Method for automatic control and positioning of autonomous downhole tools

Patent metadata

Field	Value
Publication number	US-9328578-B2
Application number	US-201113989726-A
Country	US
Kind code	B2
Filing date	Nov 17, 2011
Priority date	Dec 17, 2010
Publication date	May 3, 2016
Grant date	May 3, 2016

Abstract

Methods and apparatus for actuating a downhole tool in wellbore includes acquiring a CCL data set or log from the wellbore that correlates recorded magnetic signals with measured depth, and selects a location within the wellbore for actuation of a wellbore device. The CCL log is then downloaded into an autonomous tool. The tool is programmed to sense collars as a function of time, thereby providing a second CCL log. The autonomous tool also matches sensed collars with physical signature from the first CCL log and then self-actuates the wellbore device at the selected location based upon a correlation of the first and second CCL logs.

First claim

What is claimed is:

1. A method of actuating a downhole tool in a wellbore, the wellbore having casing collars that form a physical signature for the wellbore, comprising:
acquiring a CCL data set from the wellbore, the CCL data set correlating recorded magnetic signals with measured depth, thereby forming a first CCL log for the wellbore;
selecting a location within the wellbore for actuation of a wellbore device;
downloading the first CCL log into a processor on-board the downhole tool;
deploying the downhole tool into the wellbore such that the downhole tool traverses casing collars, the downhole tool comprising the processor, a casing collar locator, and an actuatable wellbore device;
wherein the processor is programmed to:
continuously record magnetic signals as the downhole tool traverses the casing collars, forming a second CCL log;
transform the recorded magnetic signals of the second CCL log by applying a moving windowed statistical analysis, wherein applying a moving windowed statistical analysis comprises (i) defining a pattern window size (W′) for sets of magnetic signal values, and (ii) computing a moving mean m(t+1) for the magnetic signal values over time;
incrementally compare the transformed second CCL log with the first CCL log during deployment of the downhole tool to correlate values indicative of casing collar locations;
recognize the selected location in the wellbore; and
send an actuation signal to the actuatable wellbore device when the processor has recognized the selected location; and

sending the actuation signal to actuate the downhole tool.

2. The method of claim 1 , wherein:
the method further comprises transforming the CCL data set for the first CCL log by applying a moving windowed statistical analysis;
downloading the first CCL log into a processor comprises downloading the first transformed CCL log into the processor on-board the downhole tool; and
the processor incrementally compares the second transformed CCL log with the first transformed CCL log to correlate values indicative of casing collar locations.

3. The method of claim 1 , wherein:
the first CCL log represents a depth series;
the second CCL log represents a time series; and
incrementally comparing the second transformed CCL log with the first CCL log uses a collar matching pattern algorithm to compare and correlate individual peaks representing casing collar locations.

4. The method of claim 3 , wherein the collar matching pattern algorithm comprises:
establishing baseline references for depth from the first CCL log, and for time from the transformed second CCL log;
estimating an initial velocity v 1 of the autonomous tool;
updating a collar matching index from a last confirmed collar match, indexed to be d k for the depth, and t l for the time;
determining a next match of casing collars using an iterative process of convergence;
updating the collar matching index based on a best computed match; and
repeating the iterative process.

5. The method of claim 4 , wherein estimating an initial velocity v 1 of the autonomous tool comprises:
assuming a first depth d 1 matches a first time t 1 ;
assuming a second depth d 2 matches a second time t 2 ; and
calculating the estimated initial velocity using the following equation:

v
1

=

d
2

-

d
1

t
2

-

t
1

.

6. The method of claim 4 , wherein the iterative process of convergence comprises the following steps:
(1) If

v
=

(

d

k
+
1

-

d
k

v

l
+
1

-

v
l

)

satisfies (1−e)u<v<(1+e)u, match d k+1 with t l+1 ;
(2) Else, if (d k+1 −d k )<v(t l+1 −t l ), delete d k+1 from the index and reduce all later indices by 1 so that the next depth number in sequence is d k+1 , and return to step (1);
(3) Else, if (d k+1 −d k )>v(t l+1 −t l ), delete d l+1 from the index and reduce all later indices by 1 so that a next time number in sequence is t l+1 , and return to step (1);
wherein
u represents a last confirmed velocity estimate; and
e represents a margin of error.

7. The method of claim 6 , wherein the margin of error e is no greater than 10 percent.

8. The method of claim 1 , wherein:
the moving mean m(t+1) is in vector form and represents a mean of magnetic signal values for a pattern window (W); and
applying a moving windowed statistical analysis further comprises:
defining a memory parameter μ for the windowed statistical analysis; and
calculating a moving covariance matrix Σ(t+1) for the magnetic signal values over time.

9. The method of claim 8 , wherein:
the moving mean m(t+1) is an exponentially weighted moving average for the magnetic signal values for a pattern window (W); and
calculating a moving mean m(t+1) for the magnetic signal values is done according to the following equation:
m ( t+ 1)=μ y ( t+ 1)+(1−μ) m ( t )

where
y(t+1) is a collection of magnetic signal values in a most recent pattern window (W+1), and
m(t) is the mean of magnetic signal values for a preceding pattern window (W).

10. The method according to claim 9 , wherein calculating a moving covariance matrix Σ(t+1) for the magnetic signal values comprises:
computing an exponentially weighted moving second moment A(t+1) for the magnetic signal values in a most recent pattern window (W+1); and
computing the moving covariance matrix Σ(t+1) based upon the exponentially weighted second moment A(t+1).

11. The method of claim 10 , further comprising:
defining m(W)=y(W) when the downhole tool is deployed,
where
m(W) is the mean m(t) for a first pattern window (W), and
y(W) is a transpose for m(W);

and
defining y(W)=[x(1), x(2), . . . x(W)] T when the downhole tool is deployed,
where
x(1), x(2), . . . x(W) represent magnetic signal values within a pattern window (W).

12. The method of claim 10 , wherein:
computing an exponentially weighted second moment A(t+1) is done according to the following equation:
A ( t+ 1)=μ y ( t+ 1)×[ y ( t+ 1) T +(1−μ) A ( t )

and
computing the moving covariance matrix Σ(t+1) is done according to the fol

Assignees

• Kumaran Krishnan
• Subrahmanya Niranjan A
• Entchev Pavlin B
• Tolman Randy C
• Angeles Boza Renzo Moises
• Exxonmobil Upstream Res Co

Inventors

• Kumaran Krishnan
• Subrahmanya Niranjan A
• Entchev Pavlin B
• Tolman Randy C
• Angeles Boza Renzo Moises

Classifications

• E21B47/092

  by detecting magnetic anomalies · CPC title

• E21B23/00 Primary

  Apparatus for displacing, setting, locking, releasing or removing tools, packers or the like in boreholes or wells (setting of casings, screens or liners E21B43/10) · CPC title

• E21B43/116

  Gun or shaped-charge perforators · CPC title

• E21B41/00

  Equipment or details not covered by groups E21B15/00 - E21B40/00 · CPC title

• E21B47/0905

  Fixed Constructions · mapped topic