[Editor's Note: Bright Eyindah Odike is a member of the TWA Editorial Board and the author of previous TWA articles.]
Nima Daneshvarnejad, PhD, SPE, is a petroleum engineer and researcher at the University of Southern California (USC), specializing in methane-emission monitoring, artificial intelligence (AI), geospatial analysis, and legacy oil and gas assets. His doctoral research at USC integrated Internet of Things (IoT)-enabled sensor systems, COMSOL-based CFD modeling, field experiments, and machine learning to detect and predict methane leaks from abandoned wells.
He developed a low-cost static flux chamber, time-series forecasting models including liquid time-constant networks, and AI-assisted optimization tools for monitoring-system design. His broader work includes ArcGIS-based well mapping, satellite methane analysis, carbon-credit methodology, enhance oil recovery optimization, CO2 measurement, and air-quality monitoring.
Bright Eyindah Odike (BO): Nima, thank you for taking the time to speak with us. Your background is rooted in petroleum engineering, but your recent work brings together methane monitoring, well integrity, and data-driven modeling. What drew you to this intersection, and how has it shaped the way you see the role of petroleum engineers in the energy transition?
Nima Daneshvarnejad (ND): Hello Bright, thank you so much for interviewing me. I started my studies in petroleum engineering in Iran, where the focus was on reservoir modeling, drilling, and topics related to increasing production. After moving to the US in 2019, I soon realized how different petroleum engineering needs are in California compared with the Middle East or even other states in the US.
When I was a master’s student at USC, I joined a project in Kern County that asked me to test and verify service companies’ results from a steam cogeneration plant that provides steam for thermal EOR operations. The operating company believed it was being overcharged for its carbon footprint. The problem was in a completely new area for me and did not involve the typical production-related questions. Instead, it required more fluid mechanics, gas-behavior modeling, and extensive experimental work.
I had to quickly adapt and use my experimental and fluid mechanics skills to develop a testing procedure that was accurate and not biased by what the company initially believed was happening. I soon realized that the environmental side of oil and gas operations is scientifically very demanding and that fewer scientists are dedicating their time to it. I also realized how difficult emissions detection, monitoring, and measurement can be because of the highly variable behavior of gases.
The project was very successful and resulted in the development of a new process and probe-testing tool that is now being used in the field. More importantly, it showed both me and the operating company how bias can result from poor field testing and how much engineering knowledge is needed for this often-overlooked side of oil and gas operations. This experience motivated me to continue into my PhD, focusing on methane-emission monitoring from poorly abandoned, unplugged, and idle oil and gas wells.
It has also shaped how I see the role of petroleum engineers in the energy transition. Our understanding of wells, fluids, flow behavior, and field operations can be applied not only to production, but also to emissions reduction, well integrity, and improving the environmental performance of oil and gas operations.
BEO: What made methane emissions from abandoned wells stand out as a problem you wanted to solve?
ND: I live in Los Angeles, and my family lives here as well. If you take a quick look at the map of plugged and abandoned wells or idle wells, you will quickly understand how much of our city was built on what used to be oil and gas fields. Today, houses and critical infrastructure are located above many of these wells, and methane emissions from them can pose a real threat, not only from a global warming perspective but also as a safety risk to our communities.
The lack of a cost-effective, scalable, and accurate detection and monitoring system, both in the commercial market and in academic research, showed me how rewarding it could be to work on this issue, both as a scientist and as a member of the community.
BEO: Methane emissions from abandoned wells can be intermittent. What can continuous monitoring reveal about a well that periodic inspections may miss?
ND: Emission monitoring, especially for a lightweight gas like methane, needs to be considered in the right context. For example, we first need to ask why a particular well is being monitored. If a well is being monitored in the context of the voluntary carbon crediting market, intermittency can be a major challenge because it may prevent us from establishing an accurate baseline of the well’s emissions.
From a regulatory perspective, if a well is being ranked and scheduled for maintenance when resources and personnel are limited, intermittent emissions can cause the well to be poorly prioritized for maintenance and may even lead to a false negative classification.
This is why a long-term, cost-effective monitoring system with IoT capability is essential for addressing this problem.
BEO: Field conditions can introduce significant variability into methane measurements. How do you distinguish changes in a well’s emissions from changes in the surrounding environment, and what further validation is still needed?
