Description of field of research:

Porosity being a key parameter for reservoir characterisation, its reliable determination remains a challenging topic due to the complexity of the underground. It can be obtained from various wireline logging devices in the field or porosimetry in the laboratory, but the results are not always consistent, confusing further modelling at the subsurface. A better understanding of calibration between logging readings and core porosity underpins the basics in mining, geotechnical and petroleum engineering. A series of world-leading measurement equipment will be open to candidates for this in-depth investigation. The project aims to comprehensively investigate the related logging response to the porosity and applies up-to-date algorithms to calibrate with the core porosity of sandstone, siltstone and organic rich-rocks. The candidate will interact as a team member in the research group, interacting with other PhD students and academic staff. During this project, the candidate can develop a strong research background and understand the basic knowledge regarding porosity lab measuring, wireline log interpolation, classic machine learning techniques and research management skills.

School

Minerals and Energy Resources Engineering

Research areas

Petrophysics; Laboratory rock measurement; Machine learning

The novelty in the proposed studies lies in the unified data-driven/theoretical relationship linking core porosity with  geophysical logs. A series of laboratory measurement will be undertaken to capture core porosity to validate the relationship. This outgrowth has innovative prospect in all underground engineering projects such as reservoir gas content estimation and tunnelling. The candidate is expected to help HDR students to generate experimental results and high-quality publications.

The successful applicant is expected to: 

  1. Propose a robust model between well-logging data and core porosity, which can indicate the continuous variation along well bores in studied areas; 
  2. Improve the understanding of data analysis process for geophysical and geotechnical purposes and be able to identify significant features.