DOWNHOLE CEMENT EVALUATION USING AN ARTIFICIAL NEURAL NETWORK

Evaluation of borehole annulus cement quality is performed by an artificial neural network configured to estimate one or more cement attributes based on a radiation response of the annulus cement. A plurality of attributes indicative of quality of cement in the annulus can be estimated or derived ba...

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Bibliographic Details
Main Authors Hu, Yike, Guo, Weijun
Format Patent
LanguageEnglish
Published 10.01.2019
Subjects
DETECTING MASSES OR OBJECTS
EARTH DRILLING
EARTH DRILLING, e.g. DEEP DRILLING
FIXED CONSTRUCTIONS
GEOPHYSICS
GRAVITATIONAL MEASUREMENTS
MEASURING
MINING
OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR ASLURRY OF MINERALS FROM WELLS
PHYSICS
TESTING
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Summary:Evaluation of borehole annulus cement quality is performed by an artificial neural network configured to estimate one or more cement attributes based on a radiation response of the annulus cement. A plurality of attributes indicative of quality of cement in the annulus can be estimated or derived based on gamma radiation response information (such as a gamma spectrum of the annulus cement). The artificial neural network is trained to perform the estimation by provision to the artificial neural network of training data from multiple example boreholes. The training data can include empirical data and/or simulation data.
Bibliography:Application Number: US201616066502