Corrosion is one of the major causes of failure in pipelines for transporting oil and gas products. To mitigate the impact of this problem, organizations perform different maintenance operations, including detecting corrosion, determining corrosion growth, and implementing optimal maintenance policies. This paper proposes a partially observable Markov decision process (POMDP) model for optimizing maintenance based on the corrosion progress, which is monitored by an inline inspection to assess the extent of pipeline corrosion. The states are defined by dividing the deterioration range equally, whereas the actions are determined based on the specific states and pipeline attributes. Monte Carlo simulation and a pure birth Markov process method are used for computing the transition matrix. The cost of maintenance and failure are considered when calculating the rewards. The inline inspection methods and tool measurement errors may cause reading distortion, which is used to formulate the observations and the observation function. The model is demonstrated with two numerical examples constructed based on problems and parameters in the literature. The result shows that the proposed model performs well with the added advantage of integrating measurement errors and recommending actions for multiple-state situations. Overall, this discrete model can serve the maintenance decision-making process by better representing the stochastic features.

Offshore safety includes process safety and personal safety. Safety leading indicators (LIs), or potential safety precursors, are parameters defined in the safety program pointing to potential areas for improvement, that if corrected can improve safety performance. For example, timely completion of equipment inspections can reduce the likelihood of equipment failure with associated hazards. They identify which safety metrics are more strongly associated with safety performance in a particular organization. This information can be used to improve future safety performance. This paper describes the research efforts to identify potential safety LIs that may help predict or prevent safety issues for the offshore oil and gas industry. Three relevant case studies of LIs for the offshore industry from the literature are discussed. The focus of this paper is on personal and process safety LIs in the offshore sector. Based on the Bureau of Safety and Environmental Enforcement (BSEE) safety culture factors, this paper collects and categorizes a list of potential offshore safety leading indicators.

This research identifies key factors, or safety culture categories, that can be used to help describe the safety culture for the offshore oil and gas industry and develop a comprehensive offshore safety culture assessment toolkit for use by the US Gulf of Mexico (GoM) owners and operators. Detailed questionnaires from selected safety culture frameworks of different industries were collected and analyzed to identify important safety culture factors and key questions for assessment. Safety frameworks from different associations were investigated, including the Center for Offshore Safety (COS), Bureau of Safety and Environmental Enforcement (BSEE), and the National Transportation Safety Board (NTSB). The safety culture factors of each of these frameworks were generalized and analyzed. The frequency of the safety culture factors in each framework was analyzed to explore commonality. The literature review and analysis identified a list of common factors among safety culture frameworks.

This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and plan future inspection (monitoring) schemes and maintenance policy. A POMDP model is formulated as a decision-making support tool to effectively handle partially observed corrosion defect levels. It formulates states as the pipeline’s degradation level using a probability distribution. Inline inspection (ILI) methods estimate the latest state of the pipeline, which also defines the initial state of the optimization process. The set of actions comprises corrosion mitigation operations. The errors associated with the ILI method are used to construct the observation function for the model. The sum of inspection, maintenance operations, and failure costs for a given state and action are formulated as rewards. Numerical experiments are made based on data collected from the literature. The results showed that different policies, whether derived from solvers (theoretical) or determined from practical experience, can be compared to identify the best maintenance alternative using the model. It was also observed that the choice of the solvers is important since they affect the discounted rewards and the run time to obtain them. The model approximates the parameters and uncertainty associated with the propagation of corrosion, proficiency of inspection methods, and implementation of maintenance policies. Overall, it can be applied to improve the maintenance decision-making process for the oil and gas pipeline as it incorporates the stochastic features of the operation.