Title :
Classification of Petroleum Well Drilling Operations Using Support Vector Machine (SVM)
Author :
Serapião, Adriane B S ; Tavares, Rogério M. ; Mendes, José Ricardo P ; Guilherme, Ivan R.
Author_Institution :
UNESP/IGCE/DEMAC, Sao Paulo State Univ., Rio Claro
fDate :
Nov. 28 2006-Dec. 1 2006
Abstract :
During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a support vector machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert.
Keywords :
oil drilling; pattern classification; petroleum industry; process monitoring; support vector machines; well logging; drilling process monitoring; instrumentation system; mud-logging system; petroleum well drilling operation classification; rig; support vector machine; Computational intelligence; Condition monitoring; Cost function; Drilling; Instruments; Mechanical sensors; Petroleum; Production; Support vector machine classification; Support vector machines;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7695-2731-0
DOI :
10.1109/CIMCA.2006.66