ND: I have modeled the behavior of these leaks under different atmospheric conditions, including wind, pressure, temperature, and relative humidity, and wind stands out as the main challenge in detecting and monitoring these wells. Even small changes in wind can cause methane to dilute rapidly and become difficult to detect, while pressure drops in unplugged wells caused by wind can be strong enough to trigger a leak event.
Other field conditions, such as underground injection into the same formations or activities in the surrounding area, require more extensive studies at the field scale and remain important open questions.
BEO: Could continuous emissions data eventually provide an early indication that a well’s integrity is deteriorating, rather than simply detecting methane after a leak occurs?
ND: If a plugged and abandoned well shows any leakage, then its integrity is already compromised. However, the important question is how quickly the well’s integrity is deteriorating compared with other wells, so that resources can be allocated in a timely manner before a more serious incident occurs. This can be better understood through long-term monitoring using an IoT capable flux chamber or through repeated periodic visits to the same site.
BEO: Your work combines machine learning with physics-based modeling. Where do you see machine learning adding the most value in methane monitoring, and where should engineers remain cautious?
ND: I think machine learning and AI-based approaches can add the most value in methane monitoring by processing large volumes of continuous data, identifying emission patterns, and helping distinguish emission signals from environmental variability. These tools can make analysis faster and more efficient, but only when used where they are truly needed.
At the same time, engineers should remain cautious about relying on AI without the right technical knowledge, reliable data, physical understanding, and proper validation, especially as large language models and agentic AI systems become more common in research.
BEO: Abandoned wells are often prioritized using factors such as location and proximity to communities. How could continuous emissions data help move well prioritization toward a more dynamic, risk-based approach?
ND: The other important issue is that, while I agree that well location is important in ranking systems, establishing a strong testing method that minimizes sources of error and gives us a better understanding of how often leaks occur, how long they persist, and how their magnitude changes over time can significantly improve prioritization. Continuous emissions data could allow ranking systems to be updated as conditions change, making well prioritization more dynamic and risk based.
BEO: What do you see as the biggest technical challenge in taking this approach from individual field deployments to monitoring abandoned wells at much larger scale?
ND: The main challenge today is that some commercial monitoring tools are presented as broadly applicable even though they may not be well suited for a particular type of well or emission source. Another issue is that many of these systems are mechanically complex, which can make consistent and accurate field performance difficult to achieve.
Cost is also a major barrier. The financial burden associated with deploying and maintaining these tools can discourage operators and regulators from implementing monitoring programs across large numbers of abandoned wells.
BEO: Beyond reducing monitoring costs, how could continuous emissions data improve the economics of methane abatement by helping operators or regulators direct limited resources where they can have the greatest impact?
ND: The resources available to maintain these wells are very limited and are usually a direct cost to operators, except in the context of voluntary carbon markets and cap-and-trade programs. Having continuous, cost effective, and data-driven monitoring can help operators and regulators identify which wells require the most attention and direct limited resources toward the highest priority cases. It can also help operators plan for these costs more effectively and reduce unplanned expenditures in the future, which are a major concern across the industry.
BEO: Although your research focuses on abandoned wells, where else in oil and gas do you think the lessons from continuous, low-cost methane monitoring could have the greatest impact?
ND: I think the biggest impact would be in how we manage late life and legacy oil and gas assets. Plugging and abandoning an asset does not necessarily mark the end of its life cycle. Legacy wells that helped drive US economic growth over the past century will remain with us and may continue to require monitoring and care in the future, as is the case with any other energy infrastructure.
We should therefore no longer view plugging and abandonment as the final stage in the life of an oil and gas well, but as part of a longer-term asset management and environmental stewardship process.
BEO: Where do you see the next major advances in methane monitoring for abandoned wells, and what challenges still need to be solved?
ND: The main challenge will be developing edge devices that can reliably collect and transmit data from leaking wells to the operators who need to make decisions about how those assets should be managed.
I think the next major advancement will be designing plugging and abandonment strategies that allow wells to continue providing useful monitoring data through embedded or nearby edge devices. This could make continuous monitoring a more integrated part of long-term well management.
BEO: For students and young professionals in petroleum engineering, what skills or ways of thinking will be most important for contributing to methane management and the broader energy transition?
ND: I want to encourage students and young professionals to broaden their understanding of petroleum engineering and be willing to work on issues that have traditionally been overlooked in our industry. The challenges facing the industry continue to evolve, and new problems are emerging beyond the areas petroleum engineers have historically focused on. The number and nature of these challenges will continue to grow and shift, and as petroleum engineers, we must recognize that we are well equipped to address them.
