# Reservoir Prediction Uncertainty AI Agent Connect

> Quantifies uncertainty in reservoir predictions using parameter ranges and correlations.

## Overview
- **Category:** engineering
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_sRj0Pdms1a05U1Qhtp7BARs4YfXyEnVOoOw0pSPw/ai-agent-connect
- **Tags:** reservoir, uncertainty, prediction, geology, statistics

## Description

This MCP server provides tools to quantify uncertainty in reservoir engineering predictions. By propagating parameter ranges and accounting for correlations, it calculates confidence intervals, identifies key uncertainty drivers, and generates statistical summaries of probability distributions. Use `calculate_prediction_intervals` to find likely outcome ranges, `identify_uncertainty_drivers` to find high-impact parameters, and `generate_distribution_summary` for statistical insights.

## Tools

### calculate_prediction_intervals
Calculates prediction intervals using parameter ranges and correlations

### generate_distribution_summary
Generates a statistical summary of a probability distribution

### identify_uncertainty_drivers
Identifies key uncertainty drivers by ranking sensitivity

### validate_parameter_consistency
Validates if parameter ranges and correlations are logically compatible

## Prompt Examples

**Prompt:** 
```
What is the range of likely outcomes for my reservoir prediction given these parameters?
```

**Response:** 
```
The predicted fluid recovery range is between 1.2 million and 1.8 million barrels at a 95% confidence level.
```

**Prompt:** 
```
Which parameters are the main drivers of uncertainty in this model?
```

**Response:** 
```
The primary uncertainty drivers are porosity and permeability.
```

**Prompt:** 
```
Give me a summary of this probability distribution.
```

**Response:** 
```
The distribution has a mean of 1.5 million, a median of 1.48 million, and a standard deviation of 0.15 million.
```

## Frequently Asked Questions

**How do I calculate the range of likely outcomes?**
You can use the `calculate_prediction_intervals` tool by providing your parameter ranges and any existing correlations.

**How can I find which parameters impact my model most?**
Use the `identify_uncertainty_drivers` tool to rank parameters based on their sensitivity scores.

**Can I check if my input data is logically consistent?**
Yes, the `validate_parameter_consistency` tool checks if your parameter ranges and correlations are physically and logically compatible.