Emissions estimations at a hydrocarbon operation location using a data-driven approach

Abstract

A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.

What is claimed is: 
     
         1 . A method, comprising:
 collecting a first set of equipment data from a first hydrocarbon operation location;   collecting emissions data relating to the first hydrocarbon operation location;   training at least one machine-learning model to estimate an emissions factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location;   receiving a second set of equipment data from a second hydrocarbon operation location; and   applying the at least one machine-learning model to the second set of equipment data to estimate an emissions factor of at least one equipment component of the second hydrocarbon operation location.   
     
     
         2 . The method of  claim 1 , further comprising generating total emissions of the second hydrocarbon operation location for a predetermined operation time using the emissions factor of the at least one equipment component of the second hydrocarbon operation location. 
     
     
         3 . The method of  claim 1 , wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations, emissions persistence, and a location of the at least one equipment component of the first hydrocarbon operation location. 
     
     
         4 . The method of  claim 1 , further comprising preprocessing the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data. 
     
     
         5 . The method of  claim 1 , wherein training the at least one machine-learning model comprises training a plurality of machine-learning models, wherein each machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location. 
     
     
         6 . The method of  claim 1 , wherein the emissions factor comprises a methane emissions factor. 
     
     
         7 . The method of  claim 1 , wherein the at least one machine-learning model comprises a deep convolutional neural network (DCNN). 
     
     
         8 . The method of  claim 1 , wherein the emissions data comprises historical leak data extracted from leak detection and repair (LDAR) reports for the at least one equipment component of the first hydrocarbon operation location. 
     
     
         9 . The method of  claim 1 , wherein the emissions data comprises weather characteristics extracted from local weather reports for the first hydrocarbon operation location. 
     
     
         10 . A system comprising:
 a processor; and   a memory that includes instructions executable by the processor for causing the processor to:
 collect a first set of equipment data from a first hydrocarbon operation location; 
 collect emissions data relating to the first hydrocarbon operation location; and 
 train at least one machine-learning model to estimate an emissions factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and emissions data of the first hydrocarbon operation location. 
   
     
     
         11 . The system of  claim 10 , wherein the emissions data comprises emissions rates, emissions plume heights, maximum emissions concentrations, emissions persistence, and a location of the at least one equipment component of the first hydrocarbon operation location. 
     
     
         12 . The system of  claim 10 , wherein the memory further comprises instructions executable by the processor for causing the processor to:
 preprocess the first set of equipment data and the emissions data by standardizing units of the first set of equipment data and the emissions data.   
     
     
         13 . The system of  claim 10 , wherein training the at least one machine-learning model comprises training a plurality of machine-learning models, wherein each machine-learning model of the plurality of machine-learning models is associated with an individual equipment component of the first hydrocarbon operation location. 
     
     
         14 . The system of  claim 10 , wherein the emissions factor comprises a methane emissions factor. 
     
     
         15 . The system of  claim 10 , wherein the at least one machine-learning model comprises a DCNN. 
     
     
         16 . A non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor to perform operations comprising:
 receiving a set of equipment data from a hydrocarbon operation location;   applying at least one trained machine-learning model to the set of equipment data to estimate an emissions factor of at least one equipment component of the hydrocarbon operation location, the at least one trained machine-learning model trained using an initial set of equipment data and initial emissions data from an initial hydrocarbon operation location; and   generating total emissions of the hydrocarbon operation location for a predetermined amount of time using the emissions factor of the at least one equipment component of the hydrocarbon operation location.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein the initial emissions data comprises emission rates, emissions plume heights, maximum emissions concentrations, emissions persistence and a location for at least one equipment component of the initial hydrocarbon operation location. 
     
     
         18 . The non-transitory computer-readable medium of  claim 16 , wherein the emissions factor comprises a methane emissions factor. 
     
     
         19 . The non-transitory computer-readable medium of  claim 16 , wherein the at least one trained machine-learning model comprises a DCNN. 
     
     
         20 . The non-transitory computer-readable medium of  claim 16 , wherein the initial emissions data comprises weather characteristics extracted from local weather reports for the initial hydrocarbon operation location.