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New analytical techniques are transforming the way that reservoirs are discovered as well as characterized and produced. With all the new processes and approaches, data continues to be a challenge, especially in developing reservoir models that are accurate and can be scaled.

Welcome to an interview with Benmadi Milad, Ph.D., who will be participating in the AAPG Success with Difficult Times GTW in Houston, Nov 12-13. His focus is on machine learning for better modeling using a wide array of data.

Recently Added Training

AAPG Event Houston Texas United States 22 November, 2019 Emerging Technology, Machine Learning, Imaging and Analytics 1:00 PM 5:00 PM 24 October, 2019
 
Houston, Texas, United States
22 November 2019

Date: 11/22/2019 to 11/22/2019
Time: 1PM to 5PM
Venue: The Cannon Houston
Location Address: 1334 Brittmoore Rd, Houston, TX 77043
Location City/State: Houston, Texas

This course is for people without a background in machine learning, software engineering, or data science. The purpose of the course is to help people who are immersed in the oil and gas industry to gain a practical understanding of what unstructured data is, what value there is in it, how it can be utilized, and why this is now relevant. Much of unstructured data mining is based on machine learning, so this course also seeks to instill memorable intuitive understanding of machine learning. This course does not require any use of computers.

The course consists of these segments:
  1. The differences between structured and unstructured data will be explored, and emphasis will be placed on why unstructured data is crucial to firms in the oil and gas industry. The drastic inefficiencies created by mismanagement of unstructured data will be given context by the growth in private equity backing, the lower hydrocarbon price environment, the proliferation of data sources, and the aftermath of the big crew change.
  2. Attendees will engage in an interactive simulation of a typical oil and gas workflow involving structured and unstructured data.
  3. Incumbent solutions, including data file structuring projects and off-the-shelf enterprise search tools, will be considered. Each will be evaluated based on its ability to return accurate and relevant information to users in a useful format and at speeds that do not inhibit seamless operations.
  4. Attendees will engage in an interactive simulation of each of the two typical incumbent solutions to handle unstructured data.
  5. The basics of machine learning will be explained in simple and intuitive terms, including a few examples.
  6. Attendees will engage in an interactive application of machine learning to solve a problem. This application will be a simulation not requiring the use of any electronics.
  7. Three case studies will be explored showing results and highlighting value added of a new (to oil and gas) class of solution. The first study involved implementation of a tool to help workers navigate historical reports to extract knowledge to make better decisions in real time. The second study involved implementation of a tool to serve as a surrogate for a retiring subject matter expert so that less experienced employees could still get good answers to questions. The third case study has not been completed, yet, but it involves simply using a tool to automatically fill out missing fields in a database from data scattered across unstructured reports.
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Join a TIG

 
How detrital zircon is the literal Rosetta Stone of deep marine clastics, revealing provenance, depositional history, and timing. Chaired by John Snedden.
American Association of Petroleum Geologists (AAPG)
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Join a SIG

 
The mission of AAPG Women's Network is to increase participation and advancement of women in Earth sciences and the Energy Industry, with an emphasis on education, outreach, support, leadership development, and ultimately retention. AAPG Women's Network will interact with women in Earth Science, their male peers and employers, educational institutions, and professional societies to accomplish this mission.
American Association of Petroleum Geologists (AAPG)
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Join a Committee

 

Provide support for various Student Job Expos. The committee should contain members from all areas that provide Student Expos in order to share best practices. Communicate news about Student Expos to Chair of AAPG Corporate Advisory Committee.

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Classified Position Available

Three Positions in Sedimentary Geology

The University of Houston Department of Earth and Atmospheric Sciences invites applications for three tenure-track positions in the general field of sedimentary geology. Hiring will be at the assistant professor level with up to one hire at the associate professor level.

Deadlines

30 Nov

AAPG/EAGE 5th Myanmar Oil & Gas Conference – Call for Abstracts Expires in 16 days

Business and Economics, EngineeringAsia/Pacific Region - ...Myanmar
05 Dec

Unconventional Resources Technology Conference 2020 - Call for Papers Expires in 21 days

Business and Economics, EconomicsUnited States - ...Gulf Coast Section - ......Texas
09 Dec

IBA 2020 Call for Applications Expires in 25 days

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