Drilling data correction with machine learning and rules-based predictions

Patent metadata

Field	Value
Publication number	US-2022205351-A1
Application number	US-202017134738-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 correction system corrects drilling data entries in high-importance drilling data segments using machine learning and rules-based drilling models. A data importance analyzer identifies high-importance data segments in incoming drilling data. The drilling data correction system inputs features of drilling data into machine learning drilling models and rules-based drilling models trained to predict the high-importance data segments. Predictions from the machine learning drilling models and rules-based drilling models are presented to a user based on drilling data prediction criteria. The machine learning drilling data predictions are used to automatically correct the high-importance data segments, or the user chooses between machine learning drilling data predictions and rules-based drilling data predictions to correct the high-importance drilling data segment.

First claim

What is claimed is:

1 . A method comprising:
identifying a first subset of drilling data having flawed drilling data entries, wherein the first subset of drilling data corresponds to a data segment of a first drilling data attribute; inputting features of the drilling data into a trained machine learning model to generate a first prediction for the data segment of the first drilling data attribute; applying one or more drilling rules to the drilling data to generate a second prediction for the data segment of the first drilling data attribute; and indicating a set of one or more corrections for the data segment of the first drilling data attribute based, at least in part, on the first prediction, the second prediction and a confidence value for the first prediction.

2 . The method of claim 1 further comprising,
determining that the confidence value for the first prediction satisfies a confidence threshold; and
correcting flawed drilling data entries in the first subset of drilling data with the first prediction.

3 . The method of claim 1 further comprising,
determining that the confidence value for the first prediction does not satisfy a confidence threshold;
determining that the second prediction satisfies a data quality criterion; and
correcting flawed drilling data entries in the first subset of drilling data with the second prediction.

4 . The method of claim 1 further comprising,
generating drilling feature data based, at least in part, on a first plurality of features of a second subset of drilling data; and
generating the trained machine learning model to predict the data segment of the first drilling data attribute based, at least in part, on the drilling feature data.

5 . The method of claim 1 , further comprising identifying the data segment of the first drilling data attribute based, at least in part, on flaws in the first subset of drilling data.

6 . The method of claim 1 , wherein the data segment of the first drilling data attribute comprises a curve of petrophysical property values.

7 . The method of claim 1 , further comprising updating the first subset of drilling data with at least a correction of the set of one or more corrections for the data segment of the first drilling data attribute.

8 . The method of claim 7 , further comprising retraining the trained machine learning model using at least the updated first subset of drilling data.

9 . One or more non-transitory machine-readable media comprising program to:
identify a first subset of drilling data having flawed drilling data entries, wherein the first subset of drilling data corresponds to a data segment of a first drilling data attribute; input features of the drilling data into a trained machine learning model to generate a first prediction for the data segment of the first drilling data attribute; apply one or more drilling rules to the drilling data to generate a second prediction for the data segment of the first drilling data attribute; and indicate a set of one or more corrections for the data segment of the first drilling data attribute based, at least in part, on the first prediction, the second prediction and a confidence value for the first prediction.

10 . The non-transitory machine-readable media of claim 9 further comprising program code to,
determine that the confidence value for the first prediction satisfies a confidence threshold; and
correct flawed drilling data entries in the first subset of drilling data with the first prediction.

11 . The non-transitory machine-readable media of claim 9 further comprising program code to,
determine that the confidence value for the first prediction does not satisfy a confidence threshold;
determine that the second prediction satisfies a data quality criterion; and
correct flawed drilling data entries in the first subset of drilling data with the second prediction.

12 . The non-transitory machine-readable media of claim 9 further comprising program code to,
generate drilling feature data based, at least in part, on a first plurality of features of a second subset of drilling data; and
generate the trained machine learning model to predict the data segment of the first drilling data attribute based, at least in part, on the drilling feature data.

13 . The non-transitory machine-readable media of claim 9 , further comprising program code to identify the data segment of the first drilling data attribute based, at least in part, on flaws in the first subset of drilling data.

14 . The non-transitory machine-readable media of claim 9 , wherein the data segment of the first drilling data attribute comprises a curve of petrophysical property values.

15 . The non-transitory machine-readable media of claim 9 , further comprising program code to update the first subset of drilling data with at least a correction of the set of one or more corrections for the data segment of the first drilling data attribute.

16 . The non-transitory machine-readable media of claim 15 , further comprising program code to retrain the trained machine learning model using at least the updated first subset of drilling data.

17 . 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 subset of drilling data having flawed drilling data entries, wherein the first subset of drilling data corresponds to a data segment of a first drilling data attribute;
input features of the drilling data into a trained machine learning model to generate a first prediction for the data segment of the first drilling data attribute;
apply one or more drilling rules to the drilling data to generate a second prediction for the data segment of the first drilling data attribute; and
indicate a set of one or more corrections for the data segment of the first drilling data attribute based, at least in part, on the first prediction, the second prediction and a confidence value for the first prediction.

18 . The apparatus of claim 17 further comprising program code executable by the processor to cause the apparatus to,
determine that the confidence value for the first prediction satisfies a confidence threshold; and
correct flawed drilling data entries in the first subset of drilling data with the first prediction.

19 . The apparatus of claim 17 further comprising program code executable by the processor to cause the apparatus to,
determine that the confidence value for the first prediction does not satisfy a confidence threshold;
determine that the second prediction satisfies a data quality criterion; and
correct flawed drilling data entries in the first subset of drilling data with the second prediction.

20 . The apparatus of claim 17 further comprising program code executable by the processor to cause the apparatus to,
generate drilling feature data based, at least in part, on a first plurality of features of a second subset of drilling data; and
generate the trained machine learning model to predict the data segment of the first drilling data attribute based, at least in part, on the drilling feature data.

Assignees

• Landmark Graphics Corp

Inventors

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

Classifications

• G06F18/214 Primary

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

• G06N5/025

  Extracting rules from data · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/09

  Supervised learning · CPC title

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title