Abstract: Systems and methods include a method for implementing a recommendation and advisory systems layer in a drilling and workover operation (D&WO) system. Aggregation functions, defined for ontological frameworks modeling categories of components of a facility, define and aggregate information from target components of Things, Events, and Methods categories. Source data, received in real-time from disparate sources/formats, provides information about the components of the facility and external systems. A quality assurance (QA) layer with services from multiple providers applies ensemble techniques on external systems outputs, achieving higher reliability levels. Using the aggregation functions, the source data is aggregated to form the ontological frameworks. Each ontological framework models a component of the Things category, a component of the Events category, or a component of the Methods category.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for monitoring the flow of drilling mud from a wellbore by image processing. One method includes: capturing, using a digital imaging device, images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system that circulates drilling mud through the wellbore and a wellbore drilling assembly; receiving, by one or more processors of a computer system operatively coupled to the digital imaging device, the images captured by the digital imaging device; and processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones.

Abstract: Provided is a method of drilling a hydrocarbon well that includes conducting a drilling operation, collecting drilling data including characteristics of the drilling operation over a timespan, determining (based on the drilling data) drilling conditions for instants of time within the timespan, determining (based on application of the drilling conditions) preliminary classifications identifying a preliminary classification of the drilling operation for instants of time within the timespan, determining (based on the preliminary classifications) a series of classifications for the drilling operation that each indicate a determined classification for a respective instant of time, determining (based on the series of classifications) a change of classifications, conducting (in response to determining the change of classifications) a change point detection to identify a time of the change of classifications, generating drilling characteristic data indicating the time, and conducting the drilling operation in accord

Abstract: A method includes receiving, by a computer system, a video of a visible state of a component of a generator, the generator powering at least a portion of a rig equipment system at a wellsite. The computer system can determine an operational parameter based on the visible state of the component of the generator imaged in the video, and can transmit the operational parameter to an output device.

Abstract: Analysis of drilling slurry solids by image processing includes receiving images of a solid slurry including a mixture of wellbore drilling mud and solid objects found in the wellbore while drilling the wellbore through a subterranean zone. The solid objects include drill cuttings and non-drilled solids. By implementing image processing techniques on the images, the solids in the solid slurry are classified a drill cutting or as a non-drilled solid.

Abstract: A method for determining a slips status during a drilling operation of a subterranean formation. The method includes capturing, using one or multiple camera devices mounted on a drilling rig of the drilling operation, a plurality of images, each of the plurality of images comprising a portion that corresponds to a slips device of the drilling rig, generating, using a sensor device of the drilling rig, a plurality of parameters of the drilling rig, wherein the plurality of parameters are synchronized with the plurality of images, providing, by a computer processor, the plurality of parameters as input to a machine learning model of the drilling rig, and analyzing, by the computer processor and based on the machine learning model, the plurality of images to generate the slips status.

Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: capturing, using an image sensor directed at a drilling system, an image feed of movement of a component of the drilling system with respect to a vertical reference line; converting the image feed into a digital representation of the movement of the component, the digital representation including a number of offset pixels from the component to the vertical reference line in the image feed; converting the digital representation into a machine learning (ML), the ML representation including a plurality of vectors each including the number of offset pixels from the component to the vertical reference line at a respective time; training a ML model using the ML representation to characterize the movement of the component as normal or abnormal; and using the trained ML model to characterize the movement of the component in real-time as normal or abnormal.

Abstract: A system and method control at least one rig by receiving key performance indicator (KPI) data of the at least one rig among a set of rigs, including at least one of flat time performance data, rig lost time performance data, health-safety-environment data, and local labor data. The KPI data is processed to determine at least one of a flat time score, a rig lost time score, a health-safety-environment score, and a local labor score as KPI scores. A rig efficiency index (REI) is determined from a weighting of the KPI scores. A control signal based on the REI is determined to control a first rig among the set of rigs.

Abstract: A method includes receiving, by a computer system, a video of a visible state of a component of a generator, the generator powering at least a portion of a rig equipment system at a wellsite. The computer system can determine an operational parameter based on the visible state of the component of the generator imaged in the video, and can transmit the operational parameter to an output device.

