ANN Phase Envelope Prediction from Downhole Fluid Data

The system performs preliminary actions by collecting and processing downhole fluid analysis data in real-time at the wellsite, before laboratory analysis is completed. The ANN model is trained offline with comprehensive data, enabling rapid online predictions that eliminate the months-long waiting period for lab results while maintaining accuracy through pre-processed high-quality input data.

An artificial neural network model serves as an intermediary between downhole fluid analysis data and phase envelope determination. The ANN processes the data through learned relationships from training data, providing accurate predictions without requiring direct laboratory measurement and thermodynamic model calculation, thus bridging the gap between rapid data collection and reliable fluid characterization.

The patent replaces the mechanical system of physical laboratory PVT tests and manual EoS model development with an automated computational system. The ANN model, trained on comprehensive training data including PVT measurements, automatically predicts fluid properties and phase envelopes, eliminating the need for operators to manually conduct experiments and develop thermodynamic models, thus dramatically improving productivity while maintaining reliability.

The system enables self-service by automatically processing downhole fluid analysis data through the trained ANN model to generate phase envelopes and fluid property predictions without requiring expert intervention. The model autonomously performs the tasks previously requiring specialized reservoir fluid analysis expertise, making the process both faster and more accessible to non-experts.

The ANN model performs self-service by automatically determining fluid parameters and phase envelopes from downhole analysis data without requiring operator intervention in the complex calculations. The model handles data processing, parameter estimation, and phase envelope generation autonomously, eliminating the need for operators to navigate complex thermodynamic equations and manual model development procedures.

The ANN model acts as an intermediary that simplifies the complex relationship between downhole fluid analysis data and phase envelope determination. Instead of requiring operators to directly apply complex thermodynamic models, the ANN has already learned the relationships during training, providing simplified input-output mapping that maintains precision while dramatically easing operation.