Method and system for rate of penetration optimization using artificial intelligence techniques

1. A method for automatic optimization of rate of penetration (ROP), the method comprising:

obtaining, by a computer processor, a plurality of drilling surface parameters for a field of interest;

identifying, by a computer processor, an undefined compressive strength (UCS) data for a targeted formation of interest based on well logs;

calculating, by a computer processor, a mechanical specific energy (MSE) data based on the identified UCS data for the targeted formation of interest;

filtering, by a computer processor, the calculated MSE data based on the identified UCS data with a range for the targeted formation of interest;

training, by a computer processor, a machine learning model using the drilling surface parameters as inputs;

outputting, by a computer processor, a plurality of weights for drilling parameters in a ROP equation derived by using the trained machine learning model for the field of interest, wherein the drilling surface parameters are used as inputs;

determining, by a computer processor, a plurality of weights for drilling parameters in a Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;

outputting, by a computer processor, a plurality of weights for drilling parameters in the Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;

combining, by a computer processor, the machine learning ROP equation with the Teale's MSE equation to form a set of two equations;

determining, by a computer processor, a plurality of optimum drilling parameters by simultaneously solving the set of machine learning ROP equation and the Teale's MSE equation;

generating, by a computer processor, a work order to adjust the drilling parameters based on the determined optimum drilling parameters and previous drilling parameters; and

causing, by a computer processor, display of the work order and the determined optimum drilling parameters in a user interface of a client device.

2. The method of claim 1 ,

wherein the drilling surface parameter is one selected from a group consisting of an UCS data, a MSE data, a torque data, a revolutions per minute (RPM) data, a weight on bit (WOB) data, a pumping rate (GPM) data, and a stand pipe pressure (SPP) data.

3. The method of claim 1 :

wherein the machine learning algorithm is one selected from a group consisting of a linear regression algorithm, a logistic regression algorithm, a support vector regression algorithm, a random forest algorithm, a boosted decision tree algorithm, a multi-layer perceptron algorithm, and a convolutional neural network.

4. The method of claim 1 ,

wherein the outputted weight in the machine learning ROP equation is a value for a drilling surface parameter selected from a group consisting of UCS, MSE, torque, RPM, WOB, GPM, and SPP.

5. The method of claim 1 ,

wherein the outputted model weights in the MSE equation is a value for a drilling surface parameter selected from a group consisting of UCS, MSE, torque, RPM, WOB, GPM, and SPP.

6. The method of claim 1 , further comprising:

maintaining, by a computer processor, an optimum drilling ROP curve and an optimum MSE during downhole application for the field of interest; and

determining, by a computer processor, a pair of optimum drilling parameters by adjusting the targeted drilling parameters.

7. The method of claim 6 :

wherein the determined optimum drilling parameter are torque and WOB; and

wherein the targeted drilling surface parameters are MSE, RPM, WOB, GPM, and SPP.

8. The method of claim 6 , further comprising:

determining, by a computer processor, a correlation coefficient between the predicted ROP curve and the actual ROP curve for the field of interest;

determining, by a computer processor, an “efficiency” type if the obtained optimum MSE value is within the targeted efficient MSE range and the correlation coefficient between the predicted ROP curve and/or the actual ROP curve is above a threshold; and

determining, by a computer processor, an “inefficiency” type if the obtained optimum MSE is outside the targeted efficient MSE range and the correlation coefficient between the predicted ROP curve and/or the actual ROP curve is below a threshold.

9. The method of claim 8 , further comprising:

wherein the correlation coefficient is one selected from a group consisting of a Pearson correlation coefficient, an intra-class correlation coefficient, a rank correlation coefficient, and a polychoric correlation coefficient.

10. The method of claim 8 , further comprising:

determining, by a computer processor, new optimum drilling parameters based on updated targeted drilling surface parameters.

11. The method of claim 1 :

wherein the machine learning model determines the weights for a plurality of drilling parameters in the ROP equation using an offset well data for the field of interest.

12. The method of claim 1 , further comprising:

determining well path through the subterranean region of interest using the optimum drilling parameters; and

performing the well path using a drilling system.

13. The method of claim 1 :

wherein the UCS data is determined based on actual well logs with a range for the targeted formation of interest, and

wherein the MSE data is determined from the UCS data for the targeted formation of interest.

14. A system for automatic optimization of ROP, the system comprising:

an AI module comprising a plurality of machine learning algorithms and a processor configured to execute instructions stored in a non-transitory computer storage medium for performing a method for optimizing ROP comprising:

obtaining, a plurality of drilling surface parameters for the field of interest;

identifying, an UCS data for a targeted formation of interest based on well logs;

calculating, a MSE data based on the identified UCS values for the targeted formation of interest;

filtering, the calculated MSE data based on the identified UCS data with a range for the targeted formation of interest;

training, a machine learning model using the drilling surface parameters as inputs;

outputting, a plurality of weights for drilling parameters in a ROP equation derived by using the trained machine learning model for the field of interest, wherein the drilling surface parameters are used as inputs;

determining, a plurality of weights for drilling parameters in a Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;

outputting, a plurality of weights for drilling parameters in the Teale's MSE equation for the field of interest, wherein the drilling surface parameters are used as inputs;

combining, the machine learning ROP equation with the Teale's MSE equation to form a set of two equations;

determining, a plurality of optimum drilling parameters by simultaneously solving the set of machine learning ROP equation and the Teale's MSE equation;

generating, a work order to adjust the drilling parameters based on the determined optimum drilling parameters and previous drilling parameters; and

causing, display of the work order and the determined optimum drilling parameters in a user interface of a client device.

15. The system of claim 14 :

wherein the machine learning algorithm is one selected from a group consisting of a linear regression algorithm, a logistic regression algorithm, a support vector regression algorithm, a random forest algorithm, a boosted decision tree algorithm, a multi-layer perceptron algorithm, and a convolutional neural network.

16. The system of claim 14 , further comprising:

maintaining an optimum drilling ROP curve and an optimum MSE during downhole application for the field of interest; and

determining a pair of optimum drilling parameters by adjusting the targeted drilling parameters.

17. The system of claim 16 :

wherein the determined optimum drilling parameters are torque and WOB; and

wherein the targeted drilling surface parameters are MSE, RPM, WOB, GPM, and SPP.

18. The system of claim 16 , further comprising:

determining a correlation coefficient between the predicted ROP curve and the actual ROP curve for the field of interest;

determining an “efficiency” type if the obtained optimum MSE value is within the targeted efficient MSE range and the correlation coefficient between the predicted ROP curve and/or the actual ROP curve is above a threshold; and

determining an “inefficiency” type if the obtained optimum MSE is outside the targeted efficient MSE range and the correlation coefficient between the predicted ROP curve and/or the actual ROP curve is below a threshold.

19. The system of claim 18 :

wherein the correlation coefficient is one selected from a group consisting of: a Pearson correlation coefficient, an intra-class correlation coefficient, a rank correlation coefficient, and a polychoric correlation coefficient.

20. The system of claim 14 , further comprising:

determining, new optimum drilling parameters based on updated targeted drilling surface parameters.