Machine Vision for Oil Pipeline Inspection

Pipelines are now regarded as the safest way of transporting and distributing oil, gases, and other fluids such as chemicals, especially hazardous combustible substances.

Oil leakage, on the other hand, is an unavoidable hazard in pipeline systems due to wear, corrosion, and damage to pipeline infrastructure, all of which cause gradual degradation and disintegration over time. Monitoring and detection of liquid leaks is a core requirement for many businesses and governments as part of their occupational health and safety programs. Fluid leakage not only generates enormous economic losses each year but also poses significant risks to safe and steady operations.

Pipeline leaks are caused primarily by the natural wear process, corrosion phenomena on both the inner and outer pipe surface, damages caused by severe mechanical loads, assembly flaws, thermal exhaustion, and material defects. The collapse of the pipes is usually ascribed to aging infrastructure and/or harsh weather conditions. This gradual degradation also raises the risk of failure. While most inspection approaches are sensor-based, time-consuming, and labor-intensive, robotic systems drastically reduce the amount of human effort necessary for the same degree of inspections.

How to solve oil pipeline problems by using machine vision?

Machine vision refers to all industrial and non-industrial applications in which a combination of hardware and software provides operational guidance to devices in the execution of their activities based on image acquisition and processing. Industrial vision system utilizes many of the same techniques and methodologies as academic/educational and governmental/military computer vision applications, however, there are some differences in limitations.

Machine vision systems collect images using digital sensors embedded in industrial cameras with specialized optics, which are then processed, analyzed, and evaluated by computer hardware and software. Machine vision inspection systems examine the visual appearance of the observed material. Through the statistical analysis, the system detects probable faults on the material's surface and organizes them into groups based on resemblance, contrast, texture, and/or geometry.

Adoption of Vision technology in Oil and Gas

Oil and gas companies can use SmartMore's digitalization technology to boost operational efficiency through industrial automation (Industry 4.0). This usually leads to faster processes and lower operational risks. Following are the most common application types:

Prediction of maintenance & operational life

Safety and compliance monitoring

Reliability, resulting in fewer business interruptions

Risk assessment and monitoring of structural health

Resource efficiency and sustainability

Inspection and non-destructive testing

Analyze fatigue and corrosion of systems

In the section below, we'll go through some of the most common machine vision applications in further depth.

Remote Oil and Gas Field Monitoring

Oil and gas field monitoring can be done in real-time by using the SmartMore machine vision system to automate and digitize oil development sites for offshore oil and gas fields and pipeline maintenance. These systems use optimization algorithm approaches to monitor and anticipate the condition of load pumps to boost oil and gas productivity. At the same time, it is equally effective in smooth operations of oil and gas pipelines by timely inspection for all defects.

The oil and gas industry's digital transformation can be done by Smartmore’s low-cost sensors and high-performance computation with distributed systems to extract high-value information from big data directly from the source of data. Smart Cameras' multidimensional value and comparably inexpensive cost enable large-scale video analysis without the requirement for physical sensors.

Corrosion Detection with Smartmore’s vision technology

Corrosion is a major defect in structural systems; it has a substantial economic impact and, if left unattended, can create safety risks. Sometimes in hazardous conditions Inspection tasks that must be performed periodically are often carried out manually. Furthermore, manual interpretation is often time-consuming, costly, and subjective. To automate inspection activities, our optimization algorithm evaluates video images captured by the SmartMore machine vision system.

The presence of corrosion is a crucial indicator during the inspection. So Smartmore’s machine vision technology can be successfully implemented for automatic rust detection. Hence our smart algorithm with standard machine vision technology can provide cost-saving, faster, better solutions to prevent and take corrective measures for corrosion, especially in oil pipelines.