Method and system for formation pore pressure prediction with automatic parameter reduction
AssigneeSaudi Arabian Oil Company
InventorsFatai A. Anifowose, Mokhles M. Mezghani
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Abstract
A method for formation pore pressure prediction involves obtaining an input parameter set while drilling a well. The input parameter set includes surface drilling parameters, logging while drilling parameters, and advanced mud gas measurements. The method further involves generating, from the input parameter set, a reduced input parameter set, by eliminating at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure, and predicting the pore pressure by applying a machine learning model to the reduced input parameter set.
Claims14
A method for formation pore pressure prediction, the method comprising:
- obtaining an input parameter set while drilling a well, the input parameter set comprising:
- surface drilling parameters,
- logging while drilling parameters, and
- advanced mud gas measurements;
- obtaining a historical data set from an offset well, the historical data set comprising:
- a historical input parameter set, and
- historical pore pressure;
- identifying at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure by determining that the at least one input parameter that is considered non-relevant has a mean squared error greater than a threshold selected for the historical pore pressure;
- generating, from the input parameter set, a reduced input parameter set, by eliminating the at least one input parameter of the input parameter set that is considered non-relevant;
- predicting the pore pressure by applying a machine learning model to the reduced input parameter set;
- and
- guiding the drilling of the well using the predicted pore pressure;
- wherein determining the at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure comprises at least one of:
- a linear correlation filter,
- a neighborhood components analysis, and
- a forward selection and backward elimination.
A system for formation pore pressure prediction, the system comprising:
- a database comprising a historical data set from an offset well, the historical data set comprising:
- a historical input parameter set, and
- historical pore pressure;
- at least one processor configured to:
- receive an input parameter set while drilling a well, the input parameter set comprising:
- surface drilling parameters,
- logging while drilling parameters, and
- advanced mud gas measurements;
- obtain the historical input parameter set from the database;
- identify at least one input parameter that is considered non-relevant by determining that the at least one input parameter that is considered non-relevant has a mean squared error greater than a threshold selected for the historical pore pressure;
- generate, from the input parameter set, a reduced input parameter set, by eliminating the at least one input parameter of the input parameter set that is considered non-relevant;
- predict the pore pressure by applying a machine learning model to the reduced input parameter set;
- and
- guiding the drilling of the well using the predicted pore pressure;
- wherein determining the at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure comprises at least one of:
- a linear correlation filter,
- a neighborhood components analysis, and
- a forward selection and backward elimination.
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:
- obtaining an input parameter set while drilling a well, the input parameter set comprising:
- surface drilling parameters,
- logging while drilling parameters, and
- advanced mud gas measurements;
- obtaining a historical data set from an offset well, the historical data set comprising:
- a historical input parameter set, and
- historical pore pressure;
- identifying at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure by determining that the at least one input parameter that is considered non-relevant has a mean squared error greater than a threshold selected for the historical pore pressure;
- generating, from the input parameter set, a reduced input parameter set, by eliminating the at least one input parameter of the input parameter set that is considered non-relevant;
- predicting the pore pressure by applying a machine learning model to the reduced input parameter set;
- and
- guiding the drilling of the well using the predicted pore pressure;
- wherein determining the at least one input parameter of the input parameter set that is considered non-relevant for predicting the pore pressure comprises at least one of:
- a linear correlation filter,
- a neighborhood components analysis, and
- a forward selection and backward elimination.
Dependencies are inferred from the claim text.