Graduation Semester and Year
Summer 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Earth and Environmental Science
Department
Earth and Environmental Sciences
First Advisor
Nathan Brown, Ph.D.
Second Advisor
Qinhong Hu, Ph.D.
Third Advisor
Arne Winguth, Ph.D.
Fourth Advisor
Majie Fan, Ph.D.
Fifth Advisor
Jiechao Jiang, Ph.D.
Abstract
Petrophysics Characterization with Multiple Sample Scales of Cuttings, Cores and Well Logs for Different Rock Types
Laboratory-based experimental evaluations and nuclear magnetic resonance log data interpretation of carbonates, sandstones and shale rocks enable multi-scale petrophysical parameters characterization for integrated studies that can be applied in both the industry and academia. Numerous studies have been published on pore geometry and connectivity, as well as the diffusivity, migration, and containment of a fluid (oil/gas/water) in different reservoir types. However, despite the importance of diffusion and imbibition as a transport mechanism especially in unconventional resources, the direct measurements of these parameters have been a challenge. Secondly, where a paucity of data exists in drilled wells, it is difficult to assess cuttings aggregate that can accurately predict intact rock attributes. In addition, such uncertainty exists when considered as an input for cuttings enabled automated (AI) lithology and petrophysics predictions. In addition, past studies on micro-scale pore structure and diffusivity mostly performed with crushed rock from intact cores are inapplicable in the absence of in situ-rock or core data. Different data types consisting of drill cuttings, mudlog, wireline logs and core data from four vertical wells located in Denver-Julesburg and Williston Oilfield Basins in the United States were analyzed and integrated in this dissertation. Different workflows were applied to improve the understanding of petrophysical parameters from nanometer (nm) to centimeter (cm) scale observations by focusing on both quantitative and qualitative interpretations of drill cuttings, core plugs, and wireline log measurements via Combinable Magnetic Resonance Tool. The laboratory analyses deployed in this dissertation involved innovative methods for an improved understanding of the petrophysical properties of different Formation types, especially in the absence of core samples or well log data. The analyses performed include particle size distribution, helium pycnometry for particle density, GeoPyc envelopment method for bulk density, X-ray diffraction analysis for mineralogy, scanning electron microscopy for pore morphology, total organic carbon, and gas diffusion for chemical migration. Other laboratory analyses with drill cuttings and core plugs include small-angle X-ray scattering, ultra-small-angle X-ray scattering, liquid displacement, liquid immersion porosity (vacuum saturation), and spontaneous imbibition. This research work highlights some of the uncertainty issues in assessing petrophysical parameters, pore connectivity, liquid imbibition, and gas diffusivity resulting from different scales of measurements and applied methodologies.
Keywords
Petrophysics Parameters, Gas Diffusion, Spontaneous Imbibition, Pore Throat Size, Drill Cuttings, Cores, Nuclear Magnetic Resonance (NMR), Nano-Macro Scale, Denver Basin, Williston Basin
Disciplines
Geology | Oil, Gas, and Energy | Other Earth Sciences
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Azike, Ogochukwu, "PETROPHYSICS CHARACTERIZATION WITH MULTIPLE SAMPLE SCALES OF CUTTINGS, CORES AND WELL LOGS FOR DIFFERENT ROCK TYPES" (2024). Earth & Environmental Sciences Dissertations. 56.
https://mavmatrix.uta.edu/ees_dissertations/56
Comments
I would like to thank Dr. Qinhong Hu (Max) porous media research group and the EES Department, UTA for providing research funding and laboratory apparatus. I also want to acknowledge Forest Oil Corporation (Sabine Oil & Gas), OMINEX Resources Inc. and United States Geological Survey (USGS), Denver, Colorado for the research data.