Model-based early gas kick and well loss detection

Ala E. Omrani, Matthew A. Franchek, Karolos Grigoriadis, Reza Tafreshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Drilled well control is a process that incorporates the assessment of well status through the monitoring of its physical parameters. This management allows the detection of well anomalies such as gas kick and mud loss. In this paper is developed a gas kick/ well loss early detection model that determines the well condition and early predicts possible anomaly. The developed model insures the system safety along the drilling operation through the early prediction of gas kick influx or mud loss using a model-based control system solution. A reduced order model is derived to predict the mud pressure and flow rate responses for given real time pre-filtered measurements. The model sensitivity to the well anomalies is captured through its coefficients variations permitting a real time evaluation of well status by monitoring the model coefficients location. The obtained results prove that the presented solution is capable of detecting a gas kick before the gas influx reaches the surface in contrast with the widely used delta flow method capturing the kick once the gas is on the floor level.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1228-1233
Number of pages6
ISBN (Electronic)9781509020652
DOIs
StatePublished - Sep 26 2016
Event2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016 - Banff, Canada
Duration: Jul 12 2016Jul 15 2016

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2016-September

Other

Other2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
CountryCanada
CityBanff
Period7/12/167/15/16

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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