An Integrated Bayesian Network and Cost-Benefit Analysis Model for Blowout Preventer Configuration Selection in Deepwater Offshore Fields

Due to the capital intensive nature, limited supply quantities, infeasible and unviable prospects, among several other setbacks of other emergent energy sources, huge importance continues to be placed on Blowout Preventers (BOPs), the principal defense mechanism against blowouts during any drilling/workover operation in the oil and gas sector. Particularly so after the Macondo disaster, BOPs have been the center of regulatory change and sector development. BOP availability and reliability become even more important as drilling advances into deep and ultra-deep water offshore fields. The BOP configuration choice for such variable environments will have far reaching consequences. Reliability, though hugely important and vital, is one of the several criteria that operators must use for determining the most cost-effective configuration as the cost of accidents in deeper waters increases proportionately. In the current paper, an integrated framework for the selection of the most appropriate BOP configuration in deep and ultra-deep water conditions is proposed. The framework captures all evaluation criteria such as BOP reliability, handling/deployability, overall weight and CAPEX/OPEX ratio. Appropriate mathematical and evaluation tools such as Bayesian Network (BN) and Lifecycle Cost Analysis (LCCA) are employed to evaluate different configurations. The models are applied to a commonly used CLASS VII subsea BOPs in deeper waters. The results indicate that configuration 1 (with 2 annular, 2 pipe rams, 1 blind shear ram, 1 casing shear ram) is slightly less reliable than configuration 2 (with 1 annular, 2 pipe rams, 1 blind shear ram, 2 casing shear rams), however, the operation and maintenance (O&M) costs are higher for the latter configuration. Our framework can serve as a valuable decision making tool for BOP stakeholders as varying facets of information regarding the device are obtained