Semester of Graduation

Spring 2018

Degree

Master of Science in Petroleum Engineering (MSPE)

Department

The Craft and Hawkins Department of Petroleum Engineering

Document Type

Thesis

Abstract

In the field of petroleum engineering, rock samples are often taken from wells during the drilling process. Grain partitioning of digital three-dimensional microtomography segmented images obtained from these samples provides valuable in-situ properties and statistics that allow for accurate particle and structure characterization. This information can be used directly in detailed production and reservoir analysis, and can also be used to generate realistic packing models for advanced simulation. Additionally, the partitioned image can be used as a building block for realistic hydraulic fracture modeling. This technology has applications in other fields as well, such as core analysis in soil sciences and developing novel structures in material science. Several automatic and manual partitioning algorithms have been developed, but these algorithms often perform poorly for irregularly shaped or consolidated rocks. The objective of this work is to improve the accuracy, reliability, and control of the grain partitioning algorithm VOX2GRAINS. The program is broken into two separate categories: initial partitioning, and post processing refinement. The initial partitioning methodology assumes that the true particle interfaces coincide with watershed surfaces. In order to combat the over partitioning that is common with this methodology and to provide smoother interfaces, several new iterative techniques have been implemented into the particle assembly stage along with an adjustment to the distance map generation. Once the initial partitioning is completed, the user has the opportunity to interact with the three-dimensional partitioned image. Bulk and individual properties, such as porosity, particle volumes, surface areas, contact areas, and aspect ratios are calculated and displayed. Results show that many of program’s new additions and alterations provide more accurate and realistic grain-grain interfaces. The program’s post processing options have been expanded to include planar regression of grain-grain contact surfaces and the reduction of over partitioned grains through machine learning via logistic regression. The machine learning refinement option was found to be a particularly effective method that combines user control, automation, and time efficiency to create a more accurately partitioned image.

Date

3-22-2018

Committee Chair

Thompson, Karsten

Available for download on Tuesday, March 26, 2019

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