An integrated model for asset reliability, risk and production efficiency management in subsea oil and gas operations

• Main Author: Okaro, Ikenna Anthony
• Format: Thesis
• Language: English
• Published: Newcastle University 2017
• Subjects: Arctic
• Online Access: http://hdl.handle.net/10443/3651

PhD Thesis The global demand for energy has been predicted to rise by 56% between 2010 and 2040 due to industrialization and population growth. This continuous rise in energy demand has consequently prompted oil and gas firms to shift activities from onshore oil fields to tougher terrains such as shallow, deep, ultra-deep and arctic fields. Operations in these domains often require deployment of unconventional subsea assets and technology. Subsea assets when installed offshore are super-bombarded by marine elements and human factors which increase the risk of failure. Whilst many risk standards, asset integrity and reliability analysis models have been suggested by many previous researchers, there is a gap on the capability of predictive reliability models to simultaneously address the impact of corrosion inducing elements such as temperature, pressure, pH corrosion on material wear-out and failure. There is also a gap in the methodology for evaluation of capital expenditure, human factor risk elements and use of historical data to evaluate risk. This thesis aims to contribute original knowledge to help improve production assurance by developing an integrated model which addresses pump-pipe capital expenditure, asset risk and reliability in subsea systems. The key contributions of this research is the development of a practical model which links four sub-models on reliability analysis, asset capital cost, event risk severity analysis and subsea risk management implementation. Firstly, an accelerated reliability analysis model was developed by incorporating a corrosion covariate stress on Weibull model of OREDA data. This was applied on a subsea compression system to predict failure times. A second methodology was developed by enhancing Hubbert oil production forecast model, and using nodal analysis for asset capital cost analysis of a pump-pipe system and optimal selection of best option based on physical parameters such as pipeline diameter, power needs, pressure drop and velocity of fluid. Thirdly, a risk evaluation method based on the mathematical determinant of historical event magnitude, frequency and influencing factors was developed for estimating the severity of risk in a system. Finally, a survey is conducted on subsea engineers and the results along with the previous models were developed into an integrated assurance model for ensuring asset reliability and risk management in subsea operations. A guide is provided for subsea asset management with due consideration to both technical and operational perspectives. The operational requirements of a subsea system can be measured, analysed and improved using the mix of mathematical, computational, stochastic and logical frameworks recommended in this work.