Rate of penetration optimization technique

Abstract

A method for optimizing drilling performance is disclosed. The method includes determining, while advancing a drill bit during a drilling operation based on drilling parameters specified by a user, a rate of penetration (ROP), acquiring, using sensors disposed on drilling equipment of a well, measurement data of each drilling equipment that represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation, determining, using an artificial intelligence method based on the measurement data, a non-linear relationship between the ROP, the drilling parameters, and the conditions of the drilling equipment, identifying a constraint specified by the user based on the conditions of the drilling equipment, determining, based on the non-linear relationship and the user specified constraint, a target value of the drilling parameters to optimize a pre-determined performance measure of the drilling operation, and further performing the drilling operation based on the target value.

What is claimed is: 
     
         1 . A method for optimizing drilling performance of a drilling operation, the method comprising:
 determining, while advancing a drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a rate of penetration (ROP);   acquiring, using a plurality of sensors disposed on a plurality of drilling equipment of a well, measurement data of each drilling equipment, wherein each measurement data represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation;   determining, using an artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a non-linear relationship between the ROP, the plurality of drilling parameters, and the conditions of the plurality of drilling equipment;   identifying a constraint specified by the user based at least on the conditions of the plurality of drilling equipment;   determining, based at least on the non-linear relationship and the user specified constraint, a target value of the plurality of drilling parameters to optimize a pre-determined performance measure of the drilling operation; and   further performing the drilling operation based on the target value of the plurality of drilling parameters.   
     
     
         2 . The method according to  claim 1 , further comprising:
 generating, using the artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a blackbox function that describes a relationship between a performance measure of the drilling operation with respect to the plurality of drilling parameters and the conditions of the plurality of drilling equipment,   wherein the blackbox function is used as an objective function to represent the pre-determined performance measure of the drilling operation, and   wherein the target value of the plurality of drilling parameters is determined by applying an iterative optimization algorithm to the objective function while meeting the user specified constraint.   
     
     
         3 . The method according to  claim 2 ,
 wherein the blackbox function comprises the non-linear relationship and at least one physics-based analytical relationship.   
     
     
         4 . The method according to  claim 3 , further comprising:
 specifying, based on the blackbox function, the plurality of drilling parameters of a subsequent drilling operation.   
     
     
         5 . The method according to  claim 1 ,
 wherein the plurality of drilling parameters comprise a weight-on-bit value, a rotary speed value, and a pump flow rate value.   
     
     
         6 . The method according to  claim 1 ,
 wherein the conditions of the plurality of drilling equipment comprise standpipe pressure, return flow rate, bit condition, hole learning index, equivalent circulating density (ECD), top-drive torque, top-drive hydraulics and electronics condition, stuck-pipe index, surface and downhole drill string vibration, bottomhole-assembly condition, drilling mud properties, and hole geometry.   
     
     
         7 . The method according to  claim 1 ,
 wherein the constraint defines an acceptable operating range of the conditions of the plurality of drilling equipment, and   wherein the acceptable range comprises an extreme value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus.   
     
     
         8 . A data gathering and analysis system for optimizing drilling performance of a drilling operation, comprising:
 a processor; and   a memory coupled to the processor and storing instruction, the instructions, when executed by the processor, comprising functionality for:
 determining, while advancing a drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a rate of penetration (ROP); 
 acquiring, using a plurality of sensors disposed on a plurality of drilling equipment of a well, measurement data of each drilling equipment, wherein each measurement data represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation; 
 determining, using an artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a non-linear relationship between the ROP, the plurality of drilling parameters, and the conditions of the plurality of drilling equipment; 
 identifying a constraint specified by the user based at least on the conditions of the plurality of drilling equipment; 
 determining, based at least on the non-linear relationship and the user specified constraint, a target value of the plurality of drilling parameters to optimize a pre-determined performance measure of the drilling operation; and 
 further performing the drilling operation based on the target value of the plurality of drilling parameters. 
   
     
     
         9 . The data gathering and analysis system according to  claim 8 , the instructions, when executed by the processor, further comprising functionality for:
 generating, using the artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a blackbox function that describes a relationship between a performance measure of the drilling operation with respect to the plurality of drilling parameters and the conditions of the plurality of drilling equipment,   wherein the blackbox function is used as an objective function to represent the pre-determined performance measure of the drilling operation, and   wherein the target value of the plurality of drilling parameters is determined by applying an iterative optimization algorithm to the objective function while meeting the user specified constraint.   
     
