Dead Oil Viscosity Calculator
Product of Interaction of Phase-Behavior and Flow (IPB&F) Consortium.
Calculates the Viscosity (cp) of dead oil using Molecular Weight of Stock Tank Oil (MW), API, and Temperature of Interest (°C) using XGB Method.
Dr. Birol Dindoruk
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Utkarsh Sinha
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CO₂ Minimum Miscibility Pressure (MMP) Predictor
A product of Interaction of Phase-Behavior and Flow (IPB&F) Consortium.
Developed by – Utkarsh Sinha, Dr. Birol Dindoruk and Dr. M.Y. Soliman
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Dr. Birol Dindoruk
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Utkarsh Sinha
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Product Description: Calculates the Minimum Miscibility Pressure (psia) for pure CO₂ injection.
HC-Gas-MMP App: A Physics-Guided Tool for Rapid Miscibility Screening
The HC-Gas-MMP App is an interactive Streamlit-based web application developed to support screening and design of hydrocarbon gas injection and Enhanced Oil Recovery (EOR) projects. The tool enables rapid estimation of Minimum Miscibility Pressure (MMP) and Minimum Miscible Enrichment (MME)—two critical parameters used to evaluate the feasibility and effectiveness of miscible gas injection. By transforming advanced thermodynamic and data-driven modeling into a lightweight, accessible interface, the app allows engineers, researchers, and students to quickly analyze reservoir and injection-gas scenarios without the need for complex compositional simulation workflows.
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Dr. Birol Dindoruk
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Utkarsh Sinha
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The application is built on the physics-guided, data-driven methodology presented in Physics-Guided Data-Driven Model to Estimate Minimum Miscibility Pressure (MMP) for Hydrocarbon Gases (Sinha, Dindoruk, and Soliman, Geoenergy Science & Engineering). At its core is a Light Gradient Boosting Machine (LightGBM) model enhanced with physics-informed feature engineering to ensure predictions remain consistent with the governing principles of multi-contact miscibility. The workflow requires only readily available inputs such as reservoir temperature, oil and gas compositions, and key fluid properties, while incorporating derived parameters (e.g., characterization factors and pseudo-critical properties) to capture the functional dependence of miscibility.
Beyond technical prediction, the HC-Gas-MMP App supports broader operational, economic, and environmental decision-making. By enabling rapid evaluation of miscible injection feasibility using locally available hydrocarbon gases, the tool helps operators optimize recovery, reduce flaring, and minimize greenhouse-gas emissions. It also supports early-stage screening, injection-program design, and future CCUS planning by providing a practical bridge between academic research and field deployment.
Q&A Chatbot on Physics-Informed Machine Learning
The Q&A Chatbot on Physics-Informed Machine Learning (PIML) was developed as an interactive companion to the review paper
“Review of Physics-Informed Machine Learning (PIML) Methods Applications in Subsurface Engineering”
(Sinha and Dindoruk, 2025, Geoenergy Science & Engineering).
🔑 Access key: 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
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Dr. Birol Dindoruk
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Utkarsh Sinha
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The chatbot transforms the review paper into an interactive knowledge assistant. Instead of manually navigating the paper, users can ask technical questions and receive context-aware answers grounded directly in the source material. This enables faster exploration of complex topics related to physics-informed machine learning in subsurface engineering.
The system is built using a Retrieval-Augmented Generation (RAG) framework combined with FAISS vector search and Group Relative Policy Optimization (GRPO). When a user submits a question, FAISS retrieves the most relevant text sections from the paper, which are then supplied to the language model through RAG to ensure responses remain grounded in the literature. GRPO further improves answer quality through context-aware reinforcement learning and prompt optimization, enhancing clarity, relevance, and factual consistency.
Physics-informed machine learning integrates governing physical laws with data-driven algorithms to improve predictive reliability in complex engineering systems. In subsurface engineering applications, this hybrid framework helps overcome limitations of purely physics-based simulations and purely data-driven models by combining the strengths of both approaches. As a result, PIML methods are increasingly being applied to reservoir characterization, flow modeling, and energy-transition technologies.