Abstract: A method of drilling includes obtaining historical data for historical wells in a field, determining a set of drilling parameters, and determining a set of hole section sizes defining a wellbore geometry. For each combination of a parameter in the set of parameters and a hole section size in the set of hole section sizes, historical wells having average values of the parameter exceeding a threshold for the hole section size from the historical surface drilling data are selected. An expected output for each of a model to be trained by each of the selected historical wells is derived based on a rate of penetration while drilling. A final model is trained with the selected historical wells and expected outputs. An operating envelope is determined for each of the parameters in the set of parameters from the trained model. The operating envelopes may be used to guide drilling of a well in the field.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for monitoring the flow of drilling mud from a wellbore by image processing. One method includes: capturing, using a digital imaging device, images of drilling mud at a surface of the wellbore as the drilling mud flows through one or more image capture zones of a circulation system that circulates drilling mud through the wellbore and a wellbore drilling assembly; receiving, by one or more processors of a computer system operatively coupled to the digital imaging device, the images captured by the digital imaging device; and processing, by the one or more processors, the images captured by the digital imaging device to determine a rate of flow of the drilling mud through the one or more image capture zones.

Abstract: A method for determining a slips status during a drilling operation of a subterranean formation. The method includes capturing, using one or multiple camera devices mounted on a drilling rig of the drilling operation, a plurality of images, each of the plurality of images comprising a portion that corresponds to a slips device of the drilling rig, generating, using a sensor device of the drilling rig, a plurality of parameters of the drilling rig, wherein the plurality of parameters are synchronized with the plurality of images, providing, by a computer processor, the plurality of parameters as input to a machine learning model of the drilling rig, and analyzing, by the computer processor and based on the machine learning model, the plurality of images to generate the slips status.

Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: capturing, using an image sensor directed at a drilling system, an image feed of movement of a component of the drilling system with respect to a vertical reference line; converting the image feed into a digital representation of the movement of the component, the digital representation including a number of offset pixels from the component to the vertical reference line in the image feed; converting the digital representation into a machine learning (ML), the ML representation including a plurality of vectors each including the number of offset pixels from the component to the vertical reference line at a respective time; training a ML model using the ML representation to characterize the movement of the component as normal or abnormal; and using the trained ML model to characterize the movement of the component in real-time as normal or abnormal.

Abstract: Analysis of drilling slurry solids by image processing includes receiving images of a solid slurry including a mixture of wellbore drilling mud and solid objects found in the wellbore while drilling the wellbore through a subterranean zone. The solid objects include drill cuttings and non-drilled solids. By implementing image processing techniques on the images, the solids in the solid slurry are classified a drill cutting or as a non-drilled solid.

Abstract: A method of drilling includes obtaining historical data for historical wells in a field, determining a set of drilling parameters, and determining a set of hole section sizes defining a wellbore geometry. For each combination of a parameter in the set of parameters and a hole section size in the set of hole section sizes, historical wells having average values of the parameter exceeding a threshold for the hole section size from the historical surface drilling data are selected. An expected output for each of a model to be trained by each of the selected historical wells is derived based on a rate of penetration while drilling. A final model is trained with the selected historical wells and expected outputs. An operating envelope is determined for each of the parameters in the set of parameters from the trained model. The operating envelopes may be used to guide drilling of a well in the field.

Abstract: Provided is a method of drilling a hydrocarbon well that includes conducting a drilling operation, collecting drilling data including characteristics of the drilling operation over a timespan, determining (based on the drilling data) drilling conditions for instants of time within the timespan, determining (based on application of the drilling conditions) preliminary classifications identifying a preliminary classification of the drilling operation for instants of time within the timespan, determining (based on the preliminary classifications) a series of classifications for the drilling operation that each indicate a determined classification for a respective instant of time, determining (based on the series of classifications) a change of classifications, conducting (in response to determining the change of classifications) a change point detection to identify a time of the change of classifications, generating drilling characteristic data indicating the time, and conducting the drilling operation in accord

Abstract: Exemplary embodiments of the present disclosure relate to systems and methods for structural health monitoring in which a sensor network includes motes distributed with respect to a structure. The sensor network can utilize dynamic pattern matching to monitor and localize damage in the structure without modeling or solving equations of the engineered structure, and without ascertaining or separately accounting for extraneous and often-difficult-to-recognize or evaluate factors, such as of the environmental and stimuli-related variability type.

Abstract: Exemplary embodiments of the present disclosure relate to systems and methods for structural health monitoring in which a sensor network includes motes distributed with respect to a structure. The sensor network can utilize dynamic pattern matching to monitor and localize damage in the structure without modeling or solving equations of the engineered structure, and without ascertaining or separately accounting for extraneous and often-difficult-to-recognize or evaluate factors, such as of the environmental and stimuli-related variability type.