Knowledge-based decision support in oil well drilling: combining general and case-specific knowledge for problem solving

Authors:
Pål Skallel;Agnar Aamodt
Affiliations:
Norwegian University of Science and Technology, Trondheim, Norway and Department of Petroleum Engineering and Geophysics;Norwegian University of Science and Technology, Trondheim, Norway and Department of Computer and Information Science
Venue:
Intelligent information processing II
Year:
2004

Citing 7
Cited 2

The componential framework and its role in reusability

Second generation expert systems
Knowledge engineering and management: the CommonKADS methodology

Knowledge engineering and management: the CommonKADS methodology
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology

Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology
Representation of Failure Context for Diagnosis of Technical Applications

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Representing Temporal Knowledge for Case-Based Prediction

ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
An evaluation of the usefulness of case-based explanation

ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Improved efficiency of oil well drilling through case based reasoning

PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence

Fault detection and other time series opportunities in the petroleum industry

Neurocomputing
Plan recommendation for well engineering

IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Oil well drilling is a complex process which frequently is leading to operational problems. The process generates huge amounts of data. In order to deal with the complexity of the problems addressed, and the large number of parameters involved, our approach extends a pure case-based reasoning method with reasoning within a model of general domain knowledge. The general knowledge makes the system less vulnerable for syntactical variations that do not reflect semantically differences, by serving as explanatory support for case retrieval and reuse. A tool, called TrollCreek, has been developed. It is shown how the combined reasoning method enables focused decision support for fault diagnosis and prediction of potential unwanted events in this domain.