Abstract: A video clip showing a wellsite activity can be extracted from a video and then labeled for use in training a machine-learning model in some examples described herein. In one such example, a system can train a model with a set of training data to identify an object and a corresponding spatial location of the object in each image frame in a video depicting a wellsite activity. The system can analyze image frames in the video using the trained model to identify a target image frame in which the object is present in a predefined spatial area thereof, where the predefined spatial area is associated with the wellsite activity. The system can then generate a video clip that includes only a subpart of the video based on the target image frame, where the subpart includes a series of consecutive image frames containing the target image frame.

Abstract: Systems and methods to determine an activity associated with an object of interest are presented. The method includes receiving a sequence of a plurality of frames that capture an area of interest during a threshold period of time, wherein an object of interest is present in the area of interest during the threshold period of time. The method also includes analyzing the plurality of frames to detect the object of interest. The method also includes tracking the object of interest in the area of interest during the threshold period of time. The method also includes extracting data indicative of the object of interest. The method also includes predicting, based on the data indicative of the object of interest, an activity associated with the object of interest.

Abstract: A video clip showing a wellsite activity can be extracted from a video and then labeled for use in training a machine-learning model in some examples described herein. In one such example, a system can train a model with a set of training data to identify an object and a corresponding spatial location of the object in each image frame in a video depicting a wellsite activity. The system can analyze image frames in the video using the trained model to identify a target image frame in which the object is present in a predefined spatial area thereof, where the predefined spatial area is associated with the wellsite activity. The system can then generate a video clip that includes only a subpart of the video based on the target image frame, where the subpart includes a series of consecutive image frames containing the target image frame.

Abstract: The value for equipment to be replaced can be maximized by determining a threshold cutoff value for a failure prediction indicator and a window size for obtaining the threshold cutoff value for a piece of oil or gas drilling or recovery equipment; applying the threshold cutoff value and the window size to an equipment failure prediction model; and deriving a recall value and an average hour-loss value from the equipment failure prediction model. Predictive maintenance for the piece of oil or gas drilling or recovery equipment may be performed based on the recall value and the average hour-loss value to perform predictive maintenance for a piece of equipment in an oil or gas recovery operation.

Abstract: The value for equipment to be replaced can be maximized by determining a threshold cutoff value for a failure prediction indicator and a window size for obtaining the threshold cutoff value for a piece of oil or gas drilling or recovery equipment; applying the threshold cutoff value and the window size to an equipment failure prediction model; and deriving a recall value and an average hour-loss value from the equipment failure prediction model. Predictive maintenance for the piece of oil or gas drilling or recovery equipment may be performed based on the recall value and the average hour-loss value to perform predictive maintenance for a piece of equipment in an oil or gas recovery operation.

Abstract: A virtual life meter can track the operational status of equipment used in the oil and gas industry. Historical characteristics corresponding to usage of one or more fracking devices may be received. A feature set can be generated for each fracking device using the historical characteristics. The feature sets can be used to train a machine-learning model training. Once trained, operational characteristics for fracking devices may be received and processed using the trained machine-learning model. The machine-learning model can be used to generate service objects for the fracking devices using the operational characteristics. The service objects provide an indication of an amount of time operational life remaining for each fracking device. Upon receiving a request for operational characteristics for a particular fracking device, the corresponding service object associated with the particular fracking device can be transmitted.

Abstract: Various embodiments include a system having: a computing device configured to monitor a gas turbine (GT) by: determining a moisture content and/or an oxygen content of inlet air entering the inlet of the GT compressor section; determining a corresponding one of the moisture content and/or the oxygen content of exhaust gas from the GT turbine section; calculating a flow rate of the exhaust gas from the turbine section and a flow rate of the inlet air entering the inlet of the compressor section based upon the moisture content and/or the oxygen content of the inlet air and the exhaust gas; and calculating a temperature of the exhaust gas and an energy of the exhaust gas from the turbine section based upon the flow rate of the exhaust gas from the turbine and the flow rate of the inlet air entering the inlet of the compressor section.

Abstract: Various embodiments include a system having: a computing device configured to monitor a gas turbine (GT) by: determining a moisture content and/or an oxygen content of inlet air entering the inlet of the GT compressor section; determining a corresponding one of the moisture content and/or the oxygen content of exhaust gas from the GT turbine section; calculating a flow rate of the exhaust gas from the turbine section and a flow rate of the inlet air entering the inlet of the compressor section based upon the moisture content and/or the oxygen content of the inlet air and the exhaust gas; and calculating a temperature of the exhaust gas and an energy of the exhaust gas from the turbine section based upon the flow rate of the exhaust gas from the turbine and the flow rate of the inlet air entering the inlet of the compressor section.

Abstract: Systems and methods for model based control optimization for gas turbine systems. In one embodiment, a nonlinear optimization process with multiple nonlinear constraints create a set of control schedules that can work across an ambient range based on specific unit characteristics derived from a single test ambient. As part of model based control for a gas turbine system, all of the control schedules are defined based on a nominal unit. Multiple control loops are active across the ambient range with each control loop being active at a different ambient condition. This optimization process provides a set of control schedules to provide optimized performance for a specific gas turbine system or unit over a wide operation range.

Abstract: Systems and methods for model based control tuning for gas turbine systems. In one embodiment, a nonlinear optimization process with multiple nonlinear constraints can create a set of control schedules that can work across an ambient range based on specific unit characteristics derived from a single test ambient. As part of model based control for a gas turbine system, all of the control schedules can be defined based on a nominal unit. Multiple control loops may be active across the ambient range with each control loop being active at a different ambient condition. This tuning process can provide a set of control schedules to provide improved performance for a specific gas turbine system or unit over a wide operation range.