Tulsa SPWLA Monthly Luncheon Meeting  

Thursday Sept 12 2019

Tulsa University
Helmerich Hall- Room 121
800 S Tucker Dr.
Tulsa, OK 74104

11:30 – 1:30 pm

Register and pay online or pay cash/check at the door.
RSVP: [email protected]
Cost - $25 for Professionals and FREE to students with student ID

Machine-Learning Methods: Analysis of Rock Images and Beyond

Presented By: James J. Howard, DigiM Solutions

ABSTRACT: 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.

BIO: James Howard 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.