MACHINE LEARNING APPROACH FOR IDENTIFYING MUD AND FORMATION PARAMETERS BASED ON MEASUREMENTS MADE BY AN ELECTROMAGNETIC IMAGER TOOL

• Main Authors: GUNER, Baris, Samson, Etienne M, Fouda, Ahmed E
• Format: Patent
• Language: English
• Published: 21.01.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; 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

Aspects of the subject technology relate to systems and methods for identifying values of mud and formation parameters based on measurements gathered by an electromagnetic imager tool through machine learning. One or more regression functions that model mud and formation parameters capable of being identified through an electromagnetic imager tool as a function of possible tool measurements of the electromagnetic imager tool can be generated using a known dataset associated with the electromagnetic imager tool. One or more tool measurements obtained by the electromagnetic imager tool operating to log a wellbore can be gathered. As follows, one or more values of the mud and formation parameters can be identified by applying the one or more regression functions to the one or more tool measurements.