Project 1: Drilling Optimisation
- Thomas Richard (Curtin University)
- Andrew Wurst (Curtin University)
- Hongyang Zhang (Curtin University)
- Yevhen Kovalyshen (CSIRO)
- Alton Grabsch (CSIRO)
- Daniel Wurst (UniSA).
1 January 2019 - 31 December 2021
- Curtin University
- University of South Australia
- Geotec Boyles
- South 32
Project 1 aims to improve drilling optimisation and automation by (a) engineering modelling of various processes involved in drilling operation, (b) systematic and reliable recording of drilling data, which can be used directly for real-time and post-mortem optimisation, (c) developing engineering algorithms that can be used for optimisation of the drilling operation and autonomous analysis of drilling data, and (d) developing a modular autonomous optimisation system that can pave the way towards complete automation.
In Phase 1, drilling processes will be studied in the lab and field, engineering models and algorithms will be created to characterise each method, and prototype optimisation modules will be developed. Phase 1 will provide the research, technical and practical for technologies that will be further developed and matured in Phase 2 and 3.
Phase 1 Objectives:
- Develop a comprehensive and detailed review of technologies and models in the mineral and petroleum drilling industry that can be incorporated in drilling optimisation and automation.
- Develop a drilling optimisation and automation roadmap based on the objectives of the Project and current (and projected) status of drilling technologies.
- Study the key drilling processes and developing engineering models and algorithms, e.g. bit/rock interaction, vibration, hydraulics, and borehole stability.
- Develop/utilise data recording systems to characterise drilling performance, provide quality data for research, evaluation of drilling solutions and field parametric analysis.
- Conduct field experiments to validate/calibrate the engineering models and algorithms.
- Identify and document effective drilling practices based on field trials and incorporate the learnings in autonomous interpretation and optimisation algorithms.
- Commence development of prototypes autonomous optimisation modules.