TY - THES

T1 - Early Stuck Pipe Detection based on Real-Time Data Analysis

AU - Msahli, Ahmed

N1 - embargoed until 06-09-2020

PY - 2017

Y1 - 2017

N2 - This research looks at the work previously done by other scholars regarding pipe sticking prediction, especially the ones using real-time data, then goes on to prove it possible to predict impending sticking events using real-time and simulated data. An algorithm is created based on case based reasoning and improved methods from previous work. This algorithm is then tested on historical real-time data to come to the conclusion that it can predict pipe sticking. This work sheds the light on the potential developments in drilling towards full automation and better economical practices.

AB - This research looks at the work previously done by other scholars regarding pipe sticking prediction, especially the ones using real-time data, then goes on to prove it possible to predict impending sticking events using real-time and simulated data. An algorithm is created based on case based reasoning and improved methods from previous work. This algorithm is then tested on historical real-time data to come to the conclusion that it can predict pipe sticking. This work sheds the light on the potential developments in drilling towards full automation and better economical practices.

KW - pipe sticking

KW - drilling

KW - real time

KW - case based reasoning

KW - CBR

KW - drilling problems

KW - prediction

KW - Stuck-Pipe

KW - Drilling

KW - Case Based Reasoning

KW - CBR

KW - Automatisierung

M3 - Master's Thesis

ER -