Reservoir Prediction Uncertainty

Reservoir Prediction Uncertainty MCP Connector for Claude

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Quantifies uncertainty in reservoir predictions using parameter ranges and correlations.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

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.

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4 tools expose this connector's capabilities to your AI agent.

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

See how to talk to your AI agent using Reservoir Prediction Uncertainty.

What is the range of likely outcomes for my reservoir prediction given these parameters?

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

Which parameters are the main drivers of uncertainty in this model?

The primary uncertainty drivers are porosity and permeability.

Give me a summary of this probability distribution.

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

You can use the `calculate_prediction_intervals` tool by providing your parameter ranges and any existing correlations.

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