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The members and officers of the SPWLA Tulsa Chapter are proud to revive one of the founding chapters of this prestigious International Society. Please join us as we continue to promote and advance the science of formation evaluation.
Meetings will be held on 2nd Thursdays bimonthly beginning September 12, 2019, 11:30am – 1:30pm
The University of Tulsa, Room 121 of Helmerich Hall
800 S Tucker Dr, Tulsa, OK 74104
Interactive campus and parking maps at https://maps.utulsa.edu/
Lunch will be provided
$25 for professionals and FREE for students with student ID
Registration LINK ( Click here) SPWLA account is required or you will need to create an Account
For catering purposes, reservations must be made no later than 10 days prior to the meeting.
Make your reservations online through the Tulsa Chapter web page at spwla.org or by emailing email@example.com
A parking pass will be sent to you prior to the meeting.
Cash and checks only will be accepted at the door. A credit/debit card link will be available through the Tulsa Chapter web page in the very near future.
SPWLA Tulsa Chapter 2019-2020 Luncheon Speakers:
September 12, 2019
James J. Howard - DigiM Solution
Machine Learning Methods: Analysis of Rock Images and Beyond
Big-Data, Data-Driven Analytics, Artificial Intelligence and Machine Learning are all terms found in many recent presentations within the petrophysics community. Often these terms are used interchangeably, but they are distinct differences amongst them. The common thread with these terms is that the application of these various techniques and terminologies is based on the large volumes of data that confront petrophysicists in the day-to-day operations of large, highly instrumented fields. One example of Machine-Learning (ML) methods is to provide a more accurate and robust approach to the processing of images, including high-resolution images of pore systems acquired with micro-CT and SEM techniques.
Each of these imaging methods has a range of resolutions and sample volumes that cover several orders of magnitude. In an ideal situation the images are characterized by a range of intensity values that when evaluated as a histogram there is a clear distinction between grains and pores. The reality is that image quality is often compromised by instrument noise, overlapping phases in a given voxel and other factors that generate an intensity histogram that is less discrete and more difficult to process. A machine-learning based segmentation tool provides a more robust solution to the phase separation challenge by including a wide range of statistical measures of each voxel in the image.
Along with the basic image intensity value for each voxel, the accumulated statistics include information on nearest-neighbor and next-nearest-neighbor properties derived from a series of filters and gradient measurements. Since the ML-based segmentation includes nearest-neighbor information, it can be used to distinguish phases with similar intensity, but distinctly different surface textures as observed by the user. Segmentation of pore space is validated through visual inspection followed by comparison of static properties, e.g. porosity and pore-size distribution, and finally dynamic properties such as permeability, capillary pressure, relative permeability and upscaling. These algorithms and workflows can be applied to other large dataset such as image logs, whole core CT and core photos.
James is a Technical Advisor to DigiM Solution, LLC, an Image-Based Rock Physics software company, which is giving him a late-career opportunity to explore a long-held interest (since the 80s) in “digital rocks”. In an earlier life he worked on a range of tools used for pore-scale characterization of reservoir rocks, including the development of NMR interpretation methods in the lab and the field, the application of MRI and microCT imaging to monitor flow experiments, mineral mapping by SEM-EDS, and other curious topics. He has B.S and Ph.D. degrees in geology and geophysics.
November 14, 2019
Paul Craddock - Schlumberger, North America
Thermal maturity-adjusted log interpretation (TMALI) in organic shales
January 09, 2020 TBA
March 12, 2020 TBA
May 14, 2020 TBA
Reach out to us with any questions you may have regarding membership in SPWLA, our luncheon meetings and distinguished speakers, or just drop us a message and let us know how you are and what you are doing. We would love to hear from you!
SPWLA Tulsa Chapter's email address is firstname.lastname@example.org
...or you can send a letter to our post office box:
SPWLA Tulsa Chapter, PO Box 14495, Tulsa OK 74104-9998
President: Elizabeth Dickinson email@example.com
Vice President of Technology: Maureen McCollum firstname.lastname@example.org
Treasurer/Secretary: Patrick Ryan Pryan50@att.net