Petro-physical property prediction

Abstract

Embodiments relate to a method that includes measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process. Embodiments may further include identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling. Embodiments may further include outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit. Other embodiments may be described or claimed.

What is claimed is: 
     
         1 . A method comprising:
 measuring, by an electronic device at a surface of a wellbore, a functional characteristic of a drilling process;   identifying, by the electronic device based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and   outputting, by the electronic device, an indication of the change in the petro-physical property of the rock at the drill-bit.   
     
     
         2 . The method of  claim 1 , wherein the petro-physical property is porosity of the rock at the drill-bit while drilling. 
     
     
         3 . The method of  claim 1 , wherein the petro-physical property is a resistivity of the rock or a density of the rock. 
     
     
         4 . The method of  claim 1 , further comprising identifying, by the electronic device based on the change in the petro-physical property of the rock at the drill-bit, that a bottom hole assembly (BHA) is differentially jammed in the wellbore. 
     
     
         5 . The method of  claim 1 , further comprising identifying, by the electronic device based on the change in the petro-physical property of the rock at the drill-bit, that a trajectory of the drill-bit is to be adjusted. 
     
     
         6 . The method of  claim 1 , wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore. 
     
     
         7 . The method of  claim 1 , wherein the identifying the change in the petro-physical property is based on a machine learning algorithm executed by the electronic device. 
     
     
         8 . One or more non-transitory computer-readable media comprising instructions that, upon execution of the instructions by one or more processors of an electronic device, are to cause the electronic device to:
 measure, at a surface of a wellbore, a functional characteristic of a drilling process;   identify, based on a change in the functional characteristic, a change in porosity of the rock at the drill-bit while drilling; and   output an indication of the change in the porosity of the rock at the drill-bit.   
     
     
         9 . The one or more non-transitory computer-readable media of  claim 8 , wherein the instructions are further to identify, based on an increase in the porosity, that a bottom hole assembly (BHA) is differentially jammed in the wellbore. 
     
     
         10 . The one or more non-transitory computer-readable media of  claim 8 , wherein the instructions are further to identify, based on a decrease in the porosity, that a trajectory of the drill-bit is to be adjusted. 
     
     
         11 . The one or more non-transitory computer-readable media of  claim 10 , wherein the instructions are further to adjust, based on the decrease in the porosity, the trajectory of the drill-bit. 
     
     
         12 . The one or more non-transitory computer-readable media of  claim 8 , wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore. 
     
     
         13 . The one or more non-transitory computer-readable media of  claim 8 , wherein the instructions to identify the change in the porosity include instructions to execute a machine learning algorithm to identify the change in the porosity. 
     
     
         14 . An electronic device comprising:
 a user interface (UI);   one or more processors; and   one or more non-transitory computer-readable media including instructions that, upon execution of the instructions by the one or more processors, are to cause the electronic device to:
 measure, at a surface of a wellbore, a functional characteristic of a drilling process; 
 execute a machine-learning algorithm to identify, based on a change in the functional characteristic, a change in a petro-physical property of rock at a drill-bit while drilling; and 
 output, via the UI, an indication of the change in the petro-physical property of the rock at the drill-bit. 
   
     
     
         15 . The electronic device of  claim 14 , wherein the petro-physical property is porosity of the rock at the drill-bit while drilling. 
     
     
         16 . The electronic device of  claim 14 , wherein the petro-physical property is a resistivity of the rock or a density of the rock. 
     
     
         17 . The electronic device of  claim 14 , wherein the functional characteristic is a rate of penetration of the drill-bit, a torque on the drill-bit, a rotational speed of the drill-bit, a weight-on-bit measurement of the drill-bit, a pumping rate of the drilling process, or a stand pipe pressure of the wellbore. 
     
     
         18 . The electronic device of  claim 14 , wherein the machine learning algorithm is a neural network. 
     
     
         19 . The electronic device of  claim 14 , wherein the machine learning algorithm is based on a relationship between a functional characteristic of a drilling process of a previously drilled wellbore and a petro-physical property of rock adjacent to the previously drilled wellbore. 
     
     
         20 . The electronic device of  claim 14 , wherein the instructions are further to update the machine learning algorithm based on a sensor in the wellbore.