Project Background

In 1998, several Canadian heavy oil producers issued a Request for Proposals through the Petroleum Technology Alliance of Canada (PTAC), that expressed a need for an industry-funded joint industry project (JIP) that would indentify ways to significantly improve the average run life of PCPs.

The proposal C-FER submitted, which was ultimately selected, recommended taking two different approaches at improving PCP run life. The first approach is to improve PCP run life using existing technologies by adopting best practices. The second approach is to identify areas where PCP run life is limited by the current state of technology and investigate possible alternatives.

Phase I

The work in Phase I, which was completed in April 2003, included a combined field data gathering and synthesis effort on a selected set of representative fields in Alberta and Saskatchewan, Canada, followed by comprehensive analysis of the collected data.

Predominant failure mechanisms were identified in heavy and light oil applications, and the effects of several variables in failure rates were investigated. The Final Report documents conclusions and provides recommendations on how to achieve a reduction in failure rates and an improvement in run life through the implementation of better operating practices.

Phases II - III

Phases II and III focused on collecting data and the development of a web-based Reliability Information and Failure Tracking System (RIFTS) to facilitate the sharing of PCP reliability and operational information among Operators throughout the world. As well, an initial version of the Data Input Sheet (DIS) software tool was developed, along with Data Collection Standards to promote data quality.

Phases IV - VIII

Phases IV to VIII focused on growing the quantity and quality of data in the database, improving the usability of the RIFTS website, and developing the analysis features available in both the DIS and the RIFTS website. More recently, several field level drill-down analyses were conducted using data from the Participant's fields and a PCP Classification Tree tool was developed to help identify analogue fields.