Methods and systems for controlling operation of wireline cable spooling equipment
AssigneeSchlumberger Technology Corporation
InventorsMuhannad Abuhaikal, Christopher Bogath, Vassilis Varveropoulos, Tianxiang Su
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Abstract
A method of controlling operation of equipment that spools cable on and off a rotatable drum, in one or more embodiments, includes obtaining video data of a position of the cable on the rotatable drum. The method can also include feeding data into a trained artificial neural network and processing the data fed into the trained artificial neural network to determine at least one of a calculated position of the cable on the rotatable drum, a calculated fleet angle, or both. The method can also include actuating the rotatable drum to one of spool cable on and off the rotatable drum.
Claims13
A method of controlling operation of equipment that spools cable on and off a rotatable drum, the method comprising:
- obtaining video data of the cable on the rotatable drum;
- feeding data into a trained artificial neural network;
- processing the data fed into the trained artificial neural network to determine a cable position, the cable position being a distance between the cable and a flange of the rotatable drum, wherein the trained artificial neural network is trained using a ground truth cable position calculated using a rotation of the rotatable drum, a cable layer direction, and an initial cable position, and wherein the trained artificial neural network applies a Bayesian dropout method to quantify model uncertainty;
- and
- actuating the rotatable drum to spool cable on or off the rotatable drum.
A system comprising:
- a cable;
- a rotatable drum configured to spool the cable on to and off of the rotatable drum;
- a video camera system configured to obtain video data of a position of the cable on the rotatable drum;
- and
- a trained artificial neural network to process video data of the drum to infer a cable position on the drum, the cable position being a distance between the cable and a flange of the rotatable drum, wherein the trained artificial neural network is trained using a ground truth cable position calculated using a rotation of the rotatable drum, a cable layer direction, and an initial cable position, and wherein the trained artificial neural network applies a Bayesian dropout method to quantify model uncertainty.
Dependencies are inferred from the claim text.