Saudi Arabian Oil Company

Avitas Systems, Inc.

Vincent Cunningham

Ezzat Hegazi

Ayman Amer

Ahmad Aldabbagh

Ali Al Shehri

Christoph Stamm

Christof Brunner

Ser Nam Lim

Ali Shehri

Maha Nour

Mustafa Uzunbas

Muhammad Ababtain

Ali Outa

Wei Xu

Sahejad Patel

Ihsan Al-Taie

Iqbal Hussain

Faisal M Melibari

Turki A Alomairi

John Boot

Godine Kok Yan Chan

Ihsan A-Taie

Maha Ali N Alsayegh

Wesam Hussain

A single-wavelength light source is configured to generate an excitation light source. A sample holder that defines an inner cavity is capable of holding a sample and includes a surface transparent to the excitation light source. One or more mounts are attached to at least one of the light source or the sample holder. The mounts are configured to change an incident angle of the excitation light source on the surface. One or more optical components are positioned in a path of a fluorescence emission emitted from the surface and guide the fluorescence emission to a detector. A detector detects an intensity of the fluorescence emission.

Determining composition of a sample

Systems and methods include a fluorescence measurement apparatus. A single-wavelength light source generates an excitation light source. A sample holder holds a sample and includes a surface transparent to the excitation light source. Mounts attached to the single-wavelength light source(s) or the sample holder change an incident angle of the excitation light source on the surface. Optical components positioned in a path of a fluorescence emission emitted from the surface guide the fluorescence emission to a detector that obtains spectra from at least first and second angles-of-incidence. A device records spectra obtained by the detector from the first and second angles-of-incidence, normalizes and analyzes intensities of the spectra, subtracts a first spectrum corresponding to the first angle-of-incidence from a second spectrum corresponding to the second angle-of-incidence to obtain a difference, identifying a sample type of the sample based on an API gravity mapped to the difference.

Method for measuring API gravity of petroleum crude oils using angle-resolved fluorescence spectra

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

Disclosed is a methodology for determination and prediction of heat exchanger fouling, such as polymer fouling in the circulation loop that forms part of the heat exchanger system. The buildup of a polymer or other undesired material deposit in the heat exchanger provides a distinctive temperature signature (thermal gradient) on the surface of the heat exchanger asset, which is visualized using a thermographic camera. Coupling images (thermograms) from the camera with a machine learning algorithm identifies fouling and, with knowledge of the historical data of the asset and operating and ambient conditions, enables prediction of future fouling. The thermal images provide several types, or orders, of temperature information that are indicative of locations vulnerable to fouling. In one case, the method uses machine learning applied to time-based temperature change/gradient information to detect hidden polymer fouling in areas that form part of the heat exchanger asset.

Heat exchanger fouling determination using thermography combined with machine learning methods

A subsurface impact protection system for protecting an underground structure is provided. The protection system includes a subsurface polymer layer above the underground structure which is configured to absorb above-ground impact force from reaching the underground structure. A sensor network is embedded in the polymer layer. The sensor network includes a plurality of sensors configured to monitor the polymer layer by generating a corresponding plurality of sensor signals of conditions near the polymer layer and transmitting the generated sensor signals to one or more external receivers. In some embodiments, the polymer layer includes a protective mesh made up of a plurality of high density polyethylene (HDPE) strands in a woven pattern. In one such embodiment, the sensors occupy gaps in the protective mesh. A method of protecting an underground structure is also provided.

Sensor network for subsurface impact protection system

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

An opto-mechanical apparatus including a hollow housing member having a first end and a second end, the housing member having a longitudinal axis, a parabolic mirror positioned on a side of to the first end of the housing member, and a mirror adjustment mechanism attached to the second end of the housing member, the mirror adjustment mechanism connected to the parabolic mirror through the housing member, the mirror adjustment mechanism configured to adjust an axial position of the parabolic mirror along the longitudinal axis and to adjust a radial position of the parabolic mirror about the longitudinal axis.

Optomechanical part for parabolic mirror fine rotation and on-axis linear positioning

An optical signal source is positioned at a first side of the flow cell or the flow-line bypass. The optical signal source is capable of emitting an optical signal through the first side of the flow cell or the flow-line bypass. A first optical detector is positioned at a second side of the flow cell. The second side is opposite the first side. The first optical detector is capable of detecting a transmitted-light intensity of the optical signal transmitted through the second side of the flow cell or the flow-line bypass. A second optical detector is positioned at a third side of the flow cell. The third side is different than the first side and the second side. The second optical detector is capable of detecting a scattered-light intensity of a scattered optical signal transmitted through the third side of the flow cell or the flow-line bypass.

Detecting black powder levels in flow-lines

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

A method for identifying metal wall loss in an insulated metal structure is provided. The method includes receiving thermograms of the outer surface of the structure using an infrared camera, applying filters to the thermograms using a first machine learning (ML) system, determining wall loss classifications based on outputs from the filters, validating the wall loss classifications by inspecting the structure, and training the first ML system using the validation results. Outputs of the first ML system and additional structural and environmental data are input to a second ML system that incorporates information from earlier states into current states. The second ML system is trained to estimate wall loss according to changes in the outputs of the first ML system and the additional data over time until a wall loss classification accuracy is reached. The metal wall loss is thereafter estimated using the first and second ML systems in coordination.

Metal loss determinations based on thermography machine learning approach for insulated structures

A system and method is disclosed for calibrating the volume of storage containers using ultrasonic inspection techniques. The exemplary ultrasonic calibration system comprises a plurality of acoustic devices controllably deployed in respective positions on the outside surface of the container. The acoustic devices include a transducer for sending acoustic signals across the internal volume of the container and sensors configured to detect the acoustic signals. The acoustic devices are in communication with a diagnostic computing device that controls the positioning and the operation of the acoustic devices and is further configured to determine the time time-of-flight of acoustic signals that travel between the various acoustic devices. Moreover, according to the specific arrangement of acoustic devices and the measured acoustic signal information, the control computer is configured to calculate the dimensions of the container and its internal volume.

System and method for acoustic container volume calibration

A system for predicting and detecting of corrosion under fireproof insulation (CUF) in an infrastructure asset comprises a first detection apparatus adapted to capture thermal images of the asset, a second detection apparatus adapted to receive THz radiation from the asset, and at least one ambient condition sensor for detecting environmental conditions at a location of the asset, a communication device coupled to the first and second detection apparatus and at least one ambient condition sensor to receive data therefrom and adapted to communicate the received data, and a computing device communicatively coupled to the communication device and configured with instructions for executing a machine learning algorithm taking as inputs the thermal image, THz and ambient condition data received from the communication device, and to output a CUF prediction regarding the asset.

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Ferbane, Ireland

Vincent Cunningham