AIQ’s ENERGYai Drives Operational Excellence with Data Analytics in the Upstream Sector – Roshchin
In this interview, Ndubuisi Micheal Obineme, Managing Editor of The Energy Republic, talks to Dr. Mike Roshchin, Head of AI at AIQ, about the strategic role the company’s ENERGYai agentic AI framework is playing in driving efficiency and operational excellence in the upstream sector.
ENERGYai is a pioneering solution, transforming the energy sector and helping deliver maximum energy with minimum emissions.
AIQ, in a strategic partnership with ADNOC, introduced ENERGYai to enhance decision-making, sustainability, and operational efficiency.
At ADIPEC 2025, AIQ was the Co-Host of the AI Zone, leading the dialogue on how artificial intelligence is reshaping industrial performance and accelerating the energy transition. Excerpts:
TER: Could you provide an overview of AIQ’s ENERGYai features, capabilities, performance metrics, and value propositions for the energy sector?
Roshchin: Before we jump into your questions, I would like to share a couple of words in terms of the objective of ENERGYai.
ENERGYai is driving a transformational change in the energy sector.
What that means is that in the next five years, ENERGYai will be much more automated and autonomous. It will be much faster for accurate decision-making in the energy industry.
As part of ENERGYai, we have developed the foundation that allows us to equip our operations with AI agents.
What is an AI agent? It is a high-level AI expert that is available 24/7 to do the jobs that are completely manual as of today. These kinds of jobs could be routine jobs, including helping to make accurate decisions with data. So that’s exactly the objective.
As of today, ENERGYai covers almost 75% of all use cases related to subsurface and upstream operations.
ENERGYai provides ADNOC access to millions of documents with analysis within minutes instead of months. That’s one of the most noteworthy features we have as part of this exercise.
The next deliverable of ENERGYai relates to seismic interpretation. It’s a complex task, due to the volume of data. Just imagine that one seismic cube, on average, will be about 20,000 cubic kilometres. This is a 3D image. It is extremely complex and requires interpretation.
As of today, the manual interpretation for seismic takes several months to conduct with a team of between 6 and 20 people. Ideally, it is necessary to rerun the interpretations regularly, which is extremely challenging due to the lack of human capacity and the time it would consume.
However, ENERGYai can conduct seismic analysis within 40 minutes for a 20,000-kilometre seismic cube. This is a dramatic difference, and the accuracy is up to 90%.
ENERGYai also helps identify false horizons within the seismic cube and other characteristics as well.
The second and third use cases are about Geomodelling. Geomodelling isn’t only about math; it includes the scale and how to work with data. There are a lot of different datasets that need to be considered as part of this exercise.
As of today, it’s a completely manual process, and each operating company is doing its own modelling, without a standard process that can be applied.
So we take our large language models and machine learning tools, and combine them into one module to generate the final data within a couple of minutes.
A hybrid approach to geomodelling involves data-driven physics and simulations, which are required to forecast production levels in the upstream sector.
As we all know, there has been a question about accuracy regarding simulations or physics-based modelling. There have also been questions about finding the proper production levels for the future. You need to understand whether the original assumptions were correct or not. So, you need to do it continuously because the physics-based calculations are very heavy. It takes weeks to calculate just one model.
Nowadays, with the help of data-driven models, it takes a couple of minutes to undertake this type of exercise. As soon as there is a production loss, our AI agents are able to adjust the model to derive more accurate results.
Last but not least, ENERGYai also incorporates tools for field development planning. Field development planning is very important because it involves different types of modalities, which include reservoir modelling, safety implementation, and production levels, combined to make investment decisions, with analyses on the profitability of the fields. This is exactly what ENERGYai supports with.
The system automatically suggests different scenarios, allowing the operator to make the final decision on the production level and financial results for the field development.
TER: How does ENERGYai help with prevention, detection, monitoring, and reporting methane emissions? What makes AIQ’s agentic AI platform a unique solution for decarbonization and digital transformation strategies, especially on critical infrastructures in the energy sector?
Roshchin: Great question. Let me give you two examples.
One example is about emissions. The oil and gas industry has a huge carbon footprint.
ENERGYai can help optimize energy efficiency for emission reduction, including at the production level. It provides information on how much energy will be produced at the end. This enables energy producers to double up on insights.
The second example is seismic interpretation. Seismic interpretation is about the identification of oil and gas resources. That’s typically what people see.
But you can apply the same type of approach for CO2 storage.
TER: Which specific role can ENERGYai play on existing assets, facilities, and industrial plants to boost operational excellence? And, are there any notable milestones you would like to share?
Roshchin: ENERGYai is within the deployment phase within ADNOC facilities in their onshore and offshore operations.
We are working towards introducing 7-10 different agents for selected workflows.
TER: What makes the ENERGYai platform a unique solution for field development in upstream operations?
Roshchin: This is a very good question. The world is changing, and we don’t know what will happen in the next 10 years.
However, we are the first to run this type of approach at scale. We have access to all the data that guarantees the final results for field development, and ENERGYai is much more efficient in comparison to any other AI technology in the market. This makes it competitive in the market.
TER: What is the pivotal role of artificial intelligence in this era of energy transition, and what do you think needs to be done in creating an enabling environment for AI globally?
Roshchin: Two major areas need to be addressed globally, particularly from a technology perspective. One of them is data. AI without data is impossible.
We spend up to 80% of our effort on data preparation and finding ways to make it actionable before it becomes AI-ready.
We spend a lot of effort on this. Many standards exist in the market, but we need to adjust those methods to an AI perspective.
The second area is the current operational workflows. People are still wondering if they are ready to use AI because they are used to their routine jobs. They still have this type of mindset. They’re not yet ready for AI. It requires a huge step, and we aren’t there yet. Artificial intelligence is a journey that should be embraced on a global scale.
TER: What’s next for AIQ? Which other markets is AIQ interested in deploying its AI technology in?
Roshchin: We are looking forward to integrating our AI technologies in the global energy market. The beauty of our technology is that it is scalable and adaptable to existing stacks.
We are very flexible from a technology perspective. This is the beauty of our AI agents, which can be considered as PhD-level experts who can be deployed at any scale.
AIQ is an agency of AI agents. If there is demand for seismic interpretation and field development planning, we deploy our AI agents to run the operations. It makes the process extremely straightforward. Though it requires data.
At AIQ, we want to scale globally, collaborating with companies on different continents, including Africa. Africa is one of our major focus areas in 2026 and beyond.
