Industries that deal with elevated levels of risk and consequent regulatory scrutiny can make use of decision support tools that provide a reliable, objective method of assessing risk situations and recommending appropriate responses. The BOP risk model addresses the important and sometimes costly decision of when to pull a subsea Blowout Preventer (BOP) to the surface for repair and maintenance.
While in operation a subsea BOP sits on the seafloor and functions with limited visibility, or telemetry data available to operators, to assess its condition. Operators often find themselves pressured between safety concerns and economic pressures as they try to make optimum decisions about situations that could have severe effects on life, property and environment.
The BOP Risk Model has been developed to assist in this critical decision making process. Local regulations, operating procedures, P&IDs, Logic Block Diagrams and Fault Tree Analysis are applied in calculating a risk level for modelled BOP component failure, and expressed by colour-coding for easy reference. The risk level is associated with a general recommendation for course of action. Notification of high risk situations can be sent directly to regulators.
Given the physical and human systems issues involved, a risk management tool for the BOP Pull/No-Pull decision has a number of major requirements. It must provide sustained confidence in safe operations while balancing three sometimes conflicting issues: NPT (Costs), Risk (Safety), and Compliance with local, state, and federal laws. It has to do this in a way that helps people make decisions under pressure that are objective and consistent and not dependent on the availability of experts. It has to reconcile potential conflicts ahead of time based on the input and consensus of all stakeholders. And the relevant regulators must agree with the methodology (in the United States, BSEE).
Lloyd's Register Energy's general strategy has been to utilize technology that has been proven in other high risk industries (i.e., nuclear power generation) and adapt it for this purpose. Just as BOP operations involve both the physical system and the human decision making system, developing a BOP Risk Model has involved an understanding of both the physical and the human systems involved.
A major element in the success of this effort has been in the process of creating the model itself. All parties with an interest in how these decisions are made come together to develop what is essentially an expert system. The goal is to decide ahead of time how the decision should be made, based on what factors and according to what rules, and resolve differences of opinion and potential conflicts of interest ahead of time. By moving the risk evaluation and recommendations to a team design process, ad hoc decisions made under pressure without taking all issues into consideration can be avoided. A team is assembled for each BOP Risk Model (or set of models if the BOP and regulatory requirements are close to identical). It typically consists of representatives of the owner/operator, experts in BOP design and function and people with deep expertise in risk assessment methodology and fault tree analysis. The design team takes the time to thoroughly analyse all the issues that should be involved in the decision and establish the decision making logic that should ideally be consistently applied. This ensures that a consensus-based and predetermined decision-making logic is applied in all situations using automated procedures of risk evaluation.
It is important to note that there has been no attempt to make the decision for the operators. The BOP Risk Model is intended as a tool that automates risk assessment based on existing current condition information, provides an assessment of the level of risk and an associated recommendation of the general course of action to be taken given the level of risk.
The BOP Risk Model is designed to be a decision support tool. The level of risk determined for a particular situation does not enforce a decision, it yields a recommendation based on a careful prior process. The actual decision still has to be made by people and involves several parties, ultimately providing a tool for complex human-machine systems.