Abstract: Various embodiments of the present technology relate to solutions for hydrocarbon equipment monitoring. In some examples, a detection system comprises a compute engine, a gimbal, a sensor suite, and an imaging system. The compute engine generates signaling that directs the gimbal to orient the imaging system, signaling that directs the imaging system to image the equipment, and signaling that directs the sensor suite to sense the equipment. The gimbal orients the imaging system based on the signaling. The imaging system images the equipment and transfers images depicting the storage equipment to the compute engine. The sensor suite senses the equipment and transfers sensor data that characterizes the equipment to the compute engine. The compute engine processes the data with a machine learning engine trained to determine the status of the equipment. The compute engine transfers a machine learning output that indicates the status to a downstream system.

Abstract: Various embodiments of the present technology relate to solutions for hydrocarbon extraction systems. In some examples, a system models a hydrocarbon extraction site. The system comprises processing circuitry. The processing circuitry obtains a topographically accurate map of the hydrocarbon extraction site and populates the map with digital assets that correspond to equipment and a monitoring system in the hydrocarbon extraction site. The processing circuity obtains sensor data for the equipment from on-site sensors and obtains image data depicting the site from the monitoring system. The processing circuity converts the sensor data into a format interpretable by a thermodynamic model and provides the converted data to the model. The processing circuity receive an output from the model that comprises process values for the equipment. The processing circuity adds the process values and the image data to the digital assets to create a model of the hydrocarbon extraction site.

Abstract: Various embodiments of the present technology relate to solutions for gas leak detection in natural gas extraction and storage environments. In some examples, a machine learning interface generates feature vectors based on video data that depicts a natural gas storage facility and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors and generates a machine learning output that indicates the presence of the gas leak in the natural gas storage facility. The machine learning interface receives the machine learning output that indicates the presence of a gas leak in the natural gas storage facility. The machine learning interface generates and transfers an alert based on the machine learning output.

Abstract: Various embodiments of the present technology relate to systems and methods to determine fill levels in a fuel extraction and storage environment. In some examples, a system comprises a thermal imaging device, a machine learning interface, and a machine learning engine. The thermal imaging device generates a thermal image that depicts fuel storage equipment. The machine learning interface generates feature vectors based on the thermal image that depicts the fuel storage equipment and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors, generates a machine learning output that indicates a fill level for the fuel storage equipment based on the feature vectors, and transfers the machine learning output.

Abstract: A control method and system for managing aircraft fueling operations operated by a fleet operator wherein the fleet operator has a central computer system and operates at multiple locations. The control method and system utilizes hand-held control terminals with GPS receivers and mobile refueling apparatuses with GPS receivers. The control method comprising the steps of: verifying that an aircraft is at the expected/proper gate by detecting an aircraft identification indicia and communicating the indicia to the central computer system; verifying that a refueling operator is certified to fuel the aircraft; verifying that the hand-held control terminal is at the proper gate by comparing its GPS location with an expected GPS location for the hand-held control terminal; and verifying that the refueling apparatus is at the proper gate by comparing its GPS location with an expected GPS location for the refueling apparatus.

Abstract: Various embodiments of the present technology relate to solutions for hydrocarbon equipment monitoring. Some embodiments include a system comprising a multimodal sensor platform, a collapsible lift, and a vehicle. The vehicle mounts and transports the collapsible lift. The collapsible lift mounts and elevates the multimodal sensor platform. The multimodal sensor platform generates infrared video data and visible spectrum video data of hydrocarbon storage equipment. The multimodal sensor platform generates feature vectors that numerically represent the infrared video data. The multimodal sensor platform provides the feature vectors to machine learning models trained to detect leaks and measure fill levels of the hydrocarbon storage equipment. The multimodal sensor platform obtains machine learning outputs that indicate when a leak exists and the fill level of the hydrocarbon storage equipment.

Abstract: Various embodiments of the present technology relate to emission monitoring. Some embodiments comprise an exhaust testing system to characterize exhaust gas composition. The exhaust testing system comprises a sampling system and a gas analyzer. The sampling system is coupled to an exhaust stack of a combustion system. The sampling system comprises a cage, sampling pipes, and valves. The cage is mounted to the opening of the exhaust stack. The sampling pipes are mounted to the cage. The sampling pipes capture exhaust gas generated by the combustion system and emitted through the opening of the exhaust stack. The valves control gas flow through the sampling pipes. The gas analyzer is coupled to the sampling pipes. The gas analyzer determines gas composition of the exhaust gas.

Abstract: Various embodiments of the present technology relate to solutions for leak detection in hydrocarbon storage environments. In some examples, a leak identification system confirms sensor outputs in a hydrocarbon storage environment. The leak identification system comprises processing circuitry. The processing circuitry obtains sensor data that characterizes hydrocarbon inputs and hydrocarbon outputs in the hydrocarbon storage environment. The processing circuitry processes the sensor data using a thermodynamic model to determine when a discrepancy exists between the hydrocarbon inputs and the hydrocarbon outputs. The processing circuitry generates feature vectors that represent video data that depicts the hydrocarbon storage environment. The processing circuitry provides the feature vectors as input to a machine learning engine trained to detect hydrocarbon leaks in the hydrocarbon storage environment.

