Sas Institute Inc.

Keith R Holdaway

Moray L Laing

David Pope

James Duarte

Moray Laing

Sergiy Peredriy

Tops of geological layers can be automatically identified using machine-learning techniques as described herein. In one example, a system can receive well log records associated with wellbores drilled through geological layers. The system can generate well clusters by applying a clustering process to the well log records. The system can then obtain a respective set of training data associated with a well cluster, train a machine-learning model based on the respective set of training data, select a target well-log record associated with a target wellbore of the well cluster, and provide the target well-log record as input to the trained machine-learning model. Based on an output from the trained machine-learning model, the system can determine the geological tops of the geological layers in a region surrounding the target wellbore. The system may then transmit an electronic signal indicating the geological tops of the geological layers associated with the target wellbore.

Machine-learning techniques for automatically identifying tops of geological layers in subterranean formations

A computing device configured to determine an optimal value for a control of a drilling operation is provided. An event stream processing engine (ESPE) instantiated. The ESPE is instantiated to execute an objective function model determined using historical drilling data. The objective function model maximizes a rate of penetration for a previous drilling operation. The historical drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the previous drilling operation. Measured drilling data that includes current drilling data values for a current drilling operation is received by the ESPE. The received, measured drilling data is processed through the ESPE instantiated to execute the objective function model to determine an optimal value for a control of the current drilling operation. The determined optimal value is output by the ESPE for the control of the current drilling operation.

Control variable determination to maximize a drilling rate of penetration

A computing device configured to determine when an alarm is triggered for a drilling operation is provided. Measured drilling data that includes a value measured for an input variable during a previous connection event of a drilling operation is received. A predicted value for a fluid flow back measure is determined by executing a predictive model with the measured drilling data as an input. The predictive model is determined using previous drilling data that includes a plurality of values measured for the input variable during a second drilling operation. The second drilling operation is a previous drilling operation at a different geographic wellbore location than the drilling operation. A fluid flow back measurement datum determined from sensor data is compared to the determined predicted value for the fluid flow back measure. An alarm is triggered on the drilling operation based on the comparison.

Fluid flow back prediction

A method of determining an optimal value for a control of a drilling operation is provided. Drilling data from a drilling operation is received. The drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the drilling operation. An objective function model is determined using the received drilling data. The objective function model maximizes a rate of penetration for the drilling operation. Measured drilling data is received that includes current drilling data values for a different drilling operation. An optimal value for a control of the different drilling operation is determined by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input. The determined optimal value for the control of the different drilling operation is output.

Growing community of inventors

Raleigh, NC, United States of America

Keith R Holdaway