Development of an Intelligent Distributed Management System for Automated Wells (SGPA)

Abstract

Abstract The differences between foreseen results in lift well project related to the obtained in production phase increases preventing and correcting actions necessary to optimize well production. The larger number of wells and its analysis complexity don't allow to conduct the cluster of actions entirely. To help in this task, the automation of petroleum wells has grown expressively. Over 700 wells in Brazil, at Bahia State, are provided with PLCs using A.I. techniques for their control. This sort of automation generates an expressive amount of data that can be used to optimize the lift systems production. Besides this, it also allows local control and remote observation of many wells in real time basis. This paper presents an approach for an automated lift well management system, called SGPA. SGPA supports diagnosing and proposes solutions to optimize automated wells production. SGPA focus mainly on Rod-Pump wells management. However, other lift systems can be included as well. SGPA operates under three main subsystems:Data consistency module, makes the acquisition and consistency of information from different sources. This information is treated and shown in a friendly interface, and it is used in other subsystems.Dynamometric Cards apprenticeships module, allows the user to generate and train the system understanding of the dynamometric cards pattern behavior. These patterns can be used by the PLC's which control the well, and by the SGPA pattern recognition module to support the well diagnose and improve the well lift.Knowledge module that makes well analysis. The knowledge subsystem determine symptoms, makes diagnostics and adds intelligence for the well optimizing and correcting actions. The knowledge representation uses Symbolic Neural Networks (SNN) and Fuzzy Logic (FL) for its constructions and apprenticeship and evolution. It is understood that lift systems automation comes to be a complex system that distinguishes from other traditional approaches by successfully dealing with huge amount of information to cope its mission, despite the high level of empiricism and apparently erratic behavior due to well peculiarities, demanding rigorous status observation. Such reality and the present technology state-of-art justify SGPA development. Introduction Oil wells have two characteristic phases in it's production life, design and follow-up. In the design phase equipment are calculated and specified based in estimated data. In the follow-up phase estimated values are compared to measured values and design corrections should be made. In this last phase, design optimization should be implemented to reduce costs, increase production and raise the mean time between failures (MTBF). The equipment design based in poor data leads to an oversized equipment and to the necessity of redesigning the lifting system based in the measured data. This demands the analysis of a huge amount of well data, that become known as the time goes by. Usually the necessity of well servicing happens to happen in an unexpected day, where this analysis should be made in a few hours. The huge amount of data and analysis complexity causes the lift system redesign to be made only in part. Each lift system has different behavior due to different well characteristics what makes the use of generic solution a difficult task. These facts causes the lift system efficiency to be decreased by constants production stops without no easy reason identification.