Use of surface and downhole measurements to identify operational anomalies

1. A method for preventing operational disruptions in hydrocarbon extraction equipment, the method comprising:

receiving surface data from one or more surface sensors, wherein the surface data comprises measurements associated with one or more surface equipment devices;

receiving downhole data from one or more downhole sensors, wherein the downhole data comprises measurements associated with operation of one or more downhole equipment devices; and

analyzing a combination of the surface data and the downhole data to determine if an operational anomaly is detected with respect to the one or more surface equipment devices or the one or more downhole equipment devices, wherein analyzing the surface data and the downhole data further comprises:

calculating a failure probability of the one or more surface equipment devices or the one or more downhole equipment devices based on the combination of the surface data and the downhole data, as well as data pertaining to historic anomalies for one or more equipment types; and

outputting data identifying at least one piece of equipment along with an indicator of an anomaly type, the anomaly type comprising an impending failure of the at least one piece of equipment.

2. The method of claim 1 , further comprising:

collecting operational data for the one or more surface equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more surface equipment devices.

3. The method of claim 1 , further comprising:

collecting operational data for the one or more downhole equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more downhole equipment devices.

4. The method of claim 1 , further comprising:

generating a warning notification if the operational anomaly is detected.

5. The method of claim 1 , further comprising:

automatically modifying operation of at least one surface equipment device if the operational anomaly is detected.

6. The method of claim 1 , further comprising:

automatically modifying operation of at least one downhole equipment device if the operational anomaly is detected.

7. The method of claim 1 , wherein calculating comprises providing at least a portion of the surface data and the downhole data to a machine learning model.

8. A system for preventing operational disruptions in hydrocarbon extraction equipment, the system comprising:

one or more processors; and

a non-transitory memory coupled to the one or more processors, wherein the memory comprises instructions configured to cause the processors to perform operations for:

receiving surface data from one or more surface sensors, wherein the surface data comprises measurements associated with one or more surface equipment devices;

receiving downhole data from one or more downhole sensors, wherein the downhole data comprises measurements associated with operation of one or more downhole equipment devices; and

analyzing a combination of the surface data and the downhole data to determine if an operational anomaly is detected with respect to the one or more surface equipment devices or the one or more downhole equipment devices, wherein analyzing the surface data and the downhole data further comprises:

calculating a failure probability of the one or more surface equipment devices or the one or more downhole equipment devices based on the combination of the surface data and the downhole data, as well as data pertaining to historic anomalies for one or more equipment types; and

outputting data identifying at least one piece of equipment along with an indicator of an anomaly type, the anomaly type comprising an impending failure of the at least one piece of equipment.

9. The system of claim 8 , wherein the instructions are further configured to cause the processors to perform operations for:

collecting operational data for the one or more surface equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more surface equipment devices.

10. The system of claim 8 , wherein the instructions are further configured to cause the processors to perform operations for:

collecting operational data for the one or more downhole equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more downhole equipment devices.

11. The system of claim 8 , wherein the instructions are further configured to cause the processors to perform operations for:

generating a warning notification if the operational anomaly is detected.

12. The system of claim 8 , wherein the instructions are further configured to cause the processors to perform operations for:

automatically modifying operation of at least one surface equipment device if the operational anomaly is detected.

13. The system of claim 8 , wherein the instructions are further configured to cause the processors to perform operations for:

automatically modifying operation of at least one downhole equipment device if the operational anomaly is detected.

14. The system of claim 8 , wherein calculating comprises providing at least a portion of the surface data and the downhole data to a machine learning model.

15. A tangible, non-transitory, computer-readable media having instructions encoded thereon, the instructions, when executed by a processor, are operable to perform operations for:

receiving surface data from one or more surface sensors, wherein the surface data comprises measurements associated with one or more surface equipment devices;

receiving downhole data from one or more downhole sensors, wherein the downhole data comprises measurements associated with operation of one or more downhole equipment devices; and

analyzing a combination of the surface data and the downhole data to determine if an operational anomaly is detected with respect to the one or more surface equipment devices or the one or more downhole equipment devices, wherein analyzing the surface data and the downhole data further comprises:

calculating a failure probability of the one or more surface equipment devices or the one or more downhole equipment devices based on the combination of the surface data and the downhole data, as well as data pertaining to historic anomalies for one or more equipment types; and

outputting data identifying at least one piece of equipment along with an indicator of an anomaly type, the anomaly type comprising an impending failure of the at least one piece of equipment.

16. The tangible, non-transitory, computer-readable media of claim 15 , wherein the instructions are further configured to cause the processors to perform operations for:

collecting operational data for the one or more surface equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more surface equipment devices.

17. The tangible, non-transitory, computer-readable media of claim 15 , wherein the instructions are further configured to cause the processors to perform operations for:

collecting operational data for the one or more downhole equipment devices; and

updating a machine learning model for performing operational anomaly detection using the operational data for the one or more downhole equipment devices.

18. The tangible, non-transitory, computer-readable media of claim 15 , wherein the instructions are further configured to cause the processors to perform operations for:

generating a warning notification if the operational anomaly is detected.

19. The tangible, non-transitory, computer-readable media of claim 15 , wherein the instructions are further configured to cause the processors to perform operations for:

automatically modifying operation of at least one surface equipment device if the operational anomaly is detected.