Dataset for estimating occurrence probability of causations for plugged, abandoned and decommissioned oil and gas wells

• Main Authors: Ahmed Babaleye, Rafet Emek Kurt, Faisal Khan
• Format: Article
• Language: English
• Published: Elsevier 2020-08-01
• Series: Data in Brief
• Subjects: Hierarchical Bayesian model; Failure analysis; Well plugging and abandonment; Decommissioning
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920308829

This article contains the dataset on the failure frequencies of the barrier and mechanical plugs in place within the hydrocarbon-containing wellbore during plugging and abandonment operation. The interpretation and application of this data can be found in the research article (“https://doi.org/10.1016/j.psep.2019.09.015” Babaleye et al., 2019). These datasets were collected through a comprehensive hazard identification technique workshop involving 10 engineers and academics with considerable years of field experience. The data were collected based on how likely it is for each causation to occur and these likelihoods are ranked from 1 to 10. The process is experience-driven and is complemented by a 1–10 rating of the duration of leak of hydrocarbon before remediation, should the leak reach the mudline. The ranked data was a representative of raw failure data (failure rate or mean time to failure (MTTF)) for each causation and are coded in MATLAB using gamma distribution based on hierarchical Bayesian analysis. The dataset offers unique opportunity for reuse due to its accessibility and discreteness.