# US 11,898,442 B2 · Method and system for formation pore pressure prediction with automatic parameter reduction

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- Number: US 11,898,442 B2
- Document ID: US11898442B2
- Status: Granted
- Kind: B2
- Application number: 17/651,194
- Filed: 2022-02-15
- Published: 2023-08-17
- Granted: 2024-02-13
- Earliest claimed: 2022-02-15
- Assignees: Saudi Arabian Oil Company
- Applicants: Saudi Arabian Oil Company
- Inventors: Fatai A. Anifowose; Mokhles M. Mezghani
- Web page: https://patentgrep.com/patent/US11898442B2

## CPC

- E21B 7/04: Directional drilling
- E21B 49/005: {Testing the nature of borehole walls or the formation by using drilling mud or cutting data}
- E21B 44/00: Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
- E21B 45/00: Measuring the drilling time or rate of penetration
- E21B 49/003: {by analysing drilling variables or conditions (E21B49/005 takes precedence; systems specially adapted for monitoring a plurality of drilling variables or conditions E21B44/00)}
- E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits
- E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS

## 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.

## Claims

### Claim 1

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.

### Claim 2 (depends on 1)

The method of claim 1, wherein the prediction of the pore pressure is performed in real-time, while drilling the well.

### Claim 3 (depends on 1)

The method of claim 1, wherein the surface drilling parameters comprise at least one selected from the group consisting of:
a rate of penetration (ROP),
a weight on bit (WOB),
a torque,
revolutions per minute (RPM),
a hook load,
a mud flow rate,
a D-exponent,
a mud density,
a standpipe pressure, and
a mud temperature.

### Claim 4 (depends on 1)

The method of claim 1, wherein the logging while drilling parameters comprise at least one selected from the group consisting of:
gamma ray data,
sonic data,
resistivity data, and
neutron porosity recordings.

### Claim 5 (depends on 1)

The method of claim 1, wherein the advanced mud gas measurements comprise at least one selected from the group consisting of:
C1, C2, C2S, C3, iC4, nC4, iC5, nC5, Benzene, Toluene, Helium, MethylCycloHexane, CO2, H2, and H2S.

### Claim 6 (depends on 1)

The method of claim 1, further comprising:
generating a reduced historical input parameter set by eliminating the at least one input parameter that is considered non-relevant from the historical input parameter set.

### Claim 7 (depends on 6)

The method of claim 6, further comprising:
training the machine learning model using the reduced historical input parameter set and the historical pore pressure.

### Claim 8

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.

### Claim 9 (depends on 8)

The system of claim 8, wherein the surface drilling parameters comprise at least one selected from the group consisting of:
a rate of penetration (ROP),
a weight on bit (WOB),
a torque,
revolutions per minute (RPM),
a hook load,
a mud flow rate,
a D-exponent,
a mud density,
a standpipe pressure, and
a mud temperature.

### Claim 10 (depends on 8)

The system of claim 8, wherein the logging while drilling parameters comprise at least one selected from the group consisting of:
gamma ray data,
sonic data,
resistivity data, and
neutron porosity recordings.

### Claim 11 (depends on 8)

The system of claim 8, wherein the advanced mud gas measurements comprise at least one selected from the group consisting of:
C1, C2, C2S, C3, iC4, nC4, iC5, nC5, Benzene, Toluene, Helium, MethylCycloHexane, CO2, H2, and H2S.

### Claim 12 (depends on 8)

The system of claim 8, wherein the at least one processor is further configured to:
generate a reduced historical input parameter set by eliminating the at least one input parameter that is considered non-beneficial from the historical input parameter set.

### Claim 13 (depends on 12)

The system of claim 12, wherein the at least one processor is further configured to:
train the machine learning model using the reduced historical input parameter set and the historical pore pressure.

### Claim 14

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.