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Collaborating Authors
Offshore and onshore reliability data (OREDA) gathered by several oil and gas operators for nearly 4 decades is now available online through DNV GL's data platform, Veracity. The OREDA handbook, established in 1981 in cooperation with the Norwegian Petroleum Directorate, has collected data from almost 300 installations and includes 18,000 equipment units with 43,000 failure and 80,000 maintenance records. The databank also includes information on subsea fields with more than 2,000 years of operating experience. Working in partnership with French IT service provider SATODEV and OREDA member companies, the data were originally presented in a traditional handbook and have been converted to a digital tool called "OREDA@Cloud." Instigated by a joint industry project (JIP), it allows users to have interactive access to the database.
ABSTRACT The purpose of this paper is to review the worldwide historical structural failure data in the 1990s on offshore structures, and compare this with the present risk analyses of Norwegian offshore structures. The paper describes an overview of registered accidents to offshore structures based on the databases WOAD and CODAM. The accident data is given for fixed platforms, jack-ups and for floating platforms. Estimates of risk level in annual frequencies and PLL values are given for each platform type. The paper concludes that:The risk connected to marine operations and structures give a significant contribution to the total risk. The historical risk to marine operations and structures is significant higher than the results from risk analyses. Neither component nor system based reliability analyses of structures give adequate descriptions of the real risk connected to structures. Human errors are probably the dominating cause of accidents connected to structural failure. INTRODUCTION This work was initiated as a part of the project to evaluate the development of the safety on the Norwegian Continental shelf. We have counted the number of incidents related to 24 different indicators during the period 1996–2000. A safety index for the Norwegian Continental shelf is calculated. To get one index the different types of incidents (as fire, kicks, collisions and major cracks in structures) have to be given a weight based on its relative importance to the fatality risk. Vinnem et al (2001) give more details and conclusions of the project. In Norwegian risk analyses structural failure turn up with an insignificant contribution to the risk in the industry. The risk analyses are normally giving results in accordance with reliability analyses, dealing only with intrinsic and inherent uncertainty.
Abstract Many units in the semi-submersible fleet have now reached such an age thattheir documented, minimum design fatigue life is either already exceeded, orwill, in the very near future be exceeded. It is anticipated that these units will continue in their existing mode ofoperation for some time to come, and further, that some of these units will beconsidered as candidates for production units for benign waters, with theassociated requirement for extended lifetimes. In the case of an existing unitcontinuing in the same mode of operation, the owner may require to document, toa potential client, or national authority, that the fatigue life of the unit issatisfactory. In the case of a unit being converted the requirement for suchdocumentation will additionally be required by the Classification Society andthe method described in this may be one way to document extended fatigue lifefor these units. In order to document satisfactory fatigue resistance it may therefore benecessary to undertake detailed evaluation of the fatigue reliability of theunits. One, or a combination, of the following detailed evaluations might beconsideredEvaluate the original design utilisation factors. Evaluate the operating history of the unit. Perform detailed fatigue evaluation of specified joints. Reliability analysis This paper document best estimate levels of reliability of a typical‘old-age’ semi-submersible and demonstrate how the reliability varies whenClassification inspection routines are included in the analysis. In-servicedamage statistics (database) is utilised to identify the location that is mostcritical with respect to fatigue. Overview from the reported in-serviceinspection findings is given. In addition to detailed spectral analysis, combined with a detailed FEanalysis of a critical joint, a reliability analysis has been undertaken inorder to evaluate if the probability of fatigue failure of the considered jointis at an acceptably level. The reliability evaluation accounts explicitly forprobability updating based upon information obtained from in-serviceinspection.
Simulation Database on Structural Reliabilities of Jack-Up Platforms
Liu, Xu (Sinopec Shanghai offshore Oil & Gas Company) | Feng, Qin (Sinopec Shanghai offshore Oil & Gas Company) | Li, Chenguang (Sinopec Shanghai offshore Oil & Gas Company) | Qian, Yalin (Sinopec Shanghai offshore Oil & Gas Company) | Wang, Qing (Sinopec Shanghai offshore Oil & Gas Company)
ABSTRACT The incidents of jack-up platforms could unpredictably occur during drilling or production operations and hence result in some damages of the structures. No matter what kinds of failures of the structures are caused, it is very important to estimate if the jack-up platforms are able to be qualified for operations, and therefore what necessary procedures should be applied immediately. The paper proposes a method to quickly evaluate integrities of the jack-up platforms for the subsequent operations. A typical Sinopec jack-up platform "Kantan II" is selected to perform the structural analysis and the follow up calculations of the structural reliabilities. A number of scenarios of the structural failures on different members of the platform are assumed for ultimate strength pushover analyses to obtain all the required results of Reserved Strength Ratios (RSR), and then the results are utilized for the corresponding computations of the reliabilities in accordance with the acceptable criteria by industry. The criteria of the structural reliability are introduced to assess if the damaged structures are still capable of operations. Once the simulation database is established, it is convenient that quick appraisals of the structural capabilities and safety issues can be carried out if the incidents of the jack-up platforms occur during the operations. INTRODUCTION In offshore oil and gas industry, the mobile jack-up platforms are widely used for the drilling and production operations of the oil and gas fields in the shallow water. However, the incidents caused by harsh environmental loading , geotechnical conditions or operation cases could unpredictably take place during the operations, and the structural failures could be usually caused. Safety of the operations for the platforms must be the first priority issue in appraisal of capabilities of the platforms. In this case, it is very important to assess if the platforms are robust enough, and if no further failures, in particular, no fundamental collapse of the structures could occur. In order to develop a useful approach for quick assessment before the subsequent operations, a novel method by means of combination of ultimate strength push-over structural analyses (Hellan, 1995) and reliability calculations has been proposed for practical operation purpose.
- Asia > China (0.50)
- Europe (0.49)
- North America > United States (0.46)
Failure analysis is the cornerstone of asset management via life-cycle costs optimizations. Knowledge graphs are semantic nets that are the next level of database technology. Machine learning (ML) is a field of inquiry devoted to understanding and building methods that “learn”, that is, methods that leverage data to improve performance on some set of tasks. We propose to structure the machine learning data into knowledge graphs to foster advanced failure analysis leveraging optimum life-cycle costs where costs are considered in the largest possible sense including the cost of human life preservation (safety) and the cost (impact) on the environment.
- Europe > Netherlands (0.47)
- North America > United States > California (0.28)
- Transportation > Marine (1.00)
- Energy > Oil & Gas (1.00)
- Transportation > Freight & Logistics Services > Shipping (0.46)