THE APPLICATION OF PATTERN RECOGNITION AND MACHINE LEARNING TO DETERMINE CEMENT CHANNELING & BOND QUALITY FROM AZIMUTHAL CEMENT BOND LOGS

Abstract

Cement bond log interpretation methods consist of human pattern recognition and evaluation of the quality of the downhole isolation. Typically, a log interpreter compares acquisition data to their predefined classifications of cement bond quality. This paper outlines a complementary technique of intelligent cement evaluation and the implementation of the analysis of cement evaluation data by utilizing automatic pattern matching and machine learning. The proposed method is capable of defining bond quality across multiple distinct subclassification through analysis of image data using pattern recognition. Libraries of real log responses are used as comparisons to input data, and additionally may be supplemented with synthetic data. Using machine learning and image-based pattern recognition, the bond quality is classified into succinct categories to determine the presence of channeling. Successful classifications of the input data can then be added to the libraries, thus improving future analysis through an iterative process. The system uses the outputs of a conventional azimuthal ultrasonic scanning cement evaluation log and 5-ft CBL waveform to conclude a cement bond interpretation. The 5-ft CBL waveform is an optional addition to the processand improves the interpretation. The system searches forsimilarities between the acquisition data and thatcontained in the library. These similarities are comparedto evaluate the bonding. The process is described in two parts: i) image collection and library classification and ii) pattern recognition and interpretation. The former is the process of generating a readable library of reference data from historical cement evaluation logs and laboratory measurements and the latter is the machine learning and comparison method. Example results are shown with good correlations between automated analysis and interpreter analysis. The system is shown to be particularly capable at the automated identification of channeling of varying sizes, something which would be a challenge when using only the scalar curve representation of azimuthal data. Previously published methodologies for automated classification of bond quality typically utilize scaler data whereas this approach utilizes image-based pattern recognition for automated, learning and intelligent cement evaluation (ALICE). A discussion is presented on the limitations and merits of the ALICE process which include quality control, the removal of analyst bias during interpretation, and the fact that such a system will continually improve in accuracy through supervised training.