New concepts and technology are vital for the Oil & Gas industry to meet ever more challenging requirements for drilling and production in deep and ultra-deep waters. When new technology is deployed, whether with novel equipment or standard equipment in novel applications, it is critical to build confidence in ts safety and reliability before implementation. Given that current industry standards and codes might not address all potential modes of failure for new technologies, a systematic technology qualification process can be used to identify and mitigate any potential threats. Compliance with the required functionality and reliability requirements can be demonstrated through qualification methods such as testing and analyses, and the uncertainty or risks associated with the technology, system and interfaces can be eliminated or minimized.
Several recognized technology qualification methodologiesare currently available to the industry that completely or partially address qualification of new technology: ISO 20815 Petroleum, Petrochemical and Natural Gas Industries - production assurance and reliability management [1], NASA technology readiness level assessment system [2], DOE G 413.3-4A technology readiness assessment guide [3], Technology Readiness Assessment (TRA) Guidance, United States Department of Defense [4], , API RP 17N Recommended Practice for Subsea Production Systems Reliability and Technical Risk Management [5], API RP 17Q Subsea Equipment Qualification [6], and DNV RP A203 Technology Qualification [7].
Two of the widely accepted technology qualification (TQ) methodologies in the offshore Oil & Gas industry are API 17N [5] and 17Q [6] and DNV RP A203 [7]. Although both methodologies are quite similar, the differences may confuse technology developers, this paper addresses differences including the circumstances under which one might be more efficient/cost-effective than the other, how new technology is defined, and how much rigor is needed to address potential threats.
In summary, this paper reviews and examines the similarities and differences that are presented in the two widely accepted TQ methodologies. Further, this paper provides insights on how to employ these methodologies to meet specific end user needs. In addition, a case study is included to demonstrate the comparisons between the methodologies.
