Method and apparatus for methane leakage detection
Systems and methods presented herein generally relate to greenhouse gas emission management and, more particularly, to greenhouse gas emission management systems and methods for performing greenhouse gas detection sensor placement planning, leakage source tracing, and quantification of leakage source detections for oil and gas production facilities.
1. A gas emission analysis (GEA) system, comprising:
one or more memories configured to store a digital representation of a worksite comprising a gas plume model; and
one or more processors configured to execute instructions stored in the one or more memories to perform actions comprising:
receiving input data from a plurality of data sources comprising at least a plurality of sensors;
determining one or more potential sensor locations associated with the plurality of sensors using the gas plume model based on the input data;
generating a plurality of candidate sensor deployment plans based on the one or more potential sensor locations;
determining a sensor deployment plan from the plurality of candidate sensor deployment plans using the gas plume model based at least in part on plan coverage ratios and plan cost associated with the plurality of candidate sensor deployment plans;
deploying the plurality of sensors based on the sensor deployment plan;
setting one or more gas leakage event trigger logics for a subset of the plurality of sensors;
receiving updated input data from the plurality of data sources;
determining whether at least a portion of the input data activates at least one of the one or more gas leakage event trigger logics;
receiving a list of effective gas concentration measurements from the subset of the plurality of sensors associated with a plurality of potential gas leak sources;
generating a plurality of estimation metrics based on one or more gas concentration simulations;
ranking the plurality of potential gas leak sources based on the plurality of estimation metrics; and
generating a recommendation configured to prompt a user or a device to deploy a detector to the worksite based on the recommendation.
2. The GEA system of claim 1 , wherein the one or more gas leakage event trigger logics are set during a sensor setting phase for the subset of the plurality of sensors, wherein when a reading of a sensor of the plurality of sensors is above a threshold, the GEA system automatically triggers a gas leak source tracing for determining a gas leak source.
3. The GEA system of claim 1 , wherein the list of effective gas concentration measurements comprise sensor location data, gas concentration readings associated with time stamps, wind speed, wind direction at the time stamps, and coordinates associated with the plurality of potential gas leak sources.
4. The GEA system of claim 1 , wherein the plurality of potential gas leak sources are determined using the digital representation based at least in part on a history of gas leakages being detected at the worksite, and synthetic data from simulation data based on modeling various gas leakage events associated with the worksite.
5. The GEA system of claim 1 , wherein the plurality of estimation metrics are generated based on simulated gas concentrations versus measured gas concentrations, wherein the plurality of estimation metrics are used to estimate at least a gas flow rate.
6. The GEA system of claim 1 , wherein the plurality of estimation metrics comprises regression metrics or R2 goodness-of-fit indicators.
7. The GEA system of claim 1 , wherein at least one of the plurality of sensors is configured to be deployed by the user or the robotic device, wherein the robotic device comprise an unmanned vehicle or a drone.
8. The GEA system of claim 1 , wherein the recommendation comprises one or more locations associated with one or more of the plurality of potential gas leak sources.
9. The GEA system of claim 8 , wherein the detector is configured to perform data measurement and validate a gas leakage at the one or more locations based on the data measurement.
10. The GEA system of claim 1 , wherein determining the one or more potential sensor locations or determining the sensor deployment plan comprises interactive guided sensor placement using one or more interactive functions configured to support data analysis, data processing, and data simulations associated with gas emissions based on the input data, wherein the plurality of interactive functions comprises data mining, numerical calculation, modeling, machine learning, prediction, estimations, error analysis, and data visualization.
11. The GEA system of claim 1 , wherein the digital representation includes a virtual representation of the worksite in a computational environment, wherein the virtual representation is updated using the updated input data, and wherein the virtual representation of the worksite is configured to use the gas plume model to perform simulations, machine learning, and data reasoning based on the updated input data.
12. The GEA system of claim 1 , wherein the plurality of sensors comprises one or more gas sensors, one or more Optical Gas Imaging (OGI) cameras, and one or more meteorological sensors communicatively coupled to the GEA system.
13. The GEA system of claim 12 , wherein the one or more gas sensors comprise gas composition sensors, gas specific sensors, noise or acoustic sensors, flow rate sensors, pressure sensors, wind sensors, temperature sensors, light sensors, flame sensors, flare monitors, tank sensors, gas concentration monitors, compressor health monitors, structural monitors, pipeline monitors, any other type of sensors capable of providing data related to gas emissions, or any combination thereof.
14. The GEA system of claim 1 , comprising a user interface (UI) configured to enable the user to:
interactively change a plurality of parameters comprising gas leak source identifications, gas leak time, and lagging time;
view one or more peak concentration distributions instantaneously in response to changing the plurality of parameters; and
modify one or more indicators associated with one or more sensors of the plurality of sensors that detected a gas leakage at the worksite.
15. A method comprising:
receiving input data from a plurality of sensors;
determining one or more potential sensor locations associated with the plurality of sensors using a gas plume model based on the input data;
generating a plurality of candidate sensor deployment plans based on the one or more potential sensor locations;
determining a sensor deployment plan from the plurality of candidate sensor deployment plans using the gas plume model based at least in part on plan coverage ratios and plan cost associated with the plurality of candidate sensor deployment plans;
deploying the plurality of sensors based on the sensor deployment plan;
setting one or more gas leakage event trigger logics for a subset of the plurality of sensors;
receiving updated input data from the plurality of sensors;
determining whether at least a portion of the input data activates at least one of the one or more gas leakage event trigger logics;
receiving a list of effective gas concentration measurements from the subset of the plurality of sensors associated with a plurality of potential gas leak sources;
generating a plurality of estimation metrics based on one or more gas concentration simulations;
ranking the plurality of potential gas leak sources based on the plurality of estimation metrics; and
generating a recommendation configured to prompt a user or a device to deploy a detector to a worksite based on the recommendation.
16. The method of claim 15 , wherein the gas plume model is generated using one or more mathematic algorithms associated with data analysis, data processing, data simulations, and data correlations related to a gas emission.
17. The method of claim 16 , wherein the gas emission comprises a gas distribution, a gas concentration, and a gas flow detection associated with a greenhouse gas released from the worksite.
18. The method of claim 17 , wherein the greenhouse gas comprises methane.
19. The method of claim 16 , wherein the data processing comprises image processing, wherein the image processing comprises at least image filtering, Fourier transformation, and pattern recognition based on image data acquired by Optical Gas Imaging (OGI) cameras.
20. The method of claim 16 , wherein the data correlations comprise correlating sensor data acquired by the plurality of sensors with location data, time data, and weather data.
21. A non-transitory, computer-readable medium storing instructions that, when executed by one or more processors, are configured to cause the one or more processors to perform operations comprising:
deploying a plurality of sensors based on a sensor deployment plan;
setting one or more gas leakage event trigger logics for the plurality of sensors;
receiving input data from a plurality of data sources comprising the plurality of sensors;
determining whether at least a portion of the input data activates at least one of the one or more gas leakage event trigger logics;
receiving a list of effective gas concentration measurements from a subset of the plurality of sensors associated with a plurality of potential gas leak sources;
generating a plurality of estimation metrics based on one or more gas concentration simulations;
ranking the plurality of potential gas leak sources based on the plurality of estimation metrics; and
generating a recommendation configured to prompt a user or a device to deploy a detector to a worksite based on the recommendation.
22. The non-transitory, computer-readable medium of claim 21 , wherein ranking the plurality of potential gas leak sources comprises:
sorting the plurality of potential gas leak sources; and
generating a ranking list comprising one or more candidates along with estimated gas leakage rate.