Recommendation of Counteractions for Prevention of Critical Events in Sub-Surface Drilling Environments

Martin Winter, Felix Riedel, Felix Lee, Rudolf K. Fruhwirth, Florian Kronsteiner, Herwig Zeiner and Heribert Vallant
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Martin Winter: DIGITAL - Institute for Information and Communication Technologies, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
Felix Riedel: Department of Information Management and Production Control, System Technologies and Image Exploitation (IOSB), Fraunhofer Institute of Optronics, Karlsruhe, Germany
Felix Lee: DIGITAL - Institute for Information and Communication Technologies, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
Rudolf K. Fruhwirth: TDE Thonhauser Data Engineering GmbH, Leoben, Austria
Florian Kronsteiner: TDE Thonhauser Data Engineering GmbH, Leoben, Austria
Herwig Zeiner: DIGITAL - Institute for Information and Communication Technologies, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
Heribert Vallant: DIGITAL - Institute for Information and Communication Technologies, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria

International Journal of Decision Support System Technology (IJDSST), 2013, vol. 5, issue 3, 12-30

Abstract: Sub-Surface Drilling is the process of making boreholes into the Earth, which can reach depths of many kilometers. One of the major purposes of such boreholes is the exploration of oil or gas bearing formations with the goal to recover the content of such reservoirs. Problems in drilling operations pose serious risks for the crew and the environment and can cause significant financial losses. Critical events usually do not arise abruptly, but develop over time before they escalate. In this work, the authors present a system that integrates sensor data and machine learning algorithms into a decision support system (DSS), thus helping to avoid critical events by monitoring and recommending preventive measures. The authors describe how the DSS is implemented as a distributed system and how data-driven decision support processes are implemented and integrated into the system. The DSS detects drilling operations by recognizing temporal patterns in the sensor data and uses a combination of detected operational rig-states and sensor data to predict and recommend preventive measures for the stuck pipe problem. The sensor data, detection results and predictions are distributed to all stakeholders and displayed in appropriate user interfaces.

Date: 2013
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