System and Method for a Remotely Deployable, Off-Grid System to Autonomously Detect, Quantify, and Automatically Report Emissions of Methane and Other Gases to the Atmosphere

Abstract

A system and method for a remotely deployable, off-grid system to autonomously detect, quantify, and automatically wort emissions of methane (CH.sub.4) and other gases to the atmosphere, Automated CH.sub.4 emissions detection is accomplished by the use of commercially available CH.sub.4 sensors. CH.sub.4 accuracy is maximized by simultaneously measuring, and accounting for, undesired CH.sub.4 sensor response from interfering gases such as carbon monoxide (CO) and water vapor (H.sub.2O), and undesired CH.sub.4 sensor response from ambient temperature (T) changes. Automated CH.sub.4 emissions quantification is accomplished by calculating a leak rate (mass or volume per unit time) from the measured concentration enhancements using simultaneous measurements of wind speed and direction. Automated CH emissions reporting is accomplished following transmission of measured CH.sub.4concentrations via cellular wireless, radio, or satellite link to a central cloud-based server. Remote off-grid operation is accomplished by solar, wind, or other renewable energy source(s) that charge an on-board battery. This system offers a robust, unattended, and continuous CH.sub.4 monitoring and reporting capability to permit improved accuracy and efficiency of CH.sub.4 leak detection and repair (LDAR) from sources located in remote areas without electrical power, e.g., leak detection at well pads and processing facilities in oil and gas production areas, at concentrated animal feeding operations, and other methane sources.

Claims

1. A device to selectively and accurately quantify atmospheric CH.sub.4 concentrations, comprised of: One or more low-power, low-cost CH.sub.4 sensors, a temperature sensor, a relative humidity sensor, a data logger, a telemetry capability for wireless communication off-site to a cloud-based server, a renewable power source and batter for unattended, remote, off-grid operation.

2. Optionally, the device as in claim 1, further comprising: One or more low-power, low-cost sensors for other gases of interest, e.g., hydrogen sulfide (H.sub.2S), and/or potential interferences in the CH.sub.4 measurement, e.g., carbon monoxide (CO), hydrogen (H.sub.2), methanol (CH.sub.3OH), ethanol (CH.sub.3CH.sub.2OH). acetone ((CH.sub.3).sub.2(CO)), and other hydrocarbons such as ethane (C.sub.2H.sub.6), propane (C.sub.3H.sub.8), isomers of butane (C.sub.4H.sub.1 ) and longer-chain hydrocarbons.

3. The device as in claim 1, further comprising: a wind speed and direction sensor.

4. Cloud-based software to archive the data, process detector signals, apply calibration data to calculate chemical mixing ratios, and derive leak rates, as well as software that displays raw and processed data as time series, permits data download in various file formats, and prmiuces geolocated results showing probable leak locations and magnitudes, and finally, software that produces automated alerts triggered from calculated leak rates that exceed user-selhtable threshold values.

5. The application of a network of multiple devices and servers in claims 1, 2, 3, and 4 installed to enable fenceline monitoring of gas emissions to the atmosphere, for unattended, automated, off-grid leak detection, quantification, and automatic reporting from a multitude of remote sites.

Description

DRAWING-FIGURES

[0008] FIG. 1 shows a schematic diagram showing major components (exhaust vent, battery, solar controller, sensor and processor board, and intake fan) within the outer solar-powered detector box enclosure; the version using external AC power lacks the battery and controller. Internal and external wiring and connectors are not shown.

[0009] FIG. 2 shows a pole-mounted installation with wind sensor, detector box, and solar panel.

[0010] FIG. 3 shows a screen shot from the system dashboard showing a Google Earth view of an instrumented wellpad with locations of eight detector boxes (numbered circles) and facility components (squares). The footprint for each 15-minute period is automatically generated from wind direction and variability data and shown as a shaded triangle upwind of the detector box registering a leak. In this example, the system automatically and correctly identified the southwestern tank (indicated by the arrow) as the most probable leak source.

DETAILED DESCRIPTION

[0011] The present invention simultaneously measures ambient temperature (T), ambient relative humidity (RH) and optionally ambient carbon monoxide (CO) and corrects the raw CH.sub.4 sensor signal to account for these confounding factors, maximizing CH.sub.4 accuracy and reduce the incidence of “false positive” leak reports. One or more CH.sub.4 detector boxes (FIG. 1) are installed on poles (FIG. 2) around the perimeter of a monitored facility (FIG. 3) to detect CH.sub.4 leaks at the fence line regardless of the prevailing wind direction. At least one box installed at a given CH.sub.4 source location equipped with a wind speed and direction sensor, typically, a sonic anemometer, to permit a leak to be attributed to that site, and the leak rate estimated from measured CH.sub.4 concentration data using mass balance calculations and a plume diversion model. All detector boxes include an automated remote communication ability via cellular wireless, radio, or satellite link to a central cloud-based server. Detector boxes of the present invention typically accept power from a solar, wind, or other renewable energy source that charges an on-board battery for continuous, unattended, remote, off-grid operation. Optionally, detector boxes can accept grid-tied AC or DC input power where available. Data upload is managed to reduce transmission events and the majority of data processing takes place on the cloud-based server to decrease power consumption by the detector box. Power consumption is minimized throughout the detector box by selecting a low-power fan and microprocessor, leading to an overall continuous power draw of <1.5 W to maximize off-grid uptime for a given renewable power configuration. Once data are transmitted to the cloud, the system calculates 1-minute-average chemical mixing ratios and applies an atmospheric dispersion model to derive 15-minute-average leak rates. These data are archived, displayed as time series plots on the dashboard, and made available for download. For each detector box that registers a leak, the system uses measured wind direction and its variability to calculate an upwind source area (“footprint”) for each 15-minute period and generates a .kml file for visualization in Google Earth (FIG. 3). The system further identities facility components located within the footprint of each detected leak to identify those most likely to be the source. Finally, the system alert threshold is user-configurable but by default sends automated text or email alerts when a 4-hour running mean of CH.sub.4 leak rates in excess of 2 standard deviations above a 30-day running mean is detected at a monitored facility. Automated alert information typically includes the facility name, location, leak rate and its uncertainty, and the most probable component(s) to which the leak is attributed as a guide for LDAR team response.

CONCLUSION

[0012] The ability to remotely deploy and autonomously detect, quantify and report emissions of methane and other gases to the atmosphere is an important step in the evolution of emissions calculation and reduction. This will assist energy producers, regulators, researchers, and other interested parties to better understand the emission profiles of various locations. Unlike more traditional methods that provide an emission profile at a particular point in time and/or provide a concentration level with little or no insight into the emissions profile outside of the particular time of measurement and the actual emission rate, this system will provide a more complete emission profile by capturing emission information on a continuous basis and provide an actual estimated omission rate, including calibrations that correct for factors that can impact the emission calculation such as temperature and relative humidity. The autonomous nature of this invention further allows for the continuous monitoring of facilities to occur without the need to have personnel on-site, allowing increased levels of information related to emissions without the need to increase headcount. This invention will allow a user to automatically learn of a situation at a particular facility that is of interest and/or may require attention in near real-time rather than the more traditional approach whereby emissions may go for days, weeks, or months without being detected or addressed. Although the description above contains many specificities, these should not be construed to limit the scope of the utility of this capability but as merely providing illustrations of some of several uses.