Field vision control framework

1 . A method comprising:

receiving data for a wellsite by an edge framework gateway at the wellsite, wherein the data correspond to equipment operations at the wellsite and wherein the data comprise imagery data acquired by one or more cameras at the wellsite;

performing image recognition on the imagery data via execution of a machine learning model on the edge framework gateway, wherein performing the image recognition comprises:

identifying a type of one or more gauges in the imagery data;

accessing instructional information for the type of the one or more gauges, wherein the instructional information comprises a manual associated with the type of the one or more gauges; and

recognizing content displayed on the one or more gauges in the imagery data using at least the instructional information; and

issuing an instruction based at least in part on the recognized content displayed on the one or more gauges, wherein the instruction comprises a control action to control operation of one or more valves or pumps at the wellsite.

2 . The method of claim 1 , wherein the type of the one or more gauges comprises an analog gauge or a digital gauge, and a gauge model of the analog gauge or digital gauge.

3 . The method of claim 1 , comprising training the machine learning model at least in part by assessing the performing, wherein the machine learning model comprises a trained machine learning model.

4 . The method of claim 1 , comprising contextualizing the recognized content based at least in part on the instructional information.

5 . The method of claim 1 , wherein the recognized content displayed on the one or more gauges is in an alphanumeric format.

6 . The method of claim 5 , comprising transmitting at least a portion of the recognized content to a remote site via a satellite.

7 . The method of claim 6 , comprising rendering the at least a portion of the recognized content to a display with a virtual representation of at least a portion of at least one of the one or more gauges.

8 . The method of claim 1 , comprising comparing the recognized content to human transcribed content for at least one of the one or more gauges.

9 . The method of claim 8 , comprising determining an error rate for the recognized content, an error rate for the human transcribed content or error rates for the recognized content and the human transcribed content.

10 . The method of claim 9 , comprising deciding to increase automation at the wellsite based at least in part on at least one of the error rates.

11 . The method of claim 9 , comprising adjusting a schedule for human presence at the wellsite based at least in part on at least one of the error rates.

12 . The method of claim 1 , wherein the one or more cameras comprise an outdoor camera with a lens wiper.

13 . The method of claim 1 , wherein the one or more cameras comprise an indoor camera.

14 . The method of claim 1 , wherein the type of the one or more gauges comprises an outdoor flow equipment mounted gauge.

15 . The method of claim 1 , wherein the performing image recognition on the imagery data comprises detecting a vibrational movement of at least one of the one or more gauges associated with a gauge error comprising gas entrainment or a hammer effect.

16 . The method of claim 1 , wherein the performing image recognition on the imagery data comprises detecting temperature of at least one piece of equipment at the wellsite.

17 . The method of claim 1 , wherein the one or more cameras are located on one or more drones, and wherein the one or more drones comprise a mobile gateway configured to generate a model of the wellsite and control operation of the one or more valves or pumps at the wellsite.

18 . The method of claim 1 , wherein the imagery data comprises a first subset of imagery data used in the identifying the type of the one or more gauges and a second subset of imagery data used in the recognizing content displayed on the one or more gauges, and wherein the first subset of imagery data and the second subset of imagery data are different.

19 . A system comprising:

a processor;

memory accessible to the processor; and

processor-executable instructions stored in the memory and executable by the processor to instruct the system to:

receive data for a wellsite by an edge framework gateway at the wellsite, wherein the data correspond to equipment operations at the wellsite and wherein the data comprise imagery data acquired by one or more cameras at the wellsite;

perform image recognition on the imagery data via execution of a machine learning model on the edge framework gateway, wherein the processor-executable instructions executable by the processor to perform the image recognition further comprise instructions to:

identify a type of one or more gauges in the imagery data;

access instructional information for the type of the one or more gauges, wherein the instructional information comprises a manual associated with the type of the one or more gauges; and

recognize content displayed on the one or more gauges in the imagery data using at least the instructional information; and

issue an instruction based at least in part on the recognized content displayed on the one or more gauges, wherein the instruction comprises a control action to control operation of one or more valves or pumps at the wellsite.

20 . One or more non-transitory computer-readable media comprising computer-executable instructions executable by a system to instruct the system to:

receive data for a wellsite by an edge framework gateway at the wellsite, wherein the data correspond to equipment operations at the wellsite and wherein the data comprise imagery data acquired by one or more cameras at the wellsite;

perform image recognition on the imagery data via execution of a machine learning model on the edge framework gateway, wherein the computer-executable instructions executable by the system to perform the image recognition further comprise instructions to:

identify a type of one or more gauges in the imagery data, wherein the type of the one or more gauges comprises an analog gauge or a digital gauge and a gauge model of the analog gauge or digital gauge;

access instructional information for the type of the one or more gauges, wherein the instructional information comprises a manual associated with the type of the one or more gauges; and

recognize content displayed on the one or more gauges in the imagery data using at least the instructional information; and

issue an instruction based at least in part on the recognized content displayed on the one or more gauges, wherein the instruction comprises a control action to control operation of one or more valves or pumps at the wellsite.