Abstract: Various embodiments of the present technology relate to systems and methods to determine fill levels in a fuel extraction and storage environment. In some examples, a system comprises a thermal imaging device, a machine learning interface, and a machine learning engine. The thermal imaging device generates a thermal image that depicts fuel storage equipment. The machine learning interface generates feature vectors based on the thermal image that depicts the fuel storage equipment and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors, generates a machine learning output that indicates a fill level for the fuel storage equipment based on the feature vectors, and transfers the machine learning output.

Abstract: Various embodiments of the present technology relate to solutions for hydrocarbon equipment monitoring. In some examples, a detection system comprises a compute engine, a gimbal, a sensor suite, and an imaging system. The compute engine generates signaling that directs the gimbal to orient the imaging system, signaling that directs the imaging system to image the equipment, and signaling that directs the sensor suite to sense the equipment. The gimbal orients the imaging system based on the signaling. The imaging system images the equipment and transfers images depicting the storage equipment to the compute engine. The sensor suite senses the equipment and transfers sensor data that characterizes the equipment to the compute engine. The compute engine processes the data with a machine learning engine trained to determine the status of the equipment. The compute engine transfers a machine learning output that indicates the status to a downstream system.

Abstract: Various embodiments of the present technology relate to solutions for gas leak detection in natural gas extraction and storage environments. In some examples, a machine learning interface generates feature vectors based on video data that depicts a natural gas storage facility and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors and generates a machine learning output that indicates the presence of the gas leak in the natural gas storage facility. The machine learning interface receives the machine learning output that indicates the presence of a gas leak in the natural gas storage facility. The machine learning interface generates and transfers an alert based on the machine learning output.

Abstract: A fueling data collection unit associated with a fuel transfer apparatus and for use with a system for managing fueling transactions of a fleet operator using fuel transfer apparatuses at one or more locations includes a fueling data interface module and a temperature compensation module. The temperature compensation module compensates for a volume variance of the fuel as a result of temperature variance from the standard temperature data to determine a volume correction factor at the measured fuel temperature, while the temperature compensation module uses the measured fuel temperature to determine a compensated, more accurate volume and/or mass of fuel dispensed through the fuel meter. In this way, the fuel data collection unit can accurately measure the volume and/or mass of fuel being delivered. This allows the provider and the recipient to more accurately sell/buy what was actually delivered and can avoid over-filling a fueling order.

Abstract: A fueling data collection unit associated with a fuel transfer apparatus and for use with a system for managing fueling transactions of a fleet operator using fuel transfer apparatuses at one or more locations includes a fueling data interface module and a temperature compensation module. The temperature compensation module compensates for a volume variance of the fuel as a result of temperature variance from the standard temperature data to determine a volume correction factor at the measured fuel temperature, while the temperature compensation module uses the measured fuel temperature to determine a compensated, more accurate volume and/or mass of fuel dispensed through the fuel meter. In this way, the fuel data collection unit can accurately measure the volume and/or mass of fuel being delivered. This allows the provider and the recipient to more accurately sell/buy what was actually delivered and can avoid over-filling a fueling order.

Abstract: A battery-powered fueling data collection unit for use with a meter and a register associated with a fuel transfer apparatus and for use with a system for managing fueling transactions of a fleet operator using fuel transfer apparatuses at multiple locations includes a fueling data interface module for receiving fueling information from the fueling meter and/or register and an internal battery power source. A processor is provided for monitoring the power level in the battery and for detecting if the power level in the battery drops below a threshold amount. Also, a communications module is provided for wirelessly forwarding an alert to a remote computer to alert the remote computer that the battery power is low.

Abstract: A battery-powered fueling data collection unit for use with a meter and a register associated with a fuel transfer apparatus and for use with a system for managing fueling transactions of a fleet operator using fuel transfer apparatuses at multiple locations includes a fueling data interface module for receiving fueling information from the fueling meter and/or register and an internal battery power source. A processor is provided for monitoring the power level in the battery and for detecting if the power level in the battery drops below a threshold amount. Also, a communications module is provided for wirelessly forwarding an alert to a remote computer to alert the remote computer that the battery power is low.

Abstract: A throttle body for use in the air intake system of a motor vehicle comprising a throttle body defining a throttle bore. The throttle plate is rotatably mounted within the throttle bore, having an outside diameter smaller than an inside diameter of the throttle bore. A plurality of fins, located on the throttle plate, extend from the throttle plate in a direction generally perpendicular to a plane defined by said throttle plate. The fins are optimized in number, thickness, spacing, length, shape, and angle to reduce air-rush noise without impacting engine performance.

Abstract: A throttle body for use in the air intake system of a motor vehicle comprising a throttle body defining a throttle bore. The throttle plate is rotatably mounted within the throttle bore, having an outside diameter smaller than an inside diameter of the throttle bore. A plurality of fins, located on the throttle plate, extend from the throttle plate in a direction generally perpendicular to a plane defined by said throttle plate. The fins are optimized in number, thickness, spacing, length, shape, and angle to reduce air-rush noise without impacting engine performance.