neqsim-pvt-regression-characterization-factor

PVT Regression of a Characterization Factor

Use this skill to regress a single split / characterization factor against several measured PVT or separator targets at once, with per-target weights and explicit residual reporting. It follows standard PVT regression practice (fitting EOS/characterization parameters to measured PVT experiment values with weights) and NeqSim's characterization plus PVT-simulation workflow.

The forward model is injected by the caller, so this skill has no dependency on a particular EOS. In practice it wraps a NeqSim characterization + flash / PVT-simulation evaluation, or the community pseudocomponent-split-characterization and reference-fluid-synthetic-generation skills.

When to Use

• When one heavy-end factor must reproduce multiple measured quantities (for example saturation pressure and stock-tank-oil density) simultaneously.
• When targets have different importance and need weighting.
• When you must report per-target residuals to judge whether the match is acceptable.
• When a full EOS regression is not warranted but a calibrated split factor is.

Inputs

• forward_model(factor) -> {target_name: predicted_value}: caller-supplied.
• targets: a list of RegressionTarget(name, measured, weight).
• low, high: bounds of the factor search interval.
• tol, max_iter: search controls.

Outputs

• RegressionResult: fitted factor, objective, per-target residuals and predictions, iterations, and convergence flag.
• weighted_ssr(predicted, targets): the weighted sum of squared relative residuals, usable as a standalone objective.

Engineering Method

Each residual is normalized: (predicted - measured) / measured, so quantities of different magnitude and units contribute comparably. The objective is the weighted sum of squared relative residuals. The factor is fitted by a robust golden-section 1-D search over [low, high] — no gradients, suitable for the noisy forward models produced by flash and PVT calculations.