Abstract: Some examples described herein relate to producing an optimized composition of a downhole drilling fluid. For example, a system can execute an iterative optimization process to determine an optimized composition of downhole drilling fluid that satisfies at least one objective function and matches a received set of target fluid properties. Each iteration of the iterative optimization process can involve: selecting a mixture of fluid components for the downhole drilling fluid from a search space, providing the selected mixture of fluid components as input to a trained machine-learning model, receiving a set of predicted fluid properties for the mixture of fluid components as output from the trained machine-learning model, and determining whether the set of predicted fluid properties matches the set of target fluid properties. The system can transmit a control signal to a mixing subsystem for causing the mixing subsystem to produce the optimized composition of the downhole drilling fluid.

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: Some examples described herein relate to generating a non-fungible token on a distributed ledger, where the non-fungible token represents a machine-learning model for use in performing hydrocarbon extraction operations. The non-fungible token can also include one or more identifiers of one or more training datasets used to train the machine-learning model.

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: A method for generating an x-ray image in color includes selecting three-sets of x-ray images in gray scale acquired with x-rays having different energy spectra, assigning basic colors RGB to the three-sets, and displaying the x-ray image in color with RGB signals generated. A system for generating an x-ray image in color includes an x-ray generator configured to generate at least three sets of x-rays with different energy spectra, an x-ray detector, a controller, a computer, and a color display. The computer is configured to generate three sets of x-ray images from output data of the x-ray detector acquired for x-rays with different energy spectra, assign RGB and display an x-ray image in color. A non-transitory computer readable medium stores an instruction, when the instruction is executed by a processor, cause the processor to perform the method for generating an x-ray image in color.

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 method for generating an x-ray image in color includes selecting three-sets of x-ray images in gray scale acquired with x-rays having different energy spectra, assigning basic colors RGB to the three-sets, and displaying the x-ray image in color with RGB signals generated. A system for generating an x-ray image in color includes an x-ray generator configured to generate at least three sets of x-rays with different energy spectra, an x-ray detector, a controller, a computer, and a color display. The computer is configured to generate three sets of x-ray images from output data of the x-ray detector acquired for x-rays with different energy spectra, assign RGB and display an x-ray image in color. A non-transitory computer readable medium stores an instruction, when the instruction is executed by a processor, cause the processor to perform the method for generating an x-ray image in color.