Natural Resources

Natural Resources

The promise of Image-Based Rock Physics (IBRP), often referred to as Digital Rock Physics (DRP), has been around since the 1950s when simple pore networks of nodes and connectors were analyzed with electrical resistor networks. The intervening 60+ years has produced significant developments in imaging and computational power to where IBRP is used to predict multiphase fluid flow in complex pore systems. 

IBRP has two roles in today’s Oil & Gas industry:

  1. As a predictive tool for petrophysical properties in rocks that are difficult to measure in the lab.
  2. As an evaluation tool for complex Special Core Analysis (SCAL) measurements where a series of numerical experiments with varying input parameters can be executed faster than comparable lab tests.
The red box in the figure displays the order of magnitude in which DigiM imaging covers.

DigiM’s I2S software returns control of the DRP process to the clients by providing a state-of-the-art image segmentation tool based on machine learning principles, coupled with a series of simulation modules that capture a range of single and multi-phase flow properties.

A successful DRP workflow requires:

  • Images of the pore geometry that are representative, and with the sufficient resolution,
  • Accurate segmentation the pore geometry from the rock matrix,
  • Simulation of petrophysics properties, including single- and multi-phase transport phenomena.

In the DigiM I2S platform, DigiM can help the clients to acquire images through DigiM services. DigiM can also work on the clients’ images directly, recognizing that the client has more knowledge and specific requirements on how to image their own samples. 

DigiM machine learning (ML) based image processing tool does reduce the amount of pre-processing of the image that is required before segmentation steps. Traditional image pre-processing of smoothing, filtering, edge enhancements are less important now as ML-based segmentation is much more robust and can overlook image defects. The ease in training the ML-segmentation tool provides the client with real-time feedback on the quality of their segmentation training. Multiple grain and pore types can be identified reliably with one training session, following faithfully what the users’ eyes observe in the images.

The I2S platform includes a suite of simulation modules that cover a range of petrophysical properties that include permeability, relative permeability, electrical and thermal conductivity, diffusivity, and mechanical properties.  I2S made parameter traversal and batch processing extremely easy and user friendly, which encourages numerical experimentation on the importance of individual contributors to the simulation results. 

Case Studies (Click on each image to learn more)

Presentations and Publications

  1. Howard, J. (2019). Machine-Learning Methods in Image Processing and Analysis of Porous Materials. DigiM whitepaper. 
  2. Zhang, S., Byrnes, A.P., & Howard, J. (2019). Properties Upscaling in Porosity Systems with Under-resolved Features Using Image-Based Rock Physics. Paper presented at 2019 URTeC Meeting in Denver, CO (July 22-24, 2019). With Whiting.
  3. Howard, J., Lin, S., & Zhang, S. (2019). Uncertainty Quantification in Image Segmentation for Image-Based Rock Physics in a Shaly Sandstone. Petrophysics, 60(2), 240-254. 
  4. Howard, J., Lin, S., & Zhang, S. (2018). Uncertainty Quantification in Image Segmentation for Image-based Rock Physics in a Shaly-sandstone. Proceedings of International Symposium of the Society of Core Analysts, Trondheim, Norway, 27-30 August 2018. 
  5. Byrnes, A.P, Zhang, S., Canter, L., Sonnenfeld, M.D. (2018). Application of Integrated Core and Multiscale 3-D Image Rock Physics to Characterize Porosity, Permeability, Capillary Pressure, and Two and Three-Phase Relative Permeability in the Codell Sandstone, Denver Basin, Colorado. Unconventional Resources Technology Conference, URTeC 2901840, Houston, TX (July 23-25, 2018). With Whiting.