Quantitative Risk Assessment of Tar Sands Oil Extraction Facility
Learn how we helped a major oil and gas company manage risk at an Oil Sands Operation in Northern Alberta.
Establish key hazards that could impact operational safety during Tar Sands Oil Extraction
Produce baseline Quantitative Risk Assessment (QRA) models to reveal improvements that could reduce risk
Provide recommended improvements to the client based on the QRA results
Background: What is Tar Sands Oil Extraction?
The method of oil extraction used by many companies in the Athabasca region involves pumping large volumes of steam in underground tar sand deposits to liquefy oil, then using wells drilled in the deepest sections of the reservoir to pump that oil. The overall process includes pumps, valves, pressure monitoring wells and the underground rock formation where oil is liquefied and eventually extracted. ABS Group was selected to perform a Quantitative Risk Assessment (QRA) to assist the client in better understanding the operational risks of tar sand reservoir over-pressurization.
ABS Group collaborated with our client to assemble a team of subject matter experts in oil sands extraction and geology to frame the problem. Based on expert observation, the key risks of concern were:
- An explosion of one or more underground reservoirs.
- Large volumes of the steam-hydrocarbon mixture escaping out of the reservoir. This could result in environmental damage, death or injury in populated locations.
- Seepage of hydrocarbons into the environment and underground water reservoirs. While the tar sand oil reservoirs were located well below any water tables, the pressure from the steam could act as a catalyst to move the oil at or above the water tables through cracks in the bedrock.
The operational risks observed were loss of life and damage to the environment and on-site and offsite property.
We developed baseline QRA models using RISKMAN software for three reservoirs. The model was based on in situ operations and coupled with geological knowledge at each site. Potential high-level system failures included pressure monitoring, pumps unable to stop at the designated pressure and bedrock fracturing due to overpressure. The models were further refined to include geologist’s estimates of the bedrock’s capacity threshold with respect to the pressure build-up in the reservoir.
The quantitative baseline risk and consequence results were nominal or not particularly alarming. However, performing the QRA and the process of developing the risk model revealed that several improvements could help reduce risk and identified two areas that warrant further study beyond the scope of our QRA project.
Improvement Recommendations Based On the QRA Results
- Increase the redundancies in the pressure monitoring and alarm systems. This would entail monitoring more wells per square mile to cover the reservoir with better granularity.
- Increase the emergency shutdown redundancy and utilize existing staff to perform manned monitoring of the shutdown process.
- Optimize maintenance intervals.
Through the process of building the QRA model, we learned that there are abandoned and unaccounted exploration wells that may not be properly sealed. These wells, if not properly sealed, could invalidate the geologists’ bedrock pressure tolerance by allowing steam hydrocarbon mixture to escape or seep into the environment. Our recommendation was to further research these abandoned wells by studying historical drilling permits to locate and examine the wells.
The QRA results showed that the uncertainty of the bedrock’s pressure capacity, as analyzed by geologists, greatly influences the overall risk. The recommendation was formulated in collaboration with a team of geologists that were knowledgeable in the local formations. Increasing the number of controlled test wells would reduce uncertainty, which in turn would enhance the bedrock pressure capacity computations.
To justify the above recommendations, the baseline QRA model was further developed to include hypothetical (what-if) scenarios. The hypothetical models demonstrated the risk reduction due to increased redundancy and reduced uncertainties associated with the study areas identified.