Predictive drilling data correction

Patent metadata

Field	Value
Publication number	US-2022205350-A1
Application number	US-202017134626-A
Country	US
Kind code	A1
Filing date	Dec 28, 2020
Priority date	Dec 28, 2020
Publication date	Jun 30, 2022
Grant date	—

Abstract

A drilling data analytics engine disclosed herein automatically corrects drilling data with predictive modeling. A drilling data quality analyzer segregates drilling data into good drilling data and bad drilling data that has missing, incomplete, or incorrect entries. For each bad data entry in the bad drilling data, the drilling data analytics engine preprocess drilling data attribute values for the corresponding task not including the drilling data attribute value for the bad data entry and inputs the preprocessed drilling data attribute values into a trained predictive model. The trained predictive model is trained on good drilling data to estimate values for the drilling attribute corresponding to the bad data entry.

First claim

What is claimed is:

1 . A method comprising:
identifying a first flaw in a data set of a subterranean operation according to data quality rules defined for the subterranean operation, wherein the data set includes multiple sets of data values, further wherein each set of data values is associated with one of multiple stages of the subterranean operation; determining that the first flaw corresponds to a first set of data values associated with a first of the multiple stages and to a first of a plurality of attributes of the subterranean operation; inputting at least a subset of the first set of data values into a first trained predictive model, wherein the subset of the first set of data values does not include a data value for the first attribute; and indicating outputs of the first trained predictive model having high confidence values as candidate corrections for the first flaw.

2 . The method of claim 1 , wherein each set of data values is associated with at least one of a set of one or more tasks for the subterranean operation.

3 . The method of claim 2 , wherein the set of one or more tasks for the subterranean operation comprises a set of one or more downhole operations performed by an operator of the subterranean operation.

4 . The method of claim 1 , further comprising,
identifying a subset of the data set of the subterranean operation without flaws according to the data quality rules defined for the subterranean operation; and training a predictive model to estimate data values for the first attribute based, at least in part, on the subset of the data set of the subterranean operation, wherein training the predictive model generates the first trained predictive model.

5 . The method of claim 1 , wherein the first flaw in the data set of the subterranean operation comprises at least one of a missing data value, an incorrect data value, and an incomplete data value.

6 . The method of claim 1 , further comprising replacing a data value corresponding to the first flaw in the data set of the subterranean operation with one of the candidate corrections for the first flaw.

7 . The method of claim 6 , wherein replacing the data value corresponding to the first flaw in the data set of the subterranean operation with one of the candidate corrections for the first flaw comprises replacing the data value in response to a selection of one of the candidate corrections.

8 . The method of claim 1 , further comprising preprocessing the subset of the first set of data values with natural language processing.

9 . The method of claim 1 , further comprising,
computing similarities between a data value corresponding to the first flaw in the data set and correct data values for the first attribute in the data set; and inputting the similarities in addition to the subset of the first set of data values into the first trained predictive model.

10 . One or more non-transitory machine-readable media comprising program code to:
identify a first flaw in a data set of a subterranean operation according to data quality rules defined for the subterranean operation, wherein the data set includes multiple sets of data values, further wherein each set of data values is associated with one of multiple stages of the subterranean operation; determine that the first flaw corresponds to a first set of data values associated with a first of the multiple stages and to a first of a plurality of attributes of the subterranean operation; input at least a subset of the first set of data values into a first trained predictive model, wherein the subset of the first set of data values does not include a data value for the first attribute; and indicate outputs of the first trained predictive model having high confidence values as candidate corrections for the first flaw.

11 . The non-transitory machine-readable media of claim 10 , wherein each set of data values is associated with at least one of a set of one or more tasks for the subterranean operation.

12 . The non-transitory machine-readable media of claim 11 , wherein the set of one or more tasks for the subterranean operation comprises a set of one or more downhole operations performed by an operator of the subterranean operation.

13 . The non-transitory machine-readable media of claim 10 , further comprising program code to,
identify a subset of the data set of the subterranean operation without flaws according to the data quality rules defined for the subterranean operation; and train a predictive model to estimate data values for the first attribute based, at least in part, on the subset of the data set of the subterranean operation, wherein training the predictive model generates the first trained predictive model.

14 . The non-transitory machine-readable media of claim 10 , wherein the first flaw in the data set of the subterranean operation comprises at least one of a missing data value, an incorrect data value, and an incomplete data value.

15 . The non-transitory machine-readable media of claim 10 , further comprising program code to replace a data value corresponding to the first flaw in the data set of the subterranean operation with one of the candidate corrections for the first flaw.

16 . The non-transitory machine-readable media of claim 15 , wherein the program code to replace the data value corresponding to the first flaw in the data set of the subterranean operation with one of the candidate corrections for the first flaw comprises program code to replace the data value in response to a selection of one of the candidate corrections.

17 . The non-transitory machine-readable media of claim 10 , further comprising program code to preprocess the subset of the first set of data values with natural language processing.

18 . The non-transitory machine-readable media of claim 10 , further comprising program code to,
compute similarities between a data value corresponding to the first flaw in the data set and correct data values for the first attribute in the data set; and input the similarities in addition to the subset of the first set of data values into the first trained predictive model.

19 . An apparatus comprising:
a processor; and a machine-readable medium having program code executable by the processor to cause the apparatus to,
identify a first flaw in a data set of a subterranean operation according to data quality rules defined for the subterranean operation, wherein the data set includes multiple sets of data values, further wherein each set of data values is associated with one of multiple stages of the subterranean operation;
determine that the first flaw corresponds to a first set of data values associated with a first of the multiple stages and to a first of a plurality of attributes of the subterranean operation;
input at least a subset of the first set of data values into a first trained predictive model, wherein the subset of the first set of data values does not include a data value for the first attribute; and
indicate outputs of the first trained predictive model having high confidence values as candidate corrections for the first flaw.

20 . The apparatus of claim 19 , wherein each set of data values is associated with at least one of a set of one or more tasks for the subterranean operation.

Assignees

• Landmark Graphics Corp

Inventors

• Santana Misael Luis
• Fatnani Ashish Kishore
• Verma Shashwat
• Srivastav Shreshth
• Vallabhaneni Sridharan

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title