The Electrical Submersible Pumps as a mean of oil production has an increasing importance in the oil fields operations. The need for an artificial lift and particularly the ESP is increasing day after day. The Operating companies who depend of ESP as a major production approach need reliable systems to manage the economics and cost of production, especially in the changing market of oil and gas.
ESP reliability is a major factor that influence decisions among other related factors such as cost, delivery, etc. It is common to use central tendency parameters such as average run life, and mean time between failures, to quantify or estimate ESP reliability, or to compare between different ESP performances, but those parameters doesn't really measure the reliability. We aim through this study to provide a simple statistical method used in many areas of other production industries as a methodology to measure the reliability of ESP systems, and to predict the run life in future.
The analysis will figure out statistically two aspects: first, the types of the failures: infant mortality, constant failure rate, and wear out failures. Second the characteristics run life which is the run life where 63% of the population already failed.
Based on the Weibull data analysis, the study built a prediction model to determine the reliability of an ESP group at a certain time with a certain level of confidence. Likewise, the model can predict how much percentage will fail by a certain time, and these valuable outcomes is utilized to appropriately plan and budget for pump repair activities & related requirements of replacement equipment and spare parts.
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Life Data Analysis Reference - ReliaSoft Corporation, Worldwide Headquarters 1450 South Eastside Loop Tucson, Arizona 85710-6703, USA http://www.ReliaSoft.com