Saudi Arabian Oil Company

Avitas Systems, Inc.

Ahmad Aldabbagh

Vincent Cunningham

Ayman Amer

Ser Nam Lim

Ali Al Shehri

Mustafa Uzunbas

Muhammad Ababtain

Sahejad Patel

Hassane Trigui

Ali Shehri

Kaamil Ur Rahman Mohamed Shibly

John Boot

Godine Kok Yan Chan

Wesam Hussain

An automated method of pipeline sensor integration for product mapping of a pipeline network is provided. The method includes acquiring, by a plurality of sensors of the pipeline network, first sensor responses of a pipeline in the pipeline network when a first hydrocarbon product is flowing through the pipeline. The method further includes using a prediction circuit to receive the acquired first sensor responses, integrate the received first sensor responses into one or more integrated first sensor responses in order to improve accuracy of the received first sensor responses, and identify the first hydrocarbon product in the pipeline based on the integrated first sensor responses. The prediction circuit is built from training data using a machine learning process. The training data includes first training sensor responses of the pipeline by the plurality of sensors acquired at a previous time when the first hydrocarbon product was flowing through the pipeline.

Pipeline sensor integration for product mapping

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

Robot dispatch and remediation of localized metal loss following estimation across piping structures

A technological solution for analyzing a sequence of electromagnetic spectrum image frames of a nonmetallic asset and detecting or predicting an aberration in the asset, including a detected or predicted location of the aberration. The technological solution includes receiving the electromagnetic spectrum image frames by a pair of machine learning systems of different types, applying a machine learning algorithm to the electromagnetic spectrum image frames to stratify the electromagnetic spectrum images into abstraction levels according to an image topology and output first aberration determination information, applying a second machine learning algorithm to the electromagnetic spectrum image frames to detect patterns in electromagnetic spectrum images over time and output second aberration determination information, generating an aberration assessment based on the first and second aberration determination information, and transmitting the aberration assessment to a communicating device, including a prediction and location of an aberration in or on the nonmetallic asset.

System and method for cyber-physical inspection and monitoring of nonmetallic structures

A computer-based method and system for predicting the propagation of cracks along a pipe is provided, wherein successive time-indexed ultrasound images of a pipe surface are captured and digitized. A computer vision algorithm processes the images to identify defects in the pipe, including cracks. At least one blob detection module is used to identify groups of cracks on the pipe surface that have created detectable areas of stress concentration or a prescribed likelihood of crack coalescence or crack cross-influence. The center locations and radial extents of respective blobs are each parametrized as a function of time and pipe surface location by determining parity relationships between successive digital data sets from successive captured images. The determined parity relationships are then used as training data for a machine learning process to train a system implementing the method to predict the propagation of cracks along the pipe.

Method for automated crack detection and analysis using ultrasound images

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

Cloud-based machine learning system and data fusion for the prediction and detection of corrosion under insulation

Machine learning system and data fusion for optimization of deployment conditions for detection of corrosion under insulation

Growing community of inventors

Thuwal, Saudi Arabia

Ahmad Aldabbagh