Saudi Arabian Oil Company

Avitas Systems, Inc.

King Abdullah University of Science and Technology

Ayman Amer

Fadl Abdellatif

Sahejad Patel

Ali Outa

Brian Parrott

Vincent Cunningham

Ali Shehri

Hassane Trigui

Ali Al Shehri

Ameen Obedan

Pablo Carrasco Zanini

Abdullah Arab

Ahmad Aldabbagh

Ser Nam Lim

Abdulwahab A Halawani

Mustafa Uzunbas

Muhammad Ababtain

Abderrazak Traidia

Thibault Villette

Maha Nour

Ali AlShehri

Hamad Al-Saiari

Hamad Alsaiari

Ali N Meshaikhis

Mohammed Sarraj

Wei Xu

Khaled Nabil Salama

Ihsan Al-Taie

Faisal M Melibari

Fadi Abdellatif

Hassane Trigul

Sumana Bhattacharjee

Abdullah Hassan Bukhamsin

Esraa Fakeih

John Boot

Godine Kok Yan Chan

Muhammad Sarraj

Ihsan A-Taie

Hussein Ali Outa

Ameen Al Obedan

Ali Hussein Outa

Adhesion devices for transport assemblies are configured to engage non-metallic surfaces. The transport assemblies can be wheels, such as omni-directional wheels, or can be a tractor tread assembly. The transport assembly is configured to move adjacent to a non-metallic surface of a structure. The transport assembly comprises an outer surface disposed adjacent to the non-metallic surface as the transport assembly moves, and a plurality of adhesion devices are each mounted to the outer surface and extend at least in a normal direction away from the outer surface. Each adhesion device is responsive to proximity to the non-metallic surface to adhere to the non-metallic surface. The adhesion devices in one or more embodiments include a Van der Waals-type adhesion device, a micro spine-type adhesion device, a suction cup-type adhesion device, or a combination of such devices.

Adhesion devices for transport assemblies to engage non-metallic surfaces

Systems and methods include a computer-implemented method for determining a concentration of hydrocarbon fuel in a fluid sample. A thin Cyclic Olefin Copolymer (COC) film of a COC layer having a honeycomb mesh-pattern forming a conductive metal trace is sputtered. The thin COC film is configured to dissolve upon contact with alkyl aromatic compounds present in hydrocarbon fuels. A fluid sample of a hydrocarbon fuel is positioned in an open area on the thin COC film. A resistivity of the conductive metal trace is monitored. A determination is made in response to the monitoring that a change in resistivity of the conductive metal trace has occurred resulting from dissolution of the thin COC film. A time duration is measured from the positioning of the fluid sample to the resistivity change. A concentration of the hydrocarbon fuel in the fluid sample is determined based on the time duration.

Changes in resistivity of mesh-patterned polymer thin films coated with conductive traces used to detect hydrocarbon fuels hidden in paraffin-based lubrication oils

Systems and methods include a computer-implemented method for inspection. Sensors are installed on locations susceptible to high temperature hydrogen attack (HTHA) on equipment used in hydrocarbon facilities. Attachment uses adaptable hybrid pads. Surface data captured using permanent sensors includes surface temperatures, per hydrogen (pH) measurements, and methane measurements for methane escaping from external surfaces of the equipment. Stress cracks in the equipment base metal are detected. An HTHA susceptibility analysis of the equipment is performed based on the presence of the cracks and by analyzing surface data and mapping potential HTHA cracks and locations of early signs of methane formation, including monitoring heat-affected zones (HAZ) and fusion lines of welds. Locations of the detected stress cracks caused by HTHA in metals are determined. An inspection result and assessment are generated, including a final analysis of assessment reports indicating a likelihood of cracks propagation and fissuring inside the equipment base metal.

Online inspection for early HTHA detection using a hybrid sensory system

Systems and methods include a computer-implemented method for inspection. Removable sensors are installed on equipment used in hydrocarbon facilities at locations susceptible to high temperature hydrogen attack (HTHA). Attachment uses adaptable hybrid pads. Surface data captured using the sensors includes surface temperatures, per hydrogen (Ph) measurements, and methane measurements for methane escaping from external surfaces of the equipment. A presence of cracks in the equipment base metal is determined. An HTHA susceptibility analysis of the equipment is performed based on the presence of the cracks and by analyzing the surface data and mapping potential HTHA cracks and early signs of methane formation. The steps are repeated on other locations. An inspection result and assessment, generated using the captured surface data, includes a final analysis of assessment reports indicating a likelihood of cracks propagation and a presence of fissuring inside the equipment base metal.

