Well activity management

Patent metadata

Field	Value
Publication number	US-2023103133-A1
Application number	US-202217934596-A
Country	US
Kind code	A1
Filing date	Sep 23, 2022
Priority date	Sep 29, 2021
Publication date	Mar 30, 2023
Grant date	—

Abstract

A method can include collecting sensor data from one or more data sources, determining actual well activity being performed at the rig, determining an estimated well activity for the rig, comparing the actual well activity to expected well activity in a digital well plan; and setting a confidence level for the estimated well activity, where the confidence level can indicate a degree of confidence in an accuracy of the estimated well activity. A method can include collecting parameter(s) from data source(s), determining rig tasks being performed at the rig, determining a well activity for the rig, identifying secondary operations that are occurring at the same time as primary activities, comparing the secondary operations to a digital well plan, and determining whether the secondary operations are occurring at a proper time to support future primary activities.

First claim

1 . A method for performing subterranean operations, the method comprising:
collecting sensor data from one or more data sources, wherein the sensor data is representative of conditions at a rig; based on the sensor data, determining actual rig tasks being performed at the rig; based on the actual rig tasks, determining an estimated well activity for the rig; comparing the actual rig tasks to expected rig tasks in a digital rig plan; based on the comparing, setting a confidence level for the estimated well activity, wherein the confidence level indicates a degree of confidence in an accuracy of the estimated well activity being an actual well activity; and comparing, via a rig controller, the estimated well activity to an expected well activity in a digital well plan.

2 . The method of claim 1 , further comprising requesting user input regarding the confidence level.

3 . The method of claim 2 , further comprising receiving the user input and adjusting the confidence level of the estimated well activity based on the user input.

4 . The method of claim 2 , further comprising;
requesting the user input via a rig controller; receiving the user input from an input device; adjusting the confidence level based on the user input; and altering the estimated well activity to indicate another well activity based on the user input when the confidence level is below a predetermined value.

5 . The method of claim 1 , wherein the confidence level being above or equal to a predetermined value indicates a high level of confidence that the estimated well activity is the actual well activity, and wherein the estimated well activity is a planned well activity, and wherein the planned well activity is an expected well activity according to the digital well plan since the estimated well activity correlates with the expected well activity of the digital well plan.

6 . The method of claim 1 , wherein the confidence level being below a predetermined value indicates a low level of confidence that the estimated well activity is the actual well activity, further comprising requesting user input regarding the confidence level, wherein, based on the user input, the confidence level is adjusted to be above or equal to a predetermined value, wherein the estimated well activity is a planned well activity, and wherein the planned well activity is an expected well activity according to the digital well plan since the estimated well activity correlates with an expected well activity of the digital well plan.

7 . The method of claim 6 , further comprising training a machine learning processor based on the user input to improve results of the machine learning processor.

8 . The method of claim 1 , wherein when the confidence level is above a predetermined value, thereby indicating a high level of confidence that the estimated well activity is an actual well activity, further comprising:
comparing the actual well activity to an expected well activity of the digital well plan; determining that the actual well activity is not the expected well activity of the digital well plan; and identifying the actual well activity as an unplanned well activity.

9 . The method of claim 8 , further comprising:
training a machine learning processor based on the unplanned well activity to improve results of the machine learning processor when generating a future well plan for drilling a future wellbore.

10 . The method of claim 8 , further comprising:
inserting the unplanned well activity into a future digital well plan for drilling a future wellbore, thereby modifying the future digital well plan to include the unplanned well activity.

11 . The method of claim 8 , further comprising:
modifying the digital well plan to handle the unplanned well activity and to produce a modified digital well plan to handle a future unplanned well activity; and continuing to drill a current wellbore based on the modified digital well plan.

12 . The method of claim 1 , wherein when the confidence level is below a predetermined value, thereby indicating a low level of confidence that the estimated well activity is an actual well activity.

13 . The method of claim 12 , further comprising:
requesting user input, via the rig controller, to adjust the confidence level based on a user's awareness of the actual rig tasks; receiving the user input; and adjusting the confidence level based on the user input.

14 . The method of claim 13 , further comprising:
adjusting the confidence level above the predetermined value; comparing the actual well activity to an expected well activity of the digital well plan; determining that the actual well activity is not the expected well activity of the digital well plan; and identifying the actual well activity as an unplanned well activity; and inserting the unplanned well activity into a future digital well plan, thereby modifying the future digital well plan to include the unplanned well activity.

15 . The method of claim 13 , further comprising:
adjusting the confidence level above the predetermined value; comparing the actual well activity to an expected well activity of the digital well plan; determining that the actual well activity is the expected well activity of the digital well plan; identifying the actual well activity as a planned well activity; and monitoring the sensor data to determine when the actual well activity changes.

16 . The method of claim 13 , further comprising:
adjusting the confidence level below the predetermined value; A) monitoring the sensor data; B) determining the actual well activity; C) determining the estimated well activity; D) comparing the actual well activity to the expected well activity in the digital well plan; and E) setting the confidence level for the estimated well activity; and F) repeating operations A-E until the confidence level is above the predetermined value.

17 . A method for performing subterranean operations, the method comprising:
collecting a parameter from one or more data sources, wherein the parameter comprises one or more parameters, and wherein the parameter is representative of at least one condition that affects at least one operation of a rig; based on the one or more parameters, determining rig activities being performed at the rig; based on the rig activities, determining a rig state for the rig; identifying secondary path activities that are occurring at a same time as primary path activities; comparing, via a rig controller, the secondary path activities to a digital rig plan; and based on the comparing, determining whether the secondary path activities are occurring at a proper time to support future primary path activities.

18 . The method of claim 17 , wherein the primary path activities comprise rig activities that directly impact execution of the digital rig plan, and wherein secondary path activities comprise activities that indirectly support execution of the digital rig plan, wherein the rig state of the digital rig plan requires running a drill string into a wellbore, and wherein the primary path activities for the rig state comprise a pipe handler retrieving tubulars from a tubular storage, and a top drive receiving the tubulars from the pipe handler and repeatedly connecting individual tubulars to an upper end of the drill string, thereby extending the drill string into the wellbore.

19 . The method of claim 18 , wherein the secondary path activities comprise:
verifying that a number of tubulars needed to support

Assignees

• Nabors Drilling Tech Usa Inc

Inventors

• Hamzah Mohammad
• Rolong Carlos

Classifications

• G06Q10/063114 Primary

  Status monitoring or status determination for a person or group · CPC title

• G06Q10/06312

  Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling · CPC title