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US 12,614,384 B2Granted
Unlit flare detection using satellite images
AssigneeSchlumberger Technology Corporation
InventorsFrancisco Jose Gomez, Andrew Pomerantz, Athithan Dharmaratnam, Gelmis Radeckis
Life of this patent
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Abstract
A method can include receiving data that include satellite data of a region of interest where the region of interest includes multiple hydrocarbon production sites that include gas flaring equipment, identifying one or more unlit gas flares at one or more of the multiple hydrocarbon production sites by using a trained machine learning model and at least a portion of the data and, for an unlit gas flare, issuing an instruction related to operation of the gas flaring equipment in the region of interest.
Claims17
A method comprising:
- receiving data that comprise satellite data of a region of interest that includes multiple hydrocarbon production sites that comprise gas flaring equipment, wherein the data includes weather data for the region of interest;
- processing at least a portion of the satellite data using the weather data for the region of interest, wherein the processing includes filtering based on atmospheric conditions indicated by the weather data for the region of interest for reducing false identification of an unlit gas flare state at one or more of the multiple hydrocarbon production sites;
- identifying one or more unlit gas flares at the one or more of the multiple hydrocarbon production sites using a trained machine learning model and at least a portion of the data, wherein the trained machine learning model is trained using at least spatial and temporal satellite data to classify gas flares in the region of interest;
- and
- based at least in part on the identifying, issuing at least one instruction related to operation of the gas flaring equipment in the region of interest.
2 more elements
A system comprising:
- a processor;
- a memory accessible by the processor;
- and
- processor-executable instructions stored in the memory that are executable to instruct the system to:
- receive data that comprise satellite data of a region of interest that includes multiple hydrocarbon production sites that comprise gas flaring equipment, wherein the data includes weather data for the region of interest;
- process at least a portion of the satellite data using the weather data for the region of interest, wherein the processing includes filtering based on atmospheric conditions indicated by the weather data for the region of interest for reducing false identification of an unlit gas flare state at one or more of the multiple hydrocarbon production sites;
- identify one or more unlit gas flares at the one or more of the multiple hydrocarbon production sites using a trained machine learning model and at least a portion of the data, wherein the trained machine learning model is trained using at least spatial and temporal satellite data to classify gas flares in the region of interest;
- and
- based at least in part on identification of the one or more unlit gas flares, issue at least one instruction related to operation of the gas flaring equipment in the region of interest.
6 more elements
One or more non-transitory computer-readable storage media comprising computer-executable instructions executable to instruct a computer to:
- receive data that comprise satellite data of a region of interest that includes multiple hydrocarbon production sites that comprise gas flaring equipment, wherein the data includes weather data for the region of interest;
- process at least a portion of the satellite data using the weather data for the region of interest, wherein the processing includes filtering based on atmospheric conditions indicated by the weather data for the region of interest for reducing false identification of an unlit gas flare state at one or more of the multiple hydrocarbon production sites;
- identify one or more unlit gas flares at the one or more of the multiple hydrocarbon production sites using a trained machine learning model and at least a portion of the data, wherein the trained machine learning model is trained using at least spatial and temporal satellite data to classify gas flares in the region of interest;
- and
- based at least in part on identification of the one or more unlit gas flares, issue at least one instruction related to operation of the gas flaring equipment in the region of interest.
2 more elements
Dependencies are inferred from the claim text.