Methods and systems for predicting conditions ahead of a drill bit
AssigneeSaudi Arabian Oil Company
InventorsKlemens Katterbauer, Abdallah A. Alshehri, Alberto Marsala, Ali Abdallah Alyousef
Life of this patent
Estimating the term…
Abstract
A method for predicting conditions ahead of a drill bit while drilling a well involves performing, using a machine learning model, a classification of formation properties ahead of the drill bit, based on data that includes logging-while-drilling (LWD) data obtained while drilling the well.
Claims14
A method for predicting conditions ahead of a drill bit while drilling a well, the method comprising:
- performing, using a machine learning model, a classification of formation properties ahead of the drill bit, based on data comprising logging-while-drilling (LWD) data obtained while drilling the well;
- applying the classification of the formation properties to perform a geosteering of the drill bit;
- further comprising, prior to performing the classification;
- training the machine learning model;
- prior to training the machine learning model;
- weighting, in training data used for the training, different types of data based on quality;
- wherein the quality is assessed using a signal-to-noise ratio.
A system for predicting conditions ahead of a drill bit while drilling a well, the system comprising:
- a drilling system for drilling the well, the drilling system comprising the drill bit and a drill bit logging tool;
- and
- a control system configured to:
- perform, using a machine learning model, a classification of formation properties ahead of the drill bit, based on data comprising logging-while-drilling (LWD) data obtained from the drill bit logging tool while drilling the well using the drill bit;
- applying the classification of the formation properties to perform a geosteering of the drill bit;
- further comprising, prior to performing the classification:
- training the machine learning model;
- prior to training the machine learning model:
- weighting, in training data used for the training, different types of data based on quality;
- wherein the quality is assessed using a signal-to-noise ratio.
A non-transitory machine-readable medium comprising a plurality of machine-readable instructions executed by one or more processors, the plurality of machine-readable instructions causing the one or more processors to perform operations comprising:
- performing, using a machine learning model, a classification of formation properties ahead of a drill bit while drilling a well, based on data comprising logging-while-drilling (LWD) data obtained while drilling the well;
- applying the classification of the formation properties to perform a geosteering of the drill bit;
- further comprising, prior to performing the classification:
- training the machine learning model;
- prior to training the machine learning model:
- weighting, in training data used for the training, different types of data based on quality;
- wherein the quality is assessed using a signal-to-noise ratio.
Dependencies are inferred from the claim text.