Emissions reduction is a focal point of the energy transition, compelling oil and gas and chemical processing facility operators to view Leak Detection and Repair (LDAR) and Leak Detection and Quantification (LDAQ) through a different lens. Rather than being driven by safety compliance, leak detection is now increasingly acknowledged as instrumental in meeting climate change and net zero goals.
LDAR and LDAQ programs are now fixtures in most emission reduction strategies. However, market analysts predict that advanced LDAR program adoption will continue to increase, aided in part by technological advancements and fast-approaching net zero deadlines. Solutions such as artificial intelligence (AI) and machine learning (ML) will also contribute, enabling the complex analysis required for effective leak prevention. Incorporating these latest technologies into traditional LDAR and LDAQ programs could shape — and enhance — the future of emissions reduction.
A Brief History of LDAR Innovation
The origins of LDAR can be traced back to the early 1970s, when LDAR programs were predominantly safety-driven and designed to reduce workplace risks by identifying leaks to fix. Periodic onsite inspections used simple leak detection instruments; follow-ups required manual maintenance scheduling.
More recently, leak detection programs have shifted from merely detecting to quantifying the amount of gas leaked — hence the rise of the acronym LDAQ. The standard method of quantifying continues to be laboratory-based, analyzing individually captured gas samples. Modern cameras with optical gas imaging (OGI) capabilities are replacing the standard methods more frequently. However, they have not yet been adopted at scale.
The energy transition and net zero goals have shifted LDAR and LDAQ programs from safety-driven initiatives to key drivers for GHG emissions reduction — specifically, methane mitigation. The IEA identifies reducing methane emissions as critical to limiting global warming. In 2023, IEA reporting attributed nearly 120 Mt of methane emissions to fossil fuels alone.
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LDAR and LDAQ in the New Energy Era
Industrial sectors must reduce emissions to meet Net Zero by 2050 goals. Alternative fuels like hydrogen are one pathway to achieving this objective. However, LDAR and LDAQ programs are also crucial in lowering the fugitive GHG emissions that contribute to climate change, thus protecting health and safety as we transition to environmental sustainability.
Increased Emphasis on Emissions Reduction
The IEA identifies methane (CH4) as a “powerful climate pollutant, responsible for around 30% of the rise in global temperatures since the industrial revolution.” A typical LDAR program detects fugitive methane leaks, nitrous oxides and sulfur oxides (NOx and SOx), among other gasses.
The drive to reduce emissions is significant. According to the NOAA, although methane (CH4) is more efficient at trapping heat, carbon dioxide (CO2) is the most abundant human-emitted GHG in the atmosphere. The emphasis on reducing both methane and CO2 emissions highlights the importance of LDAR/LDAQ as a critical element of reaching net zero emissions goals.
Many organizations are already managing leak detection programs that incorporate quantification. However, the next step is analyzing the root cause of leaks to enable an approach that involves prevention.
Heightened Focus on Process Engineering
An understanding of inefficiencies can help operations become proactive in the urgent need to curb emissions. Given time and resources, process engineers can uncover valuable insights into a leak’s root cause, identify strategies for preventing future leaks and pinpoint opportunities to optimize existing processes. Simply put, process engineering expertise is critical to LDAR and LDAQ effectiveness — and reducing an organization’s carbon footprint.
For instance, addressing a localized leak in a single piece of equipment can improve overall system efficiency and reduce fuel consumption. Well-maintained equipment tends to operate more efficiently over time, resulting in ongoing energy savings and an expanded lifecycle. It can also drive cost containment due to fewer repairs and replacements.
A holistic, process-minded approach is needed to address emissions throughout an organization.
Key Shortcomings of Today’s LDAR Programs
LDAR programs are important to meeting net zero emissions goals. However, current programs may have obstacles to overcome.
Siloed Knowledge
Many operators struggle with a disconnect across groups — for example, between process engineering and those involved in the execution of LDAR and LDAQ programs. Analyzing leak detection data in isolation from other groups can lead to incomplete solutions, which can obscure insights and keep an organization from improving. Common issues include:
- Lack of data sharing between the groups that understand leak detection technology and those that understand process operations
- Maintenance programs developed without adequate consideration for leak repair tasks
- No clear process informing various groups about how to handle leaks from detection to quantification to repair
- Obscured process-related insights due to restricted access to leak-related data generated by LDAR programs
- Siloed data sources that hinder an organization’s ability to analyze patterns or trends
Close collaboration and data sharing among process engineers and maintenance personnel directly involved in the workflow can enhance an LDAR program’s effectiveness and, ultimately, organizational sustainability.
