Saudi Arabian Oil Company

Mustafa A Al Ibrahim

Mokhles Mustapha Mezghani

Abdul Hafiz O Masri

Sergey Safonov

Johannes Jacobus Buiting

Leyla Ismailova

Assad Hadi Ghazwani

Egor Tirikov

Johannes Jacobus Maria Buiting

A modular geologic core examination table is claimed. The modular geologic core examination table includes at least two legs, each leg comprising an extending portion for increasing the effective length of each leg, the extending portion at least partially concentrically disposed within or around the leg from which it extends, and wheels affixed to each of the at least two legs, wherein the wheels comprise braking functionality. The modular geologic core examination table also includes a tabletop coupled to the at least two legs, the tabletop including a top surface for receiving a core sample tray configured to hold the geologic core samples, wherein the tabletop is disposed above an automated guided vehicle (AGV) configured to navigate the modular core examination table while carrying geologic core samples from one geologic processing station to another without collisions.

Robotic modular geologic core preparation and examination table

A method for generating a core description is disclosed. The method includes coring and collecting rock cores from geographical locations in the subterranean formation, detecting, using an augmented reality (AR) device worn by a user, content of an identifying tag of a rock core within a device view of the AR device to identify a well where the rock core is obtained, retrieving, by the AR device from a data repository, historical data of the well, activating, by the AR device, a sensor to acquire additional data from the rock core to supplement the historical data, and presenting, by the AR device, an AR image including a first image of the historical data and the additional data superimposed over a second image of the rock core, where the user generates the core description based on viewing the AR image.

Interactive core description assistant using virtual reality

A method for formation properties prediction in near-real time may include obtaining lab measurements of existing drill cuttings at a plurality of depths of a first well. The method may include obtaining historical drilling surface data at the plurality of depths from a plurality of wells. The method may include obtaining real-time digital photos and real-time drilling surface data of new drill cuttings at a new depth of a new well. The method may include generating, using a prediction model, predicted formation properties of the new drill cuttings based on the real-time digital photos, the real-time drilling surface data, and the new depth. The method may include predicting, using a near-real-time model and the predicted formation properties, near-real-time formation properties in the new well, wherein the prediction model comprises a historical model that employs a machine-learning algorithm.

System and method for formation properties prediction in near-real time

An example system may be used to automate one or more processes relating to drill cutting handling, cleaning, and packing to produce consistently high-quality cleaned samples. In some implementations, an example system may employ cleaning processes including centrifugal and ultrasonic processes to clean a sample automatically without the aid of a human operator thereby increasing efficiency and quality of the produced samples for better subsequent sample analysis. A system may include an unloading module, a cleaning module, and packing module, all of which may be operated in combination with a robotic arm.

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Safwa, Saudi Arabia

Mustafa A Al Ibrahim