Abstract: In some embodiments, a method for controlling a learning machine used in a drilling operation to drill a well into a subsurface formation includes receiving, via a video stream, an image of debris including cuttings from the drilling operation. The method may further include generating a first mask on the image to identify the cuttings in the debris, generating, via instance segmentation, a second mask for each of the identified cuttings, determining, based on the second masks, one or more properties of each of the cuttings, and associating each of the identified cuttings to a depth interval of the subsurface formation based, at least in part, on the properties of each of the identified cuttings and at least one property of the drilling operation.

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 present disclosure relates to a hydraulic valve that has a two-stage con-struction that provides for higher fluid flow and lower power consumption. The present disclosure also relates to a hydraulic valve that may be constructed to include a pilot-poppet, a pilot-poppet valve seat, and a main-poppet. The main-poppet may be mechanically isolated from the pilot-poppet by the pilot-poppet valve seat.

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: Introduced herein are techniques that can create descriptive taxonomies of expected data streams and identify and associate each incoming data stream with the taxonomies automatically. The introduced techniques can significantly reduce the human intervention and efforts and hence the number of human errors in identifying and mapping signals. The introduced techniques can also automatically store values of the data streams and their corresponding taxonomies as facts and dimensions in a business intelligence (BI) data repository/warehouse. Doing so, the techniques create a BI data repository with various sets of logically nested dimensions and facts that can be used to analyze performances of various assets in petroleum service environment. Leveraging their similar hierarchical structures and elements, the techniques can expand and combine taxonomies and allow an end user to, not only analyze performances of assets in a specific rig, but across multiple rigs and projects, which were not possible before.

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 present disclosure relates to a hydraulic valve that has a two-stage con-struction that provides for higher fluid flow and lower power consumption. The present disclosure also relates to a hydraulic valve that may be constructed to include a pilot-poppet, a pilot-poppet valve seat, and a main-poppet. The main-poppet may be mechanically isolated from the pilot-poppet by the pilot-poppet valve seat.