     
         10 . The data gathering and analysis system according to  claim 9 ,
 wherein the blackbox function comprises the non-linear relationship and at least one physics-based analytical relationship.   
     
     
         11 . The data gathering and analysis system according to  claim 10 , the instructions, when executed by the processor, further comprising functionality for:
 specifying, based on the blackbox function, the plurality of drilling parameters of a subsequent drilling operation.   
     
     
         12 . The data gathering and analysis system according to  claim 8 ,
 wherein the plurality of drilling parameters comprise a weight-on-bit value, a rotary speed value, and a pump flow rate value.   
     
     
         13 . The data gathering and analysis system according to  claim 8 ,
 wherein the conditions of the plurality of drilling equipment comprise standpipe pressure, return flow rate, bit condition, hole learning index, equivalent circulating density (ECD), top-drive torque, top-drive hydraulics and electronics condition, stuck-pipe index, surface and downhole drill string vibration, bottomhole-assembly condition, drilling mud properties, and hole geometry.   
     
     
         14 . The data gathering and analysis system according to  claim 8 ,
 wherein the constraint defines an acceptable operating range of the conditions of the plurality of drilling equipment, and   wherein the acceptable range comprises an extreme value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus.   
     
     
         15 . A wellsite for performing a drilling operation of a well, comprising:
 a rig having a plurality of drilling equipment of the well installed with a plurality of sensors; and   a data gathering and analysis system comprising functionality for:
 determining, while advancing a drill bit during the drilling operation based on a plurality of drilling parameters specified by a user, a rate of penetration (ROP); 
 acquiring, using the plurality of sensors, measurement data of each drilling equipment, wherein each measurement data represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation; 
 determining, using an artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a non-linear relationship between the ROP, the plurality of drilling parameters, and the conditions of the plurality of drilling equipment; 
 identifying a constraint specified by the user based at least on the conditions of the plurality of drilling equipment; 
 determining, based at least on the non-linear relationship and the user specified constraint, a target value of the plurality of drilling parameters to optimize a pre-determined performance measure of the drilling operation; and 
 further performing the drilling operation based on the target value of the plurality of drilling parameters. 
   
     
     
         16 . The wellsite according to  claim 15 , the data gathering and analysis system further comprising functionality for:
 generating, using the artificial intelligence method based on at least the measurement data of the plurality of drilling equipment, a blackbox function that describes a relationship between a performance measure of the drilling operation with respect to the plurality of drilling parameters and the conditions of the plurality of drilling equipment,   wherein the blackbox function is used as an objective function to represent the pre-determined performance measure of the drilling operation, and   wherein the target value of the plurality of drilling parameters is determined by applying an iterative optimization algorithm to the objective function while meeting the user specified constraint.   
     
     
         17 . The wellsite according to  claim 16 ,
 wherein the blackbox function comprises the non-linear relationship and at least one physics-based analytical relationship.   
     
     
         18 . The wellsite according to  claim 17 , the data gathering and analysis system further comprising functionality for:
 specifying, based on the blackbox function, the plurality of drilling parameters of a subsequent drilling operation.   
     
     
         19 . The wellsite according to  claim 15 ,
 wherein the plurality of drilling parameters comprise a weight-on-bit value, a rotary speed value, and a pump flow rate value, and   wherein the conditions of the plurality of drilling equipment comprise standpipe pressure, return flow rate, bit condition, hole learning index, equivalent circulating density (ECD), top-drive torque, top-drive hydraulics and electronics condition, stuck-pipe index, surface and downhole drill string vibration, bottomhole-assembly condition, drilling mud properties, and hole geometry.   
     
     
         20 . The wellsite according to  claim 15 ,
 wherein the constraint defines an acceptable operating range of the conditions of the plurality of drilling equipment, and   wherein the acceptable range comprises an extreme value of one or more of top drive torque, weight-on-bit, fluid flow rate, stick-slip index, bit wear rate, stuck pipe probability index, and cuttings concentration in annulus.