Introduction
Predicting the sealing potential in a carbonate succession poses significant uncertainty for both oil and gas exploration, production but also for CO2 storage. No algorithms, such as the shale gouge ratio, exist for sequences containing carbonate layers. To address this knowledge gap, several recent publications have started to work towards a predictive method for carbonate fault seal. However, none have used known fluid contacts across the fault to calibrate the predicted fault seal.
This work will improve our knowledge not only of how carbonate fault rocks influence any sealing potential of the faults but also how any marl smears may impact fluid flow. This is important for CO2 storage sites such as those within the Northern Horda Platform, as this relies on marls creating low permeability barriers to fluid flow in order for the site to be viable.
This project will form part of an ongoing research project known as FRISK, a spin-off project from NCCS, researching into methods for de-risking faults for CO2 storage.
Aims
The aim of this MSc project is to use hydrogeological information from the Maltese Islands (e.g. from the Energy and Water Agency of the Government of Malta, or simply from Barbagli In Review) in order to calibrate any previously established or new relationships to predict fault rock permeability.
Objectives
- Identify relationships that can be used to predict fault rock permeability in carbonate sequences.
- Depending on student preference, machine learning and data mining techniques can be used to improve the above step.
- Create a framework model using topography and maps of the Maltese Islands.
- Create and populate a geocellular model of the Maltese Islands.
- Code algorithms for predicting the fault permeability.
- Generate transmissibility multipliers for input in simulation.
- Simulate the flow across the Islands.
- Compare with the known water contacts across the fault.
Data
- Previously measured fault and host rock porosity and permeability.
- Topography DEM and maps
- Fieldwork on Malta identifying the role of fault rock type, depending on juxtaposed lithofacies, to accompany any results (COVID-19 restrictions dependent).
Tools and Method
The student will use simple machine learning techniques within excel, to possibly more advanced data mining software (depending on research preference).
Petrel, Move, and/or T7 (TrapTester) will be used to create fault frameworks and geocellular models. ECLIPSE (or similar) will be used for fluid flow simulation.
If permitted, fieldwork methods will include examining fault rock types along-strike and down-dip, paying significant attention to the width and properties of any marl smears, relating this back to the Shale Smear Factor.
Learning Outcomes
Proficiency using Petrel E&P Software Platform, Midland Valley Move and/or Badleys T7. Proficiency using ECLIPSE (or similar simulation software). Statistical analysis of fault rock permeability depending on input factors. Improved fieldwork analyzing faults in outcrop. Independent research, academic writing and presentation skills.
For further information or to register interest in projects, please contact Emma Michie (e.m.haines@geo.uio.no).
