Engineers, bankers, and management teams are using AI to make A&D decisions. Do the AI tools truly understand the reserves database, or are they hallucinating and guessing?
An engineer or technician opens Aries or PHDWin, runs the cases, and exports the outputs. Outputs are then loaded into Spotfire, ComboCurve, Excel, and increasingly AI tools to build charts, graphs, and financial models. The appeal is obvious. The problem is that the exports are only as good as the database behind them, and most AI tools have no way to interrogate that database directly. They are working from a summary of outputs, without visibility into the assumptions, filters, and conventions that produced it.
When multiple packages are in play simultaneously, the problem compounds. Rack and stack analysis across several seller databases requires normalized reserve categories, consistent effective dates, and comparable cost assumptions. That is difficult to do manually and nearly impossible to do quickly. Export a cleaned, normalized database for your lender.
Why Uploading the File Is Not Enough
Take Aries and PHDWin for example. These are widely used and trusted economic systems containing custom logic, project filters, price assumptions, and forecast scenarios. Years of software engineering have gone into making these tools what they are today. Aries files are the data currency of A&D transactions in the oil and gas world.
One cannot simply point a general purpose AI tool at an Aries database or a PHDWin file and expect it to know anything meaningful about it. This leaves users exporting CSV files of readable text data for the models to consume and report on. Do you trust Claude or ChatGPT not to train their models on your data? Do you trust them not to regurgitate that data with a well-formed prompt from another user?
What Agentic Actually Means
At Tauris, we have a private, local AI model trained specifically on decoding and normalizing Aries and PHDWin data. Not a Claude or ChatGPT wrapper that other companies resell to you. This lets you privately rack and stack your data files without the risk of sensitive information being leaked to the cloud.
Combine your portfolio with several PHDWin and Aries opportunities and run cases into a single normalized output for distribution to a bank or capital partner.
That sequence produces something a general purpose chatbot cannot: an evidence layer with known provenance, documented gaps, and explicit context.
From there, the agent can answer the orientation questions that currently take days to work through manually. What projects are present? Which LOS actuals support or challenge the projected costs? Which is a better fit, Target A or Target B and then what are my financing needs?
That profile becomes the shared starting point for engineering review, banking diligence, and management decisions. It replaces informal, inconsistent orientation work with a documented, repeatable process.
The Larger Point
AI in oil and gas is not limited by model intelligence alone. It is limited by whether the data can be opened, understood, validated, and referenced correctly.
Aries and PHDWin are the clearest example in the A&D market. The data is there an siloed inside systems of record with variable naming conventions that a general purpose model cannot navigate without a structured extraction layer underneath it.
Contact us to learn more. Contact us to learn more.