Saudi Arabian Oil Company

William Contreras Otalvora

Chinthaka Pasan Gooneratne

Michael Anthony Affleck

Arturo Magana Mora

Timothy Eric Moellendick

Krzysztof Karol Machocki

Arturo Magana-Mora

Ammar H Alali

Mahmoud Abughaban

Pratyush Singh

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.

System and methods for the measurement of drilling mud flow in real-time

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.

Rig power system efficiency optimization through image processing

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.

Analysis of drilling slurry solids by image processing

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.

Automatic slips detection system for the optimization of real-time drilling operations

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.

Automatic analysis of drill string dynamics

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.

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Dhahran, Saudi Arabia

William Contreras Otalvora