This paper is aimed at providing an insight in some of the most up-to-date technologies for offshore pipeline inspection. The first part reviews the basics of the main underwater pipeline inspection technologies that are commonly used to detect signs of corrosion, fractures and other flaws. The second part surveys the existing robotic technologies for deep and shallow waters that are dedicated to underwater monitoring and inspection of pipeline integrity.
In this paper a new, semi-automated tank calibration method based on advanced robotics is presented that enables enhanced volumetric measurement and calibration of custody/royalty transfer tanks, with an emphasis on those used in the Oil and Gas Industry. This technology offers relevant facilities a rapid and straightforward solution that offers improved precision over traditionally used methods, thus minimizing revenue losses resulting from inaccurately calibrated tanks. The technology developed is a semi-autonomous robotic system capable of calibrating upright cylindrical tanks. To achieve this, a magnetic crawler was designed that incorporates a digital, laser-based ruler that collects data about the offset of the tank circumference along the tank height with reference to the tanks' base circumference. The technology has been developed in accordance with the requirements of API MPMS 2.2B "Manual of Petroleum Measurement Standards Chapter 2—Tank Calibration Section 2B—Calibration of Upright Cylindrical Tanks Using the Optical Reference Line Method" to ensure alignment with industry standards. The paper will present trials carried out in both the laboratory, during the technology calibration phase, as well as preliminary tests carried out at a tank farm in Jeddah, Saudi Arabia. The feasibility of using a magnetic crawler and the laser ruler configuration was first demonstrated in the lab environment where measurement uncertainty of less than 0.2 mm was documented, compared to the current technique, which yields measurement uncertainties of at least 1mm. As such, due to increased measurement accuracy, the technology enables consequential calibration of tanks every 2 years as opposed to every 5 years using existing solutions. Apart from the measurement accuracy improvement, this method significantly reduced the total time required to carry out the calibration measurements, increasing the efficiency of the process and reducing the down-time of the asset. Other advantages are based on the digital nature of the technology that simplifies data storage, thus allowing significantly more data to be captured. Finally, remote control of the robotic solution enhances operator safety as it eliminates the need for operators to perform calibration activities at elevated heights such as the roof of the tank. The work performed and presented in this paper demonstrates a novel application of robotics in the Oil and Gas Industry that directly impacts operational efficiency while enhancing safety and minimizing revenue losses. Globally, the current lack of accurate and efficient calibration methods impacts the efficiency of the crude supply chain. The work presented here provides a suitable and cost-effective solution to enhance value realization from the sale of liquid petroleum products.
Assets in the energy industry require periodic inspections to monitor their health and integrity. These inspection activities are typically performed manually by operators. However, a significant portion of the assets are also elevated and are hard to reach without the use of scaffolding. The use of scaffolding involves a complex process that includes manufacturing, transportation, and other associated logistics. These activities contribute to the carbon footprint related to inspection activities leading to increased emissions. Recently, the use of robotic and automated inspection solutions is being increasingly adopted by energy companies and the overall market as a whole. These advanced solutions could potentially play a valuable role in the reductions in carbon emissions by minimizing both the manufacturing and transporting of scaffolding for inspection activities. In this study, we aim to quantify the environmental benefit of such solutions within the energy industry. In order to quantify the decrease in carbon emissions, we examine the carbon emissions of extracting raw materials, manufacturing, and transportation of scaffolding. We compare this to the carbon cost of manufacturing batteries and inspection technologies along with their respective power consumption. Our initial results show that switching from traditional scaffolding techniques to drone-based solutions resulted in approximately a 30-fold decrease in emissions from 4.54kg CO2e per inspection job, if scaffolding is used, to 156 g CO2e using drone-based solutions. This was achieved by minimizing scaffolding manufacturing and transportation activities in comparison to drone manufacturing and power consumption. Robotic and drone inspection is a recently emerging field that has captured the attention of large energy institutions around the globe. Our study aligns with the ambitious initiatives in reducing carbon emissions and driving a successful transition to sustainable energy. The novelty of this paper is that it analytically investigates the driving force behind the innovative inspection technologies that are recently emerging in the market. Additionally, it quantifies the positive impact of these technologies from the perspective of reducing carbon emissions, leading to sustainable energy.
This paper showcases an innovative mobile application powered by IR4.0 technologies including augmented reality (AR) and artificial intelligence (AI). The purpose of this application is to enable digital transformation of analog gauges, digitize their measurements, automate historical data storage, visualize trends, and provide useful information about the gauge to the operator. Utilizing this application will replace the current practice of manual recording of readings in order to reduce human errors as well as promote operational efficiency. With this application, the operator simply points the mobile device's camera towards the gauge and the image is converted to a digital measurement using computer vision algorithms. The digitized readings are sent to a remote database for recordkeeping and data analytics. In order to identify which gauge is being scanned, which is necessary for proper recordkeeping, the application detects a unique QR-code tag attached to the gauge. Additionally, the application utilizes AR technology to overlay gauge specific information (such as gauge type, safe operating range, fluid type, etc.) along with the digitized reading. Visualization of historical readings is another feature in the application that assists the operator in trend monitoring and decision making. Preliminary tests for the prototype application were carried out in a laboratory environment to demonstrate the working principle of this application. Although the technology is in its early stages of development, it shows promising results in terms of accuracy and speed of the computer vision algorithms to detect and digitize the analog gauges. The historical data recorded by the application can also be accessed via the control room using a web interface, where information from various gauges can be retrieved and visualized for analysis and monitoring. Overall, the presented application integrates computer vision and augmented reality to provide an effective solution for digitizing analog gauges while promoting digital transformation efforts within the industry.
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