A comprehensive risk assessment method for Christmas tree combined with a multi source information fusion algorithm...

Area of Science:

• Petroleum Engineering
• Risk Management
• Artificial Intelligence

Background:

• Offshore Christmas trees face significant safety risks during installation and operation.
• High reliability is crucial for subsea oil and gas production equipment.
• Existing risk assessment methods may not fully address the complexities of offshore operations.

Purpose of the Study:

• To develop and validate a comprehensive risk assessment method for offshore Christmas trees.
• To apply a novel combination of GA-BP neural network and improved HAZOP for risk analysis.
• To enhance the safety and reliability of subsea oil and gas production equipment.

Main Methods:

• A hybrid risk assessment approach combining GA-BP neural network and improved HAZOP.
• Multi-source data fusion for extracting risk data from offshore Christmas tree installation and operation.
• Classification and judgment of risk occurrence possibility and severity levels.
• Model training and testing to evaluate the accuracy of the proposed method.

Main Results:

• The GA-BP multi-source information fusion method achieved an average error rate of 5.10%.
• The highest failure probability was identified during the Christmas tree lowering into water phase (critical importance coefficient of 0.24).
• 36 moderate risk points were identified during the production process, with no high-risk failure modes found.

Conclusions:

• The developed risk assessment method accurately predicts potential failures in offshore Christmas trees.
• The method provides crucial technical support for improving the safety of subsea oil and gas production equipment.
• Field testing validated the method's effectiveness, particularly in mitigating nozzle blockage risks with hydrate inhibitor injection.