Abstract: Systems and methods of the present disclosure provide systems and methods related to obtaining, at one or more neural networks, log data from a wellbore and generating, using a multi-head attention layer of the one or more neural networks, a zone of interest based on probability-based weights applied to the log data. The one or more neural networks analyze the log data to infer a downhole characteristic and output an indication of an inference of the downhole characteristic and the zone of interest. Then, a computing system performs an action based at least in part on indication of the inference.

Abstract: A method for estimating a mechanical property of a subterranean formation includes engaging the formation with an engagement assembly deployed on a downhole tool to make engagement measurements while rotating the downhole tool in the wellbore. The mechanical property of the formation may be estimated from the engagement measurements. The mechanical property may include a modulus, a strain profile, or a formation integrity.

Abstract: Systems and methods of the present disclosure provide systems and methods related to obtaining, at one or more neural networks, log data from a wellbore and generating, using a multi-head attention layer of the one or more neural networks, a zone of interest based on probability-based weights applied to the log data. The one or more neural networks analyze the log data to infer a downhole characteristic and output an indication of an inference of the downhole characteristic and the zone of interest. Then, a computing system performs an action based at least in part on indication of the inference.