Downhole event detection and modified mud logging
AssigneeSchlumberger Technology Corporation
InventorsElias Temer, Hakim Arabi, David Casado, Prashant Sharma
Life of this patent
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Abstract
A method for classifying a downhole event includes acquiring logging while drilling (LWD) measurements in a downhole processing system that includes a trained deep learning model. The received LWD measurements are evaluated using the trained model to classify a drilling event. The classification classifies a change in a composition of the annular drilling fluid and enables a zone of interest in the subterranean formation to be identified.
Claims20
A method for classifying a downhole event while drilling, the method comprising:
- making logging while drilling (LWD) measurements in a wellbore penetrating a subterranean formation;
- receiving the LWD measurements in a downhole processing system, the downhole processing system including a trained deep learning model;
- classifying a drilling event by evaluating the received LWD measurements with the trained deep learning model, the drilling event causing a change in at least one property of annular drilling fluid in the wellbore, the classification classifying a change in a composition of the annular drilling fluid;
- and
- identifying a zone of interest in the subterranean formation from the deep learning model classification.
A downhole tool string comprising:
- at least one logging while drilling (LWD) tool configured make LWD measurements in a wellbore;
- a downhole processing system configured to receive the LWD measurements, the downhole processing system including a trained deep learning model, the trained deep learning model configured to classifying a drilling event by evaluating the received LWD measurements with the trained deep learning model, the drilling event causing a change in at least one property of annular drilling fluid in the wellbore, the classification classifying a change in a composition of the annular drilling fluid, the downhole processing system further configured to identifying a zone of interest in the subterranean formation from the deep learning model classification and after identifying the zone of interest generate a notification for telemetry transmission to a surface location;
- and
- a downhole telemetry tool configured to transmit the notification to the surface location.
A method for classifying a downhole event while drilling, the method comprising:
- making logging while drilling (LWD) imaging measurements in a wellbore penetrating a subterranean formation;
- making time series measurements in the wellbore corresponding to the LWD imaging measurements;
- receiving the LWD imaging measurements and the time series measurements in a downhole processing system, the downhole processing system including a trained deep learning model configured to evaluate the LWD imaging measurements and a trained machine algorithm configured to evaluate the time series measurements;
- classifying a drilling event by evaluating the received LWD imaging measurements with the trained deep learning model, the drilling event causing a change in at least one property of annular drilling fluid in the wellbore, the classification classifying a change in a composition of the annular drilling fluid;
- identifying anomalies in the times series measurements using the trained machine algorithm;
- and
- identifying a zone of interest in the subterranean formation when the drilling event classification matches a preselected classification and when an anomaly is identified.
Dependencies are inferred from the claim text.