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Home›Subsea›SES and Optimum Program in Drilling Riser Maintenance Pact
Subsea

February 7, 2020 · about 6 years ago

SES and Optimum Program in Drilling Riser Maintenance Pact

Stress Engineering Services (SES) has entered into an agreement with Optimum Program to form the RiGUARD joint venture. RiGUARD combines SES’ offshore drilling analytics expertise with the Optimum Program data-driven drilling riser maintenance program that delivers drilling riser condition-based mai

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SES and Optimum Program in Drilling Riser Maintenance Pact

Stress Engineering Services (SES) has entered into an agreement with Optimum Program to form the RiGUARD joint venture.

RiGUARD combines SES’ offshore drilling analytics expertise with the Optimum Program data-driven drilling riser maintenance program that delivers drilling riser condition-based maintenance (CBM) program.

Together, the companies aim to develop a CBM program that will optimize the timing and cost of drilling riser maintenance.

The data-driven maintenance program incorporates riser condition, usage and fatigue analysis with a risk-based inspection process.

Kenneth Bhalla , chief technology officer, SES, said: “Growing our portfolio of capabilities is critical to our success. This alliance with Optimum adds innovative capability that enhances the digital elements of our CBM program, ultimately reducing OPEX and CAPEX, and minimizing and managing risk for our clients by understanding the health of their assets. We are excited to form the RiGuard alliance and look forward to providing world class service to our clients with this state-of-the-art technology.”

Jason Fowkes , business development manager, Optimum Program, said: “This cumulative effort delivers a program that offers customers not only a transparent, cloud-based portal to manage their equipment, data and records, but true analysis and condition assessment. The platform’s capacity to gather standardized data allows for credible analysis, resulting in continuous improvement and value delivery in the realm of predictive maintenance.”

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