IBRP has two roles in today’s Oil & Gas industry:
Comparison of Image Segmentation from “Uncertainty Quantification in Image Segmentation for Image-Based Rock Physics in a Shaly-Sandstone” For Sample B, 4-micron resolution, 680x680x900 volume, slice 450.
IBRP includes contributions from three separate disciplines:
In the DigiM I2S platform, the acquisition of images is returned to the client. DigiM can provide guidance on the best methods to use, but recognizes that the client often has more expertise on how to image their own samples.
The 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 allows the client real-time information on the quality of their segmentation setup. Multiple grain and pore types can be identified reliably with the result of capturing more 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, electrical and thermal conductivity, and basic characterization of the pore space such as pore size, capillary pressure, and tortuosity.
The modules use standard algorithms and numerical solvers, but I2S returns control of the simulation to the clients. Input parameters are client-selected, which encourages numerical experimentation on the importance of individual contributors to the simulation results.