Data Science and Engineering Analytics Technical Section

It is highly important to know and learn that Artificial Intelligence can make fantastic models in Petroleum Engineering and increasing oil and gas production and identification of where to drill for best productions.

Please note that the part of AI that can do this is Artificial Engineering Intelligence and is quite different from Artificial General Intelligence. What Artificial Engineering Intelligence does in Petroleum Engineering it does not use any Assumptions, any Interpretations, and no Data from any Mathematical equations. In all engineering and specifically in Petroleum Engineering AI must use Actual Real Data that would be ALL MEASURED DATA.

Also, I am sure all Petroleum Engineering expert known that when we must use ALL MEASURED DATA, some of it are missing and some of it are "incorrect" and must remove. In such cases Artificial Intelligence is 100% capable of generating the missing and removed incorrect data without any Assumptions, Interpretations, and Mathematical equations and then it builds the final model.

What I have mentioned above, it is absolutely nothing to do with business and marketing and it is all about 32 years of working with AI for Engineering. Everything I have mentioned here include incredible amount of presentation that all Petroleum Engineering experts will absolutely love it.

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Shahab D. Mohaghegh
Professor; Petroleum & Natural Gas Engineering
Director of (WVU-LEADS)
West Virginia University Laboratory for Engineering Application of Data Science
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Great post, thank you Shahab. The AI technology added fuel to the burning debate about relationship between the "real" Engineering/Science (read: data) and a "cartoon" (read: model)  Engineering/Science.  In my view, the answer is unchanged: the best results come from using strengths of both.  Any successful AI model must obey all available data, but also use its capability to "dream" in creating an ensemble of  plausible infill proposals for the rest of the data set, giving us an unprecedented view into the uncertainty of what are plausible outcomes that are still consistent with all available data.

Shahab,

That's an important insight into the AI for the petroleum industry.
You have also been teaching the courses through SPE on this topic. Is there
any course planned in the near future by you on this topic? In the
meantime, what would you suggest to an enthusiast like king to learn about
data science and AI?

Thanks
Amrendra

This is a great discussion about the role of AI.

I'd like to emphasize that the true value of AI isn't just in generating models or filling in data gaps but in how it transforms the decision-making process, allowing faster and more informed decisions to be made, thus improving the outcomes.

By using AI solutions we aiming to minimize time consuming and repetitive tasks, and human error in working with large amount of data, but in the end rise the quality of decision made by engineers.

Dear Luliia, I am 100% sure that AI will be able to generate models of physics and especially in Petroleum Engineering.

I can actually sue to you and present in details to you and your other petroleum Engineers.

I am 100% sure that all of you (petroleum engineering experts) would absolutely love what I can show you in AI since I have worked on it since 1991.

I can also show you what I have done for many companies in Oil and Gas Fantastic Forecasting and their production optimization as well as the results of where to drill new wells.

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Shahab D. Mohaghegh
Professor; Petroleum & Natural Gas Engineering
Director of (WVU-LEADS)
West Virginia University Laboratory for Engineering Application of Data Science
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Original Message:
Sent: 01-04-2025 09:32 AM
From: Iuliia Ziablitckaia
Subject: Artificial Intelligence Reservoir Simulation and Modeling

This is a great discussion about the role of AI.

I'd like to emphasize that the true value of AI isn't just in generating models or filling in data gaps but in how it transforms the decision-making process, allowing faster and more informed decisions to be made, thus improving the outcomes.

By using AI solutions we aiming to minimize time consuming and repetitive tasks, and human error in working with large amount of data, but in the end rise the quality of decision made by engineers.

A very important perspective, Professor Mohaghegh.

As AI matures across critical infrastructure sectors, the distinction between domain-specific engineering intelligence and broader AI capabilities becomes increasingly relevant. In environments such as energy, logistics, ports, and industrial ecosystems, value is often created through deep integration with operational realities and measured data rather than abstract generalization.

Equally important is the governance dimension. As AI becomes more deeply embedded in engineering and production systems, resilience, oversight, and continuity must evolve alongside capability. The future challenge may not simply be building more intelligent systems, but ensuring they remain trustworthy, accountable, and resilient under changing conditions.

Thank you for sharing your insights and decades of experience in this field.