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29 Apr

2nd Edition: Geological Process-Based Forward Modeling AAPG Call For Abstracts Expires in 18 days

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Deep Learning/Machine Learning Technical Interest Group (TIG)

Explorer Director’s Corner By David Curtiss

Preparing for the Future

The application of technology and analytical techniques has always been a hallmark of our profession, but this year ACE had special sessions dedicated to machine learning and AI in petroleum geoscience. The trend toward increased automation and greater efficiency in our workflows will affect how the petroleum explorationist works in the decades to come. Helping our members prepare for this future is something to which AAPG is committed.

Added on 01 June, 2019

Explorer Emphasis Article By David Brown

The Oil Industry's Cyber–Transformation Is Closer Than You Think

The concept of digital transformation in the oil and gas industry gets talked about a lot these days, even though the phrase seems to have little specific meaning. So, will there really be some kind of extensive cyber-transformation of the industry over the next decade? “No,” said Tom Smith, president and CEO of Geophysical Insights in Houston. Instead, it will happen “over the next three years,” he predicted.

Added on 01 June, 2019

Explorer Emphasis Article By David Brown

The Next Big Leap

Oil and gas companies have spent years cleaning up and formatting their huge datasets for processing. When that cleanup work is done, the industry will be able to apply machine learning, artificial intelligence and Big Data techniques to extract information and improve operations. And maybe even make exploration discoveries.

Added on 01 June, 2019

Learn! Blog By Susan Nash

Interview with Alec Walker, DelfinSia: Unstructured Data Mining and Text Analytics for Improved Upstream Efficiencies

There is valuable information locked up in seemingly inaccessible places, and not of the data is compatible or even organized consistently. How can one use the valuable information for good decision-making for oil and gas operations?

Added on 03 May, 2019

Learn! Blog By Susan Nash

AAPG and Machine Learning for Business Management

We are excited to announce our support for the 4th Annual Machine Learning in Oil & Gas Conference (www.machinelearning-oilandgas.com) which will take place in Houston on April 17th & 18th.

Added on 01 April, 2019

Explorer Director’s Corner By David Curtiss

'Going the Distance' with the World's Premier Offshore Event

While shale oil and shale gas deliver quick returns and product to markets, it’s the deepwater and other offshore discoveries that yield significant reserve additions and long-lasting production.

Added on 01 April, 2019

Learn! Blog By Susan Nash

Interview with Paul Cleverley: Using Machine Learning with Geological Texts to Create Intellectual Property

One of the most challenging areas of research involves combing through articles to develop new insights and ideas. Welcome to an interview with Paul Cleverley, geologist and data scientist, who has developed a machine learning algorithm designed to comb through unstructured geological texts and pull together new intellectual property.

Added on 21 February, 2019

Events Blog By Grant Smith

Attend Two Exciting AAPG Events for Machine Learning in Oil and Gas

Learn how machine learning and analytics are transforming the way that the industry explores for and produces oil and gas with these two training events.

Added on 02 January, 2019

Correlator Article By Terrance Harris

Here Come the Robots: Should We Be Worried?

As robotics and artificial intelligence technology advances, does it necessarily mean eliminating jobs? Terrance Harris finds that restructuring of the types of work humans perform in the future will improve productivity and stabilize employment rates.

Added on 01 January, 2019

Explorer Geophysical Corner By Christopher P. Ross,David Cole

Two Data Reduction Schemes: A Shallow Oil Field Example

Geoscientists with “too much” data can either choose which data attributes are best, based on some tabulation or crossplot, or attempt to combine different data sets in a linear-based process such as cross-plotting. We review two progressive approaches to systematically reduce data dimensions while synthesizing a best attribute from an ensemble of seismic input attributes.

Added on 01 January, 2019

DL Abstract By Shane Prochnow

Subsurface Machine Learning Approaches at Hydrocarbon Recovery and Resource Forecasting for Unconventional Reservoir Systems

This presentation is a survey of subsurface machine learning concepts that have been formulated for unconventional asset development, described in the literature, and subsequently patented. Operators that utilize similar subsurface machine learning workflows and other data modelling techniques enjoy a competitive advantage at optimizing the development of unconventional plays.

Request a visit from Shane Prochnow!

Added on 14 September, 2023

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