Schlumberger Technology Corporation

Salma Benslimane

Josselin Kherroubi

Vassilis Varveropoulos

Thanh Nhan Nguyen

Laurent Laval

Ana Escobar

Remi Robutel

Shiqiang Wen

Weijia Du

Kristina Prokopetc

Systems and methods presented herein include a cable spooling system that includes a drum configured to rotate about a central axis of the drum to wind and unwind a cable onto and from the drum as the drum rotates. The cable spooling system also includes one or more cameras configured to capture images of the drum as it rotates. The cable spooling system further includes one or more markers disposed on one or more flanges of the drum. The one or more markers are configured to be in view of the one or more cameras during a portion of rotation of the drum. In addition, the cable spooling system includes a data processing and control system configured to estimate an angular velocity of the drum using the images captured by the one or more cameras.

Image-based cable spooling drum speed estimation

A method, sensor, and non-transitory computer-readable storage medium are provided for estimating actual amplitudes of a waveform. A machine learning model may be trained for an embedded system of a first three-axes sensor having a limited range to estimate the actual amplitudes of a waveform that saturates the first three-axes sensor in a direction of one of the three axes. The embedded system acquires a second waveform during use of a tool including the first three-axes sensor. The second waveform that occurs after a second waveform producing event is isolated. The embedded system extracts a multi-dimensional feature from the isolated second waveform and estimates, using the machine learning model, the actual amplitudes of the second waveform based on the extracted multi-dimensional feature.

Embedded artificial intelligence augmented sensors

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Menlo Park, CA, United States of America

Salma Benslimane