Halliburton Energy Services, Inc.

Fahad Ahmad

Varun Tyagi

Daili Zhang

Olivier Germain

Julien Marck

Christopher Michael McMillon

Shaun Patrick Lawrence

Meng Zhang

Daniel Antonio

Chao Yang

Howard James Neil Black

Yanyan Wu

Anita Kirsten Torr

Bo Liang

Sakshi Agarwal

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.

Real-time formations cuttings analysis system using computer vision and machine learning approach during a drilling operation

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.

Systems and methods to determine an activity associated with an object of interest

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.

Managing machine-learning models via non-fungible tokens on a digital ledger

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.

Extracting video clips from videos for use in training a machine-learning model

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.

Value balancing for oil or gas drilling and recovery equipment using machine learning models

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Houston, TX, United States of America

Fahad Ahmad