The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system.

The failure rate of equipment during long-term operation in severe environment is time-varying. Most studies regard the failure rate as a constant, ignoring the reliability evaluation error caused by the constant. While studying failure data that are few and easily missing, it is common to focus only on the uncertainty of reliability index rather than parameter of failure rate. In this study, a new time-varying failure rate model containing time-varying scale factor is established, and a statistical-fuzzy model of failure rate cumulated parameter is established by using statistical and fuzzy knowledge, which is used to modify the time-varying failure rate model. Subsequently, the theorem of the upper boundary existence for the failure rate region is proposed and proved to provide the failure rate cumulated parameter when the failure rate changes the fastest. The proposed model and theorem are applied to analyze the reliability of subsea emergency shutdown system in the marine environment for a long time. The comparison of system reliability under time-varying failure rate and constant failure rate shows that the time-varying failure rate model can eliminate the evaluation error and is consistent with engineering. The reliability intervals based on the failure rate model before and after modification are compared to analyze differences in uncertainty, which confirm that the modified model is more accurate and more practical for engineering.

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

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