Anomaly detection in gas measurements of hydrocarbon wells

Abstract

The present disclosure relates to systems and/or computer-implemented methods that can utilize machine learning models to monitor exploratory hydrocarbon wells for gas reading anomalies. One or more embodiments described herein can regard a method comprising collecting well feature data characterizing operation of an exploratory hydrocarbon well. The well feature data includes a gas measurement value. The method can also comprise applying a machine learning model to predict gas reading values associated with the exploratory hydrocarbon well. The method can further comprise comparing the gas measurement value with the gas reading value predicted by the machine learning model to detect a gas reading anomaly.

The invention claimed is: 
     
         1 . A method, comprising:
 collecting well feature data characterizing operation of an exploratory hydrocarbon well, wherein the well feature data includes a gas measurement value;   applying a machine learning model to predict gas reading values associated with the exploratory hydrocarbon well; and   comparing the gas measurement value with the gas reading value predicted by the machine learning model to detect a gas reading anomaly.   
     
     
         2 . The method of  claim 1 , wherein the applying the machine learning model comprises executing an ensemble learning algorithm. 
     
     
         3 . The method of  claim 2 , wherein the comparing the gas measurement value with the gas reading value comprises executing a root mean square error algorithm. 
     
     
         4 . The method of  claim 3 , further comprising generating an alert based on the root mean square error algorithm computing a value that is greater than equal to a defined threshold of deviation. 
     
     
         5 . The method of  claim 4 , wherein the collecting the well feature data is performed via an automatic loading operation via a socket connection between an anomaly detector employing the machine learning model and a well site monitoring the operation of the exploratory hydrocarbon well. 
     
     
         6 . The method of  claim 3 , further comprising:
 executing a standard scaler algorithm to standardize the well feature data; and   executing a data sequencing algorithm that utilizes a sliding window of a defined size to sequence the well feature data.   
     
     
         7 . The method of  claim 1 , further comprising:
 executing a conditional tabular generative adversarial network to generate synthetic training data; and   training the machine learning model based on the synthetic training data.   
     
     
         8 . A system, comprising:
 memory to store computer executable instructions; and   one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement:
 a data collector configured to collect well feature data characterizing operation of an exploratory hydrocarbon well, wherein the well feature data includes a gas measurement value; and 
 a machine learning engine having a training stage and an inference stage, wherein the inference stage is configured to, based on a machine learning model, predict gas reading values associated with the exploratory hydrocarbon well, and wherein the machine learning model is configured to compare the gas measurement value with the a gas reading value predicted by the machine learning model to detect a gas reading anomaly. 
   
     
     
         9 . The system of  claim 8 , wherein the machine learning model is configured to execute an ensemble learning algorithm. 
     
     
         10 . The system of  claim 9 , wherein the machine learning engine is configured to execute a root mean square error algorithm to compare the gas measurement value with the a gas reading value. 
     
     
         11 . The system of  claim 10 , further comprising an alert generator configured to generate an alert based on the root mean square error algorithm computing a value that is greater than equal to a defined threshold of deviation. 
     
     
         12 . The system of  claim 11 , further comprising a data conditioner configured to execute a standard scaler algorithm to standardize the well feature data, wherein the data conditioner is further configured to execute a data sequencing algorithm that utilizes a sliding window of a defined size to sequence the well feature data. 
     
     
         13 . The system of  claim 8 , further comprising:
 a model training configured to execute a conditional tabular generative adversarial network to generate synthetic training data, wherein the training stage of the machine learning engine is configured to train the machine learning model based on the synthetic training data.   
     
     
         14 . A computer program product for monitoring gas readings for anomalies, the computer program product comprising a computer readable storage medium having computer executable instructions embodied therewith, the computer executable instructions executable by one or more processors to cause the one or more processors to:
 collect well feature data characterizing operation of an exploratory hydrocarbon well, wherein the well feature data includes a gas measurement value;   apply a machine learning model to predict gas reading values associated with the exploratory hydrocarbon well; and   compare the gas measurement value with the gas reading value predicted by the machine learning model to detect a gas reading anomaly.   
     
     
         15 . The computer program product of  claim 14 , wherein the computer executable instructions further cause the one or more processors to apply the machine learning model using an extremely randomized tree algorithm. 
     
     
         16 . The computer program product of  claim 15 , wherein the computer executable instructions further cause the one or more processors to execute a root mean square error algorithm to compare the gas measurement value with the a gas reading value. 
     
     
         17 . The computer program product of  claim 16 , wherein the computer executable instructions further cause the one or more processors to generate an alert based on the root mean square error algorithm computing a value that is greater than equal to a defined threshold of deviation. 
     
     
         18 . The computer program product of  claim 17 , wherein the well feature data is collected via an automatic loading operation via a socket connection between an anomaly detector employing the machine learning model and a well site monitoring the operation of the exploratory hydrocarbon well. 
     
     
         19 . The computer program product of  claim 18 , wherein the computer executable instructions further cause the one or more processors to:
 execute a standard scaler algorithm to standardize the well feature data; and   execute a data sequencing algorithm that utilizes a sliding window of a defined size to sequence the well feature data.   
     
     
         20 . The computer program product of  claim 19 , wherein the computer executable instructions further cause the one or more processors to:
 execute a conditional tabular generative adversarial network to generate synthetic training data; and   train the machine learning model based on the synthetic training data.