# Enhanced Oil Recovery Screening AI Agent Connect

> Enhanced Oil Recovery Screening MCP analyzes reservoir parameters like oil gravity, viscosity, and temperature to determine the best recovery strategies. Your AI client uses this data to rank thermal, chemical, or gas injection methods and provides technical justifications for each recommendation.

## Overview
- **Category:** energy
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_hkYVZXjbfh5mcwQAYkzKDoqQ98ED0IAvimvE23hS/ai-agent-connect
- **Tags:** eor, oil-recovery, reservoir-engineering, petroleum, screening

## Description

You can use this MCP to run technical screenings for Enhanced Oil Recovery (EOR) strategies. Instead of manually checking parameters against technical windows, you provide your reservoir data to your AI client and let it handle the heavy lifting. It evaluates how oil gravity, viscosity, and temperature affect the viability of different injection methods. You can rank potential techniques like gas injection or chemical flooding and get a clear breakdown of why a specific method was or wasn't selected. If you need to see how a change in reservoir depth or oil saturation shifts your options, you can run sensitivity analyses to compare different profiles. It's a direct way to move from raw reservoir data to a ranked list of feasible EOR techniques.

## Tools

### get_eor_screening_criteria
This tool pulls the hardcoded technical windows and constants used to run the screening process.

### get_method_feasibility_details
This tool provides a technical breakdown explaining why a specific EOR method was recommended or rejected.

### screen_eor_methods
This tool evaluates all available EOR methods against your specific reservoir parameters.

### compare_eor_scenarios
This tool compares two different reservoir profiles to show how changing one variable changes the optimal EOR strategy.

## Prompt Examples

**Prompt:** 
```
Screen EOR methods for an oil with 30 API gravity, 50 cP viscosity, 80C temperature, 1500m depth, and 0.6 oil saturation.
```

**Response:** 
```
The most suitable method is gas injection with a suitability score of 0.85, followed by chemical flooding at 0.65.
```

**Prompt:** 
```
Why is thermal injection not recommended for this reservoir?
```

**Response:** 
```
Thermal injection is not recommended because the oil gravity is too low and the reservoir depth exceeds the heat loss limits for steam injection.
```

**Prompt:** 
```
What are the technical constraints for the chemical category?
```

**Response:** 
```
The chemical category constraints include temperature limits to prevent surfactant degradation and specific salinity tolerances.
```

## Capabilities

### Method Ranking
Your agent ranks various EOR techniques based on their suitability for your specific reservoir.

### Sensitivity Analysis
Your AI client compares two different reservoir profiles to see how a single variable shift impacts the strategy.

### Technical Justification
Your agent explains the specific reasons why a method is or is not viable for a given set of parameters.

### Parameter Validation
Your AI client checks reservoir data against hardcoded technical windows and constants.

## Use Cases

### Initial EOR Screening
Input new reservoir data to get a ranked list of viable injection methods.

### Sensitivity Testing
Compare two reservoir profiles to see how changing oil viscosity shifts the optimal strategy.

### Technical Auditing
Use the feasibility details to understand why a specific method was excluded from a recommendation.

### Constraint Verification
Check if reservoir temperature or salinity falls within the allowed technical windows for chemical flooding.

## Benefits

- Reduces manual checking of technical windows against reservoir data.
- Provides immediate technical reasoning for method recommendations.
- Enables quick comparison of how variable changes affect EOR outcomes.

## How It Works

Connect the MCP to your AI client and start querying reservoir data.

1. Connect the MCP to Claude, Cursor, or Windsurf via Vinkius.
2. Provide reservoir parameters like API gravity, viscosity, and temperature to your AI client.
3. The AI uses the screening tools to evaluate the data.
4. Receive a ranked list of EOR methods and technical justifications.

## Frequently Asked Questions

**What kind of reservoir data is needed for screening?**
You need to provide parameters such as oil gravity, viscosity, temperature, depth, and oil saturation.

**How does the MCP handle different EOR methods?**
The MCP evaluates thermal, chemical, and gas injection methods against the provided reservoir constraints.

**Can I compare two different reservoir scenarios?**
Yes, you can use the comparison tool to see how changing a single variable shifts the optimal EOR strategy between two profiles.

**Where does the technical criteria come from?**
The MCP uses hardcoded technical windows and constants to perform the screening and evaluations.

**Which AI clients can use this MCP?**
You can use this MCP with any compatible client like Claude, Cursor, or Windsurf.

**What parameters are required for screening?**
To use `screen_eor_methods`, you must provide oil gravity, viscosity, reservoir temperature, depth, and remaining oil saturation.

**How are suitability scores determined?**
Suitability scores are calculated by comparing your reservoir inputs against the technical constraints stored in the EOR Screening Database.