Method for determining downhole characterics in a production well

Abstract

A method for determining one or more downhole characteristic(s) in a production well, comprising: measuring a physical property of the production well using at least one sensor; pre-processing the measured values of the physical property using feature scaling or automatic gain control techniques; and determining one or more downhole characteristic(s) using a supervised machine learning method. The supervised machine learning method comprises: cross-validating a predictive analytics algorithm with training data that are previously measured physical properties and applying one or more algorithms to the measured values of the physical property to converge on an estimate for the determined downhole characteristic(s).

1 . A method for determining one or more downhole characteristic(s) in a production well, comprising:
 measuring a physical property of the production well using at least one sensor,   pre-processing the measured values of the physical property using feature scaling and/or automatic gain control techniques, and   determining one or more downhole characteristic(s) using a supervised machine learning method comprising:   cross-validating a predictive analytics algorithm with training data that are previously measured physical properties, and   applying one or more algorithms to the measured values of the physical property to converge on an estimate for the determined downhole characteristic(s).   
     
     
         2 . The method according to  claim 1 , further comprising transmitting a control signal based on the determined downhole characteristic(s). 
     
     
         3 . The method according to  claim 2 , wherein the control signal is transmitted to a downhole tool. 
     
     
         4 . The method according to  claim 1 , wherein said physical property is the permittivity of a fluid in the well and/or the current provided to a downhole tool of the well. 
     
     
         5 . The method according to  claim 1 , further comprising pre-determining said one or more downhole characteristic(s) using a linear or non-linear system identification. 
     
     
         6 . The method according to  claim 4 , wherein the physical property is measured using multiple sensors in at least two different configurations, and wherein pre-determining the downhole characteristic(s) comprises using the non-linear or linear system identification. 
     
     
         7 . The method according to  claim 1 , wherein the downhole tool is a suction tool comprising a bailer, and the downhole characteristic(s) comprises the fullness of the bailer. 
     
     
         8 . The method according to  claim 1 , wherein the downhole tool is a milling tool having miller teeth, and the downhole characteristic(s) comprises the miller teeth health. 
     
     
         9 . The method according to  claim 1 , wherein the downhole tool is an opening drilling tool having at least one drilling bit, the downhole characteristic(s) comprising the drilling bit health. 
     
     
         10 . The method according to  claim 1 , wherein the downhole tool is a tubing cutting tool having one or more cutting bit(s), the downhole characteristic(s) comprising the cutting bit health. 
     
     
         11 . The method according to  claim 1 , wherein at least one sensor comprises an electrode and/or an ampere metre. 
     
     
         12 . The method according to  claim 1 , further comprising registering an angle of the at least one sensor with respect to gravity using an accelerometer. 
     
     
         13 . The method according to  claim 1 , wherein the supervised machine learning method is configured to operate a random forest algorithm. 
     
     
         14 . The method according to  claim 1 , further comprising pre-processing the measured values of the physical property by applying feature scaling and/or an automatic gain control technique to overcome data imbalance. 
     
     
         15 . A computer programme product comprising a computer readable medium, having thereon a computer programme comprising programme instructions, the computer programme being loadable into a data-processing unit and adapted to cause execution of a method according to  claim 1  when the computer programme is run by the data-processing unit.