# Buckley-Leverett Displacement Simulator AI Agent Connect

> Buckley-Leverett Displacement Simulator MCP uses Buckley-Leverett theory to model how water displaces oil in porous media. Your agent can calculate saturation distributions, predict when water reaches production wells, and evaluate displacement effectiveness using fractional flow dynamics and relative permeability data.

## Overview
- **Category:** engineering
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_VlNvrdohRX3zokeCM8y9X3vjqzK8L9AtDDArdDjI/ai-agent-connect
- **Tags:** reservoir, waterflood, porous-media, fluid-dynamics, petroleum-engineering

## Description

You can use this MCP to run reservoir engineering simulations directly through your AI client. It implements Buckley-Leverett theory to model the movement of water through porous media during waterflood operations. Instead of manually calculating shock fronts, you can ask your agent to handle the heavy lifting of fractional flow dynamics. 

By providing relative permeability data, you'll get precise insights into how water displaces oil. You can check the spatial distribution of water saturation at specific time intervals or determine exactly when the water front will hit your production well. It's a practical way to evaluate how well your displacement process is working and to estimate sweep efficiency without leaving your coding or chat environment. Whether you are modeling a small reservoir or a large-scale field, the engine handles the math for shock fronts and saturation profiles.

## Tools

### analyze_saturation_profile
This tool predicts how water saturation is distributed across the reservoir at a specific point in time.

### calculate_breakthrough
Use this to find out exactly when the water front will reach the production end of the reservoir.

### estimate_sweep_efficiency
This tool evaluates how effectively the displacement process is working at a given time.

### get_fractional_flow_curve
This tool generates fractional flow values for a specific range of water saturations.

## Prompt Examples

**Prompt:** 
```
Predict the water saturation profile for a 100m reservoir with an injection rate of 5, viscosities of 1 and 5, and specific permeability data at time 10.
```

**Response:** 
```
The water saturation profile shows a shock front at 45.2m with a saturation of 0.65, followed by a trailing edge saturation of 0.25.
```

**Prompt:** 
```
When will water breakthrough occur in a 500m reservoir with these fluid properties?
```

**Response:** 
```
Breakthrough is predicted to occur at 1250.5 units of time with a breakthrough saturation of 0.42.
```

**Prompt:** 
```
What is the sweep efficiency after 50 units of time?
```

**Response:** 
```
The volumetric sweep efficiency is 0.35 and the oil recovery factor is 0.28.
```

## Capabilities

### Saturation Modeling
Your agent uses this to map the spatial distribution of water across the reservoir.

### Breakthrough Prediction
The AI calculates the timing of the water front reaching the production well.

### Efficiency Evaluation
Your agent estimates the volumetric sweep efficiency of the displacement process.

### Fractional Flow Generation
The tool produces fractional flow curves based on water saturation levels.

### Shock Front Analysis
The engine accounts for shock fronts and fractional flow dynamics in porous media.

## Use Cases

### Waterflood Optimization
Model how water displaces oil to improve recovery factors in a reservoir.

### Breakthrough Forecasting
Predict when water will reach production wells to plan well management.

### Saturation Mapping
Visualize how water saturation changes spatially over time.

### Fluid Dynamics Analysis
Analyze fractional flow and shock front behavior in porous media.

## Benefits

- Calculates shock fronts using Buckley-Leverett theory.
- Predicts breakthrough timing based on fluid properties.
- Evaluates displacement effectiveness through sweep efficiency metrics.
- Generates fractional flow curves from relative permeability data.

## How It Works

The process moves from connecting your client to getting specific engineering outputs.

1. Connect your MCP-compatible client to Vinkius.
2. Provide reservoir dimensions, injection rates, and fluid viscosities to your agent.
3. Input relative permeability data for the specific media.
4. Ask your agent to run specific tools like breakthrough or saturation analysis.
5. Receive the calculated profiles or efficiency metrics directly in your chat.

## Frequently Asked Questions

**What theory does this MCP use for simulations?**
This MCP implements Buckley-Leverett theory to model water displacement in porous media.

**Can I use this with Claude or Cursor?**
Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf.

**What kind of data do I need to provide?**
You typically need to provide reservoir dimensions, injection rates, fluid viscosities, and relative permeability data.

**Does this MCP handle fractional flow?**
Yes, it includes a tool to generate fractional flow curves for a range of water saturations.

**How does it calculate sweep efficiency?**
The tool evaluates the effectiveness of the displacement process at a specific time based on the provided parameters.

**How can I predict when water will reach my production well?**
You can use the `calculate_breakthrough` tool. By providing the reservoir length, injection rate, and fluid viscosities, the tool determines the exact breakthrough time and saturation.

**Can I visualize the water saturation movement?**
Yes, the `analyze_saturation_profile` tool calculates the spatial distribution of water saturation across the reservoir at any given time.

**What parameters are required for the simulation?**
The simulation requires the reservoir length, injection rate, water and oil viscosities, and a JSON string containing the relative permeability data.