Systems and methods for productivity analysis of oil and gas production systems

Abstract

Implementations claimed and described herein provide systems and methods for analyzing natural resource production. The systems and methods use a machine learning model to generate estimated sensor data associated with input data. The machine learning model is built from historical data.

What is claimed is: 
     
         1 . A system for analyzing natural resource production comprising:
 a processing system in communication with a computing device, one or more sensors, and one or more databases over a network, the computing device having one or more input systems and one or more output systems, the processing system configured to receive input data from the one or more sensors and the one or more databases;   a sensor data generation system having a machine learning model, the sensor data generation system configured to generate estimated bottomhole pressure data for the input data using the machine learning model, the machine learning model built from historical data; and   a productivity data generation system configured to generate productivity index data for one or more natural resource production systems using the estimated bottomhole pressure data.   
     
     
         2 . The system of  claim 1  further comprising:
 an output data generation system configured to generate a notification associated with the productivity index data, the processing system configured to transmit the notification to the computing device to cause the notification to be presented using the one or more output systems. 
 
     
     
         3 . The system of  claim 2 , wherein the notification includes a plot of the productivity index data. 
     
     
         4 . The system of  claim 1 , wherein the computing device includes at least one of a smartphone, a tablet, a desktop computer, a laptop computer, or a personal computing device. 
     
     
         5 . The system of  claim 1 , wherein the input data includes at least one of well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ration, or liquid rate. 
     
     
         6 . The system of  claim 1 , wherein the productivity data generation system normalizes the productivity index data by a completed permeability-length product. 
     
     
         7 . The system of  claim 1 , wherein the productivity index data is generated using reservoir model data. 
     
     
         8 . The system of  claim 7 , wherein the reservoir model data includes a reservoir pressure determined using a historically matched numerical model. 
     
     
         9 . A method for analyzing natural resource production comprising:
 receiving input data from one or more sensors and one or more databases;   generating estimated bottomhole pressure data based on the input data using a machine learning model, the machine learning model built using historical data;   generating productivity index data for one or more natural resource production systems using the estimated bottomhole pressure data; and   generating output data based on the productivity index data.   
     
     
         10 . The method of  claim 9 , further comprising:
 transmitting the output data to a computing device to cause the output data to be presented using one or more output systems of the computing device.   
     
     
         11 . The method of  claim 10 , wherein the output data includes a notification associated with the productivity index data. 
     
     
         12 . The method of  claim 11 , wherein the notification includes a plot of the productivity index data. 
     
     
         13 . The method of  claim 10 , wherein the computing device includes at least one of a smartphone, a tablet, a desktop computer, a laptop computer, or a personal computing device. 
     
     
         14 . The method of  claim 9 , wherein the input data includes at least one of well head pressure, well head temperature, gas lift rate, watercut, gas-liquid ration, or liquid rate. 
     
     
         15 . The method of  claim 9 , further comprising:
 normalizing the productivity index data by a completed permeability-length product.   
     
     
         16 . The method of  claim 9 , wherein the productivity index data is generated using reservoir model data. 
     
     
         17 . The method of  claim 16 , wherein the reservoir model data includes a reservoir pressure determined using a historically matched numerical model. 
     
     
         18 . The method of  claim 9 , further comprising:
 processing the input data before inputting the input data into the machine learning model.   
     
     
         19 . A method comprising:
 receiving historical data associated with one or more natural resource production systems;   generating a training data set based on the historical data;   training a machine learning model using the training data set; and   validating the machine learning model by comparing estimated bottomhole pressure generated using the machine learning model with sensor data including measured bottom hole pressure.   
     
     
         20 . The method of  claim 19 , further comprising:
 implementing the machine learning model into production based on the validating.