Neqsim Reference Fluid Synthetic Generation

Reference-Fluid Synthetic Generation

Use this skill for the "common reference fluid → adjust split factor → match measured data → generate representative fluids" workflow. It provides three plant-agnostic, dependency-free helpers:

1. match_split_factor — a robust golden-section 1-D search that finds the split / characterization factor best reproducing a measured target.
2. generate_fluid_cases — build representative or synthetic fluid cases by applying a range of factors to a common reference fluid.
3. blend_compositions — combine several well or fluid compositions into a single field composition by molar-rate allocation.

The forward model / fluid builder is injected by the caller, so this skill has no dependency on a particular EOS. In practice it wraps the community pseudocomponent-split-characterization gamma split or a NeqSim characterization call, so the same factor drives both the split and the match.

When to Use

• When there is no PVT study at all — a discovery, a prospect, an early concept — and a reservoir model still needs a fluid. See No PVT data below.
• When you have a common reference EOS/fluid and want field-specific or per-case fluids by adjusting one heavy-end split factor (the "common factor" idea: reuse one characterization method with field-specific calibration).
• When a split factor must be calibrated so the model reproduces a measured saturation pressure, GOR, or stock-tank-oil density.
• When production is allocated across several wells or fluids and you need one representative field composition.
• When a complete PVT study is unavailable and you must generate a usable fluid from a reference plus available measurements