Data optimization for the knowledge bases in the oil and gas Monitoring-While-Drilling (MWD) Systems

Abstract

Abstract The ability of a knowledge-oriented system to learn requires the selection and organization of data. The article is devoted to the disclosure of the stages of data preparation for the application of case-based procedures to support decision-making by the operator of the technological process in the field of oil and gas wells drilling. The presented concept allows concentrating expert experience in the subject area and satisfies the criteria of the main sources of knowledge required for the functioning of case-based considerations. The main potential of artificial intelligence to learn is based on semantic dictionaries of the subject area, case databases in the form of knowledge bases, metrics of similarity, adaptation and configuration containers, which are identified as the sources of knowledge. The effectiveness of using computer simulators as a platform for experimental research of new models of complex technological processes is estimated.