US 10,242,130 B2Granted

System, method and computer program product for wellbore event modeling using rimlier data

AssigneeLandmark Graphics Corporation

InventorsRobello Samuel, Olivier Roger Germain

Life of this patent

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Abstract

A data mining and analysis system which analyzes clusters of outlier data (i.e., rimliers) to detect and/or predict downhole events.

Claims27

Dependent claims

A computer-implemented method to model downhole events, the method comprising:

extracting a dataset from a database, the dataset comprising normal wellbore data and outlier wellbore data;
clustering a plurality of the outlier data into a plurality of clusters;
segregating the plurality of clusters into a high density cluster and a low density cluster, wherein the high density clusters are utilized as a rimlier;
analyzing the rimlier to determine data variables within the rimlier that indicate a downhole event, wherein the analyzing comprises:
determining a Head Rimlier Factor as defined by:
E
h
E
r
=
Σ
-
p
h
⁡
(
x
)
⁢
log
⁡
(
x
)
Σ
-
p
r
⁡
(
x
)
⁢
log
⁡
(
x
)
;
and
determining a Tail Rimlier Factor as defined by:
Et
E
r
=
Σ
-
p
t
⁡
(
x
)
⁢
log
⁡
(
x
)
Σ
-
p
r
⁡
(
x
)
⁢
log
⁡
(
x
)
,
wherein Eh is entropy of head data, ph is probability of the head data, Er is entropy of rimlier data, pr is probability of the rimlier data, Et is entropy of tail data, and pt is probability of the tail data,
wherein the Head Rimlier Factor and the Tail Rimlier Factor are utilized to determine the data variables indicating the downhole event;
and
modeling the downhole event based upon the analysis of the rimlier,
wherein a wellbore is drilled, completed or stimulated in accordance to the modeled downhole events.

76 more elements

A non-transitory computer-readable medium comprising instructions which, when executed by at least one processor, causes the processor to perform the method comprising:

extracting a dataset from a database, the dataset comprising normal wellbore data and outlier wellbore data;
clustering a plurality of the outlier data into a plurality of clusters;
segregating the plurality of clusters into a high density cluster and a low density cluster, wherein the high density clusters are utilized as a rimlier;
analyzing the rimlier to determine data variables within the rimlier that indicate a downhole event, wherein the analyzing comprises:
determining a Head Rimlier Factor as defined by:
E
h
E
r
=
Σ
-
p
h
⁡
(
x
)
⁢
log
⁡
(
x
)
Σ
-
p
r
⁡
(
x
)
⁢
log
⁡
(
x
)
;
and
determining a Tail Rimlier Factor as defined by:
Et
E
r
=
Σ
-
p
t
⁡
(
x
)
⁢
log
⁡
(
x
)
Σ
-
p
r
⁡
(
x
)
⁢
log
⁡
(
x
)
,
wherein Eh is entropy of head data, ph is probability of the head data, Er is entropy of dinner data, pr is probability of the rimlier data, Et is entropy of tail data, and pt is probability of the tail data,
wherein the Head Rimlier Factor and the Tail Rimlier Factor are utilized to determine the data variables indicating the downhole event;
and
modeling the downhole event based upon the analysis of the rimlier,
wherein a wellbore is drilled, completed or stimulated in accordance to the modeled downhole events.

76 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/435,404Filed 5 Nov 2012
Published: US 2015/0234954 A120 Aug 2015
Granted: 26 Mar 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification14 codes

E21B 44/00
Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…
G06F 18/2321
Non-hierarchical techniques: using statistics or function optimisation, e.g. modelling of probability density functions
G06F 18/2433
Relating to the number of classes: single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection

Family

US 2015/0234954 A1Pre-grant publication · published 20 Aug 2015
US 10,242,130 B2This patent

From the US filing history. Foreign family members aren’t listed.

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Facts

Application

14/435,404Filed 5 Nov 2012

Published

US 2015/0234954 A120 Aug 2015

Granted

26 Mar 2019

Term ends

Not estimatedThe source records don’t support an estimate.

Classification14 codes

E21B 44/00
Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…
G06F 18/2321
Non-hierarchical techniques: using statistics or function optimisation, e.g. modelling of probability density functions
G06F 18/2433
Relating to the number of classes: single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection