MCP For Claude Marketplace
History Matching Quality

History Matching Quality MCP Connector for Claude

A+

Quantifies the alignment between simulated reservoir production and observed historical data.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides tools to quantify how well reservoir simulation models match historical field observations. By comparing simulated production data against the observation tier, users can calculate critical error metrics like RMS error, NDS error, and R² (coefficient of determination). Use get_global_match_metrics for a high-level overview, get_well_performance_analysis to pinpoint specific wells causing discrepancies, and get_temporal_error_analysis to detect failures in specific time intervals. It also includes get_feature_comparison_statistics to validate statistical distributions between datasets.

reservoirsimulationhistory-matchingpetroleumerror-metrics

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

get_feature_comparison_statistics

Compares the statistical distributions of simulated vs. observed data

get_global_match_metrics

Provides a high-level overview of how well the entire simulation matches the historical observations

get_temporal_error_analysis

Detects specific time periods or stages where the simulation fails to capture reservoir behavior

get_well_performance_analysis

Identifies which specific wells are contributing most to the total error in the simulation

See how to talk to your AI agent using History Matching Quality.

What is the global match quality for my simulation data?

The global match quality score is 0.85, with an RMS error of 12.4 and an R² of 0.91.

Which wells are showing the highest error in the current model?

Well W-102 is the primary contributor to error, showing a match quality of 0.42.

Did the simulation match the production during the first six months?

During the interval from 2023-01-01 to 2023-06-30, the R² was 0.88 and the RMS error was 5.2.

You can use the `get_global_match_metrics` tool to obtain a composite match quality score, RMS error, and R² value for the entire dataset.

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