DOWNHOLE CEMENT EVALUATION USING AN ARTIFICIAL NEURAL NETWORK

• Main Authors: Hu, Yike, Guo, Weijun
• Format: Patent
• Language: English
• 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

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.