Predict Fluids Properties

We don’t need millions of datasets. We needed the right ones. 

At Fluidsdata, we’ve built something different. Not another LLM. Not a black box. But a physics-backed, domain-driven AI model—designed specifically for reservoir fluid property prediction.

Why does this matter?

Because predicting PVT properties from fluid compositions isn't a language problem. It's a science problem. It requires an understanding of how molecules behave under pressure and temperature, how GOR shifts across clusters, and how subtle variations in C7+ can influence compressibility or phase behavior. That’s where domain expertise matters.

And in our world, data is expensive. Fluid sampling and  analysis can cost hundreds of thousands dollars to millions of dollars for each well or campaign. So we trained our model to work with fewer data points, smarter inputs, and physics-grounded features.

We don’t just train a model. We embed decades of fluid characterization expertise into it.

Once the AI model is built, we make the PVT properties predictions accessible in seconds instead of waiting for months for fluid samples and its analyses.

Fluids characterization data—such as composition, saturation pressure, viscosity, GOR, and more—plays a vital role in reservoir modeling, reserve estimation, production assurance, and facility design. Fluidsdata’s AI-ML models offer a game-changing solution by predicting these fluid properties with unparalleled accuracy. This is especially beneficial for operators working with multiple wells with similar compositions, no matter their geographic location, where clusters of reservoir fluid compositions can be leveraged for for optimal results.

Rather than defaulting to expensive and time-consuming reservoir fluid sampling and analysis programs, operators can now utilize our AI-ML technology to extract insights from historical data, minimizing the need for fluid characterization tests, and more importantly expensive fluid sampling. If a new fluid closely resembles the fluid composition in the existing cluster on which the AI-ML prediction model has been already developed, there may be no need for additional testing—saving clients up to 80% in costs.