Flexible and adaptable hybrid sensor for HTHA detection

Techniques for determining a thermal condition of a pipeline include identifying a pipeline that carries a first fluid at a first temperature that includes a tubular conduit that includes a bore that carries the first fluid, and a layer of insulation installed over the tubular conduit; circulating a second fluid at a second temperature from a bypass conduit that is fluidly coupled to the tubular conduit through the layer of insulation into the bore; based on circulating the second fluid into the bore, detecting a thermal gradient between the first fluid carried in the bore and the tubular conduit or the layer of insulation at a particular location of the pipeline; and based on the detected thermal gradient, determining a presence of at least one of water or water vapor between the tubular conduit and the layer of insulation at the particular location of the pipeline.

Determining thermal conditions in a pipeline

This application discloses magnetically coupled integrated probes and probe systems, attachable to the robotic arms of a remotely operated vehicle to perform both cathodic protection (CP) voltage measurements and ultrasonic testing (UT) thickness measurements at an underwater surface. The integrated probe system can include a spring for coupling to an ROV end effector. An ultrasonic probe is disposed within and extends from the sleeve housing. A magnetic carrier, flux concentrator, and gimbal surround a portion of the ultrasonic probe, and one or more electrically conductive legs extend from the front surface of the gimbal to function as a CP probe. The legs are arranged about the ultrasonic probe, which has a flexible membrane exposed at the front surface of the gimbal, such that during inspection, at least one leg contacts the surface and the ultrasonic probe is sufficiently proximate to provide substantially simultaneous CP and UT measurements.

Magnetically coupled integrated ultrasonic testing and cathodic protection measurement probe

Techniques for determining a thermal condition of a pipeline include identifying a pipeline that carries a fluid at a steady-state temperature, where the pipeline includes a tubular conduit that includes a bore that carries the fluid, and a layer of insulation installed over an exterior surface of the tubular conduit; changing the steady-state temperature of the fluid by applying a thermal contrast to the pipeline; based on changing the steady-state temperature, detecting a thermal gradient between the fluid carried in the bore and at least one of the tubular conduit or the layer of insulation at a particular location of the pipeline; and based on the detected thermal gradient, determining a presence of at least one of water or water vapor between the exterior surface of the tubular conduit and the layer of insulation at the particular location of the pipeline.

A system and method for inspecting a surface of a structure for defects includes an inspection apparatus having a heating device for heating a section of the surface of the structure, an infrared camera for receiving infrared radiation from the surface in response to heating, a controller configured to generate thermographs from the received infrared radiation, and a communication device. A training system includes an expert system module configured to determine correlations between a set of thermographs generated by a thermal simulation of modeled structural elements with defects, and parameters of the modeled structural elements. A computer system communicatively coupled to the training system and the inspection apparatus, is adapted to receive thermographs received from the inspection apparatus and to detect quantitative parameters of defects in the structure using the correlations obtained from the training system.

Method for inspecting composite structures using quantitative infra-red thermography

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 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 cleaning device that passively self-adjusts to improve biofoul removal across curved, non-uniform, or irregular underwater surfaces. The cleaning device includes a motor, one or more shafts coupled to the motor and coupled to one another via at least one universal joint, and a cleaning mechanism for removing biofoul from the target surface. The cleaning device includes an alignment mechanism that restricts the cleaning mechanism's movement to improve biofoul removal. The alignment mechanism can include bearings, spring components, dampening material, adhesion components, floatation objects, or a combination thereof.

Underwater marine growth brushing mechanism with passive self-adjust for curved surfaces

Integrated probes and probe systems suitable for attachment to a robotic arm of a remotely operated vehicle are disclosed. The probes and probe systems serve to perform cleaning operations and both cathodic protection (CP) voltage measurements and ultrasonic testing (UT) thickness measurements at an underwater surface. The cathodic protection measurement system includes one or more electrically conductive legs that extend outwardly from the probe. These legs are arranged about a cleaning tool and an ultrasonic sensor. When the integrated probe contacts the underwater surface, at least one leg contacts the surface, thereby providing a desired distance between the probe and the underwater surface for efficient cleaning and UT inspection. The underwater surface can be cleaned and CP and UT measurements can all be performed using a single, integrated probed during a single operation, without having to reposition the probe.

Underwater vehicles with integrated surface cleaning and inspection

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

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Thuwal, Saudi Arabia

Ayman Amer