Dual electric submersible pump (ESP) systems are an attractive option for costly high-intervention wells because they provide greater assurance for uninterrupted operations. In dual ESP systems, a second ESP serves as a backup in the event that the active ESP fails. The backup ESP, however, can spend a significant amount of its lifetime in a dormant state, making the assessment of its reliability more difficult. Newer ESP systems used in deepwater, high-pressure, high-temperature (HPHT) environments are more sophisticated and must have longer service lives. However, these systems do not receive preventive maintenance and are not inspected for component deterioration after deployment, further complicating dormant reliability estimates. Even though its operating failure rate may be substantially greater than its non-operating failure rate under the same well conditions, the amount of time that the backup ESP remains dormant could be longer than five years, making dormancy a major factor to consider when estimating a mature dual ESP system's reliability and service life.
Because of their complexity and potential for prolonged dormancy throughout operations, dual ESP systems have an inherent need for higher dormant reliability. Developing an approach for assessing dormant reliability as part of the testing and evaluation process would help to meet this requirement. This approach should accurately estimate the effects of dormancy on operational effectiveness. Operational testing analyses must identify the failures that directly affect operational capability.
In this paper we present the framework for developing a methodology to estimate dormancy effects on the reliability of deepwater dual ESP systems, and models to predict dormancy effects. Actual measurement of dormant reliability is a long-term process that includes expensive real-life operational testing.
Methodologies for projecting system operational reliability at the component level are applied to dormant reliability analyses, and experiences from the field are iteratively applied toward improving the process.