DeepPVT™

DeepPVT™ is an AI-powered, modular platform designed for comprehensive PVT (Pressure-Volume-Temperature) analysis, reservoir fluid characterization, and advanced material balance computations. Tailored for engineers, researchers, and data scientists. DeepPVT supports multiple workflows including Volumetrics, Material Balance (OOIP/OGIP), EOS Tuning, AI-driven predictions, and fluid behaviour forecasting—all within a cloud-ready, extensible platform.

Traditional PVT analysis is labour-intensive and scattered across multiple tools and spreadsheets. Engineers struggle with:

• Lack of integration between material balance, compositional analysis, EOS modelling, and machine learning workflows.
• Manual efforts for plotting, curve fitting, and history matching.
• Inefficient data handling, lack of real-time simulation and difficult report generation.
• A steep learning curve for digital twin and AI/ML adoption in fluid analysis.

To build a unified, AI-enhanced reservoir fluid analysis platform that enables:

• Seamless transition from basic to advanced PVT workflows
• Integration of AI/ML for predictive modelling, EOS tuning, and optimization
• Automation of routine engineering tasks, including report generation
• Support for both static (historical) and dynamic (live simulation) fluid characterization
• Compatibility with field-ready workflows like gas injection, decline curve analysis, and digital twin modelling

DeepPVT™ brings together a suite of tools under a tab-based interface:

• Volumetrics & Contour Mapping: OOIP & OGIP calculation with reservoir property maps.
• Gas & Oil Material Balance: Analytical material balance modelling with visualizations and reporting.
• EOS Tuning & AI Modelling: Calibrate EOS using lab and simulated data; train ML models to predict PVT properties.
• AI Tabs:
  • AI-powered bubble/dew point detection
  • Gas injection optimization
  • Predictive analytics for reservoir fluid behaviour
• Digital Twin & Fluid Classification: Real-time PVT simulations and phase type prediction.
• Uncertainty Quantification: Monte Carlo simulations with sensitivity sliders.
• PDF Reporting Engine: Automated downloadable reports for every workflow.

Implementing the solution leads to several benefits:

• Accelerated Decision-Making: Engineers can run sensitivity, optimization, and prediction in minutes.
• Reduced Manual Workload: Automates plots, calculations, and reporting.
• Enhanced Accuracy: Integrates EOS, AI/ML, and classical models for more reliable insights.
• Knowledge Retention: Modular structure ensures repeatable workflows across teams.
• Future-Ready: Supports live simulations, integrations with digital twins, and scalable ML deployment.

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