Lack of Data Reconciliation
Discrepancies between overall site leak measurements and component-level measurements are common. However, these discrepancies are often undiscovered and undiagnosed by an operator.
Source-level data comes from leaking components — valves, connectors, pumps, sampling connections, compressors and pressure-relief devices — that are found and quantified by an LDAQ program.
Site-level data combines the data from LDAQ programs that involve an entire site, such as an oil refinery, typically for environmental reporting purposes.
In theory, all source-level measurements should add up to the site-level leak measurement. However, that is not necessarily the case. Without investigating the differences between the two data sets, an operator will not be able to gain a comprehensive understanding of their emissions profile. Determining the emissions “truth” enables companies to advance their program from a “find and fix” to a more effective “find and prevent” approach.
As the energy transition accelerates and scales, the shortcomings of current LDAR and LDAQ programs become a more critical roadblock to progress. Innovative technologies have the potential to address these challenges and unlock new levels of effectiveness in emissions reduction.
The Latest Innovations in LDAR and LDAQ
Investments in LDAR and LDAQ advancements have accelerated, fueled by the urgency of climate change, fast-approaching net zero deadlines and a tightening regulatory landscape. Colorado State University and Stanford University have both carried out studies that compare — and thus validate — the effectiveness of these various leak detection technologies.
Methods that make detecting gas leaks faster and safer, can help organizations take a preventative approach to reducing emissions. If adopted at scale, the following technologies can impact the effectiveness of an LDAR or LDAQ program.
AI Algorithms that Identify Leaks
One of the most common methods for determining a leak’s location is to send a technician with a handheld gas detection device. By using AI algorithms to analyze leak data gathered from in-place technology such as an OGI camera, leak detection could become exponentially faster and more accurate. Of all the technologies shaping LDAR and LDAQ, AI algorithms for identifying leaks have garnered the highest level of investment.
Visual Recognition with Automated Alerts
As OGI camera technology becomes more advanced, some models are coming equipped with visual recognition software. This software triggers an alert when gas is detected among components such as tanks or valves. By allowing operators to remotely and continuously monitor areas known to be vulnerable to leaks, this technology could facilitate a faster response. However, the cost of cameras is currently prohibitive — preventing widespread adoption of this technology.
Pattern Recognition and Machine Learning Models
Combining all available data sets, including emissions data, can provide key insights into operational trends. We have already seen early use cases in which machine learning models stitch together emissions and operational data and then perform pattern recognition. Although still in its early development stages, the use of these customized language learning models (LLMs) can provide a holistic understanding of where and why leaks happen — and address the knowledge gaps in current LDAR and LDAQ programs.
Emissions Prevention Hardware
As leak detection becomes more sophisticated, it reveals the need for smarter sealing solutions — including for assets that have historically been overlooked. For example, a component such as a tank hatch could be identified as the source of a methane leak, making it a candidate for a more effective sealing method.
Implementing a seal gas recovery system to prevent process-related emissions allows an operator to repurpose valuable process gas, which would have previously been flared or vented into the atmosphere. In doing so, these recovery systems can help support a process change that aligns leak detection, operations and GHG emissions reduction goals.
Integrated Continuous Monitoring
In today’s LDAR landscape, some continuous monitoring solutions are good at site-level measurements; others are stronger at the source level. Using two disparate systems and technologies makes it difficult to properly manage and analyze data. Luckily, the industry is working toward a system that can continually monitor for leaks and integrate multiple data sets. We are beginning to see integrated continuous monitoring systems that combine source-level and site-level data into one integrated system. Within the next few years, these solutions will likely solve many of the reconciliation challenges facing operators.
As OGI technology advances, these cameras will likely become more financially accessible. By combining this technology with the computational power of AI and machine learning, operators can gain a comprehensive, real-time view of GHG leaks.
The Future of LDAR and LDAQ Technology
The energy transition and net zero goals have changed the conversation around LDAR and LDAQ. While some manual processes may remain, advanced tools are becoming more effective at detecting and quantifying leaks — to the benefit of both safety and sustainability.
Despite significant investments in AI for LDAR and LDAQ, more innovation is needed before these programs truly empower operators to prevent leaks. John Crane is committed to investing in the sealing solutions of the future, including innovations to help operators detect and prevent leaks. We are entering the next phase of our 100-year legacy of pioneering solutions with reliability and sustainability at the core.