crater Expedition Recovers Core to Further Discovery on the Impact on Life and the Historical Dinosaur Extinction

From April to May 2016, a team of international scientists drilled into the site of an asteroid impact, known as the Chicxulub Impact Crater, which occurred 66 million years ago. The crater is buried several hundred meters below the surface in the Yucatán region of Mexico. Until that time, dinosaurs and marine reptiles dominated the world, but the series of catastrophic events that followed the impact caused the extinction of all large animals, leading to the rise of mammals and evolution of mankind. This joint expedition, organized by the International Ocean Discovery Program (IODP) and International Continental Scientific Drilling Program (ICDP) recovered a nearly complete set of rock cores from 506 to 1335 meters below the modern day seafloor.  These cores are now being studied in detail by an international team of scientists to understand the effects of the impact on life and as a case study of how impacts affect planets.

CT Scans of Cores Provide Deeper Insight Into Core Description and Analysis

Before being shipped to Germany (where the onshore science party took place from September to October 2016), the cores were sent to Houston, TX for CT scanning and imaging. The scanning was done at Weatherford Labs, who performed a high resolution dual energy scan on the entire core.  Dual energy scanning utilizes x-rays at two different energy levels. This provides the information necessary to calculate the bulk density and effective atomic numbers of the core. Enthought processed the raw CT data, and provided cleaned CT data along with density and effective atomic number images.  The expedition scientists were able to use these images to assist with core description and analysis.

CT Scans of Chicxulub Crater Core Samples

Digital images of the CT scans of the recovered core are displayed side by side with the physical cores for analysis

chicxulub-virtual-core-scan-core-detail

Information not evident in physical observation (bottom, core photograph) can be observed in CT scans (top)

These images are helping scientists understand the processes that occurred during the impact, how the rock was damaged, and how the properties of the rock were affected.  From analysis of images, well log data and laboratory tests it appears that the impact had a permanent effect on rock properties such as density, and the shattered granite in the core is yielding new insights into the mechanics of large impacts.

Virtual Core Provides Co-Visualization of CT Data with Well Log Data, Borehole Images, and Line Scan Photographs for Detailed Interrogation

Enthought’s Virtual Core software was used by the expedition scientists to integrate the CT data along with well log data, borehole images and line scan photographs.  This gave the scientists access to high resolution 2D and 3D images of the core, and allowed them to quickly interrogate regions in more detail when questions arose. Virtual Core also provides machine learning feature detection intelligence and visualization capabilities for detailed insight into the composition and structure of the core, which has proved to be a valuable tool both during the onshore science party and ongoing studies of the Chicxulub core.

chicxulub-virtual-core-digital-co-visualization

Enthought’s Virtual Core software was used by the expedition scientists to visualize the CT data alongside well log data, borehole images and line scan photographs.

Related Articles

Drilling to Doomsday
Discover Magazine, October 27, 2016

Chicxulub ‘dinosaur crater’ investigation begins in earnest
BBC News, October 11, 2016

How CT scans help Chicxulub Crater scientists
Integrated Ocean Drilling Program (IODP) Chicxulub Impact Crater Expedition Blog, October 3, 2016

Chicxulub ‘dinosaur’ crater drill project declared a success
BBC Science, May 25, 2016

Scientists hit pay dirt in drilling of dinosaur-killing impact crater
Science Magazine, May 3, 2016

Scientists gear up to drill into ‘ground zero’ of the impact that killed the dinosaurs
Science Magazine, March 3, 2016

Texas scientists probe crater they think led to dinosaur doomsday
Austin American-Statesman, June 2, 2016

Geophysical Tutorial: Facies Classification using Machine Learning and Python

Published in the October 2016 edition of The Leading Edge magazine by the Society of Exploration Geophysicists. Read the full article here.

By Brendon Hall, Enthought Geosciences Applications Engineer 
Coordinated by Matt Hall, Agile Geoscience

ABSTRACT

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist’s toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

One of the best examples is scikit-learn, a collection of tools for machine learning in Python. What is machine learning? You can think of it as a set of data-analysis methods that includes classification, clustering, and regression. These algorithms can be used to discover features and trends within the data without being explicitly programmed, in essence learning from the data itself.

Well logs and facies classification results from a single well.

Well logs and facies classification results from a single well.

In this tutorial, we will demonstrate how to use a classification algorithm known as a support vector machine to identify lithofacies based on well-log measurements. A support vector machine (or SVM) is a type of supervised-learning algorithm, which needs to be supplied with training data to learn the relationships between the measurements (or features) and the classes to be assigned. In our case, the features will be well-log data from nine gas wells. These wells have already had lithofacies classes assigned based on core descriptions. Once we have trained a classifier, we will use it to assign facies to wells that have not been described.

See the tutorial in The Leading Edge here.

ADDITIONAL RESOURCES:

AAPG 2016 Conference Technical Presentation: Unlocking Whole Core CT Data for Advanced Description and Analysis

Microscale Imaging for Unconventional Plays Track Technical Presentation:

Unlocking Whole Core CT Data for Advanced Description and Analysis

Brendon Hall, Geoscience Applications Engineer, EnthoughtAmerican Association of Petroleum Geophysicists (AAPG)
2016 Annual Convention and Exposition Technical Presentation
Tuesday June 21st at 4:15 PM, Hall B, Room 2, BMO Centre, Calgary

Presented by: Brendon Hall, Geoscience Applications Engineer, Enthought, and Andrew Govert, Geologist, Cimarex Energy

PRESENTATION ABSTRACT:

It has become an industry standard for whole-core X-ray computed tomography (CT) scans to be collected over cored intervals. The resulting data is typically presented as static 2D images, video scans, and as 1D density curves.

CT scan of core pre- and post-processing

CT scans of cores before and after processing to remove artifacts and normalize features.

However, the CT volume is a rich data set of compositional and textural information that can be incorporated into core description and analysis workflows. In order to access this information the raw CT data initially has to be processed to remove artifacts such as the aluminum tubing, wax casing and mud filtrate. CT scanning effects such as beam hardening are also accounted for. The resulting data is combined into contiguous volume of CT intensity values which can be directly calibrated to plug bulk density.

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The Latest Features in Virtual Core: CT Scan, Photo, and Well Log Co-visualization

Enthought is pleased to announce Virtual Core 1.8.  Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs.  It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.

NEW VIRTUAL CORE 1.8 FEATURE: Rotational Alignment on Core CT Sections

Virtual Core 1.8 introduces the ability to perform rotational alignment on core CT sections.  Core sections can become misaligned during extraction and data acquisition.   The alignment tool allows manual realignment of the individual core sections.  Wellbore image logs (like FMI) can be imported and used as a reference when aligning core sections.  The Digital Log Interchange Standard (DLIS) is now fully supported, and can be used to import and export data.

Whole-core CT scans are routinely performed on extracted well cores.  The data produced from these scans is typically presented as static 2D images of cross sections and video scans.  Images are limited to those provided by the vendor, and the raw data, if supplied, is difficult to analyze. However, the CT volume is a rich 3D dataset of compositional and textural information that can be incorporated into core description and analysis workflows.

Enthought’s proprietary Clear Core technology is used to process the raw CT data, which is notoriously difficult to analyze.  Raw CT data is stored in 3 foot sections, with each section consisting of many thousands of individual slice images which are approximately .2 mm thick. Continue reading

Canopy Geoscience: Python-Based Analysis Environment for Geoscience Data

Today we officially release Canopy Geoscience 0.10.0, our Python-based analysis environment for geoscience data.

Canopy Geoscience integrates data I/O, visualization, and programming, in an easy-to-use environment. Canopy Geoscience is tightly integrated with Enthought Canopy’s Python distribution, giving you access to hundreds of high-performance scientific libraries to extract information from your data.


The Canopy Geoscience environment allows easy exploration of your data in 2D or 3D. The data is accessible from the embedded Python environment, and can be analyzed, modified, and immediately visualized with simple Python commands.

Feature and capability highlights for Canopy Geoscience version 0.10.0 include:

  • Read and write common geoscience data formats (LAS, SEG-Y, Eclipse, …)
  • 3D and 2D visualization tools
  • Well log visualization
  • Conversion from depth to time domain is integrated in the visualization tools using flexible depth-time models
  • Integrated IPython shell to programmatically access and analyse the data
  • Integrated with the Canopy editor for scripting
  • Extensible with custom-made plugins to fit your personal workflow

Contact us to learn more about Canopy Geoscience! Continue reading

Enthought at EAGE Copenhagen!

Enthought is at the European Association of Geophysicists and Engineers Conference & Exhibition in Copenhagen! Phew, that’s a mouthful. Enthought cut its teeth on seismic applications and, as you can see from the screenshot above, continues to cultivate its geoscience roots.

If you are in town, please come visit us at Stand 2132 in the Bella Center. We’d love to chat over a smørrebrød!