Liquid loading identification

Abstract

This disclosure relates to collecting and analyzing data associated with a well site to predict instances of liquid loading that occurs (or that will occur) in a particular well environment. Systems herein involve training a liquid loading classification model based on historical data to predict whether or not liquid loading is occurring or will occur based on real-time sensor data that is collected by a plurality of sensors at the well site. The data is preprocessed and labeled in a manner that provides an efficient training process and which enables a machine learning model, such as a recursive neural network (RNN), to accurately predict when liquid loading will occur such that a presentation may be generated and presented to an individual with the ability to prevent or otherwise mitigate the liquid loading at a time when inefficiencies and other issues can be prevented.

What is claimed is: 
     
         1 . A method, comprising:
 collecting historical data associated with a well site, the historical data including historical data samples captured by a plurality of sensors and associated labels indicating instances of liquid loading corresponding to the historical data samples;   training a liquid loading classification model based on the data samples and associated labels from the historical data;   obtaining input sample data, wherein obtaining the input sample data includes configuring sensor data captured by the plurality of sensors to be provided as input to the liquid loading classification model;   applying the liquid loading classification model to the input sample data to generate an output including liquid loading labels indicating predicted instances of liquid loading in connection with the sensor data captured by the plurality of sensors and associated timestamps of the sensor data.   
     
     
         2 . The method of  claim 1 , wherein the liquid loading classification model is a machine learning model trained to predict instances of liquid loading based on series of given data samples captured by a given plurality of sensors associated with a given well site. 
     
     
         3 . The method of  claim 2 , wherein the machine learning model is a recursive neural network (RNN) model. 
     
     
         4 . The method of  claim 1 , wherein the plurality of sensors includes one or more tubing head pressure (THP) sensors and one or more casing head pressure (CHP) sensors. 
     
     
         5 . The method of  claim 4 , wherein the plurality of sensors further includes one or more gas rate sensors. 
     
     
         6 . The method of  claim 4 , wherein the plurality of sensors further includes one or more temperature sensors. 
     
     
         7 . The method of  claim 1 , wherein configuring the sensor data captured by the plurality of sensors includes pre-processing the sensor data by performing one or more of:
 removing outliers from the sensor data based on distances between values of samples of the sensor data and a mean value of other samples from the sensor data;   removing noise from the sensor data by applying a smoothing algorithm to reduce data fluctuations within the sensor data.   
     
     
         8 . The method of  claim 7 , wherein configuring the sensor data captured by the plurality of sensors further includes applying a fast-labeling model to the pre-processed sensor data to generate preliminary labels for the sensor data prior to applying the liquid loading classification model to the sampled input data. 
     
     
         9 . The method of  claim 1 , wherein the liquid loading classification model is trained to predict instances of liquid loading based at least in part on a liquid loading severity metric, the liquid loading severity metric being determined based on an average of a gas rate metric and a pressure metric. 
     
     
         10 . The method of  claim 9 , wherein the gas rate metric is based on a maximum observed gas rate over a predetermined period of time, and wherein the pressure metric is based on a difference between a case heading pressure (CHP) and a tubing head pressure (THP) observed by the plurality of sensors. 
     
     
         11 . The method of  claim 1 , further comprising causing a computing device to generate and display a presentation including a visualization of any predicted instances of liquid loading based on the output of the liquid loading classification model. 
     
     
         12 . A system, comprising:
 at least one processor;   memory in electronic communication with the at least one processor; and   instructions stored in the memory, the instructions being executable by the at least one processor to:
 collect historical data associated with a well site, the historical data including historical data samples captured by a plurality of sensors and associated labels indicating instances of liquid loading corresponding to the historical data samples; 
 train a liquid loading classification model based on the data samples and associated labels from the historical data; 
 obtain input sample data, wherein obtaining the input sample data includes configuring sensor data captured by the plurality of sensors to be provided as input to the liquid loading classification model; 
 apply the liquid loading classification model to the input sample data to generate an output including liquid loading labels indicating predicted instances of liquid loading in connection with the sensor data captured by the plurality of sensors and associated timestamps of the sensor data. 
   
     
     
         13 . The system of  claim 12 , wherein the liquid loading classification model is a recursive neural network (RNN) model trained to predict instances of liquid loading based on series of given data samples captured by a given plurality of sensors associated with a given well site. 
     
     
         14 . The system of  claim 12 , wherein the plurality of sensors includes:
 one or more tubing head pressure (THP) sensors;   one or more casing head pressure (CHP) sensors;   one or more gas rate sensors; and   one or more temperature sensors.   
     
     
         15 . The system of  claim 12 , wherein configuring the sensor data captured by the plurality of sensors includes pre-processing the sensor data by performing one or more of:
 removing outliers from the sensor data based on distances between values of samples of the sensor data and a mean value of other samples from the sensor data;   removing noise from the sensor data by applying a smoothing algorithm to reduce data fluctuations within the sensor data.   
     
     
         16 . The system of  claim 15 , wherein configuring the sensor data captured by the plurality of sensors further includes applying a fast-labeling model to the pre-processed sensor data to generate preliminary labels for the sensor data prior to applying the liquid loading classification model to the sampled input data. 
     
     
         17 . The system of  claim 12 , wherein the liquid loading classification model is trained to predict instances of liquid loading based at least in part on a liquid loading severity metric, the liquid loading severity metric being determined based on an average of a gas rate metric and a pressure metric, and wherein the gas rate metric is based on a maximum observed gas rate over a predetermined period of time, and wherein the pressure metric is based on a difference between a case heading pressure (CHP) and a tubing head pressure (THP) observed by the plurality of sensors. 
     
     
         18 . The system of  claim 12 , further comprising instructions being executable by the at least one processor to cause a computing device to generate and display a presentation including a visualization of any predicted instances of liquid loading based on the output of the liquid loading classification model. 
     
     
         19 . A non-transitory computer readable medium storing instructions thereon, the instructions being executable by at least one processor to:
 collect historical data associated with a well site, the historical data including historical data samples captured by a plurality of sensors and associated labels indicating instances of liquid loading corresponding to the historical data samples;   train a liquid loading classification model based on the data samples and associated labels from the historical data;   obtain input sample data, wherein obtaining the input sample data includes configuring sensor data captured by the plurality of sensors to be provided as input to the liquid loading classification model;   apply the liquid loading classification model to the input sample data to generate an output including liquid loading labels indicating predicted instances of liquid loading in connection with the sensor data captured by the plurality of sensors and associated timestamps of the sensor data; and   cause a computing device to generate and display a presentation including a visualization of any predicted instances of liquid loading based on the output of the liquid loading classification model.   
     
     
         20 . The non-transitory computer readable medium of  claim 19 , wherein the liquid loading classification model is a recursive neural network (RNN) model trained to predict instances of liquid loading based on series of given data samples captured by a given plurality of sensors associated with a given well site.