Abstract
This paper describes the implementation of a methodology for classification of artificial lift system failures and adding a commonly used root cause failure classification from C-Fer (RIFT). The subject field is the oldest Colombian oilfield, which was discovered in 1918, and was produced by primary recovery, then by gas injection as secondary recovery; in the 80's the water flooding project started and finally, in 2006 optimization of water flooding took place increasing production from 4,500 bopd up to 40,000 bopd. Conditions are challenging for all types of artificial lift systems, including sand production, CO2 corrosion, H2S presence, high water cuts and unstable injection conditions.
This methodology was applied to different artificial lift systems, such as: Beam Pump (BP), Progressive Cavity Pumps (PCP), Electrical Submersible Pumping Systems (ESP) and Electrical Submersible Progressive Cavity Pumping Systems (ESPCP). The starting point was the definition or limitation of the boundaries of every system, since some of them use rods for power transmission and others use a power cable. Then, every job in the well was defined as Failure, Failure No-ALS or No Failure in order to classify and look for ways to improve run life and decrease failures by implementing action plans with operations, engineering, and suppliers among others.
The addition of root cause failure classification helped to refine the action plans in order to solve the issues. In some cases, it was necessary to customize RIFT classification in order to track local issues; such as: unstable injection wells, drilling operations in the same well location, etc (some examples are included).
Included in this methodology, some KPIs: failure index, indirect failure index, pulling index, recurrence index, average run life, average run time were used. These KPIs helped track performance improvement in the last two years, getting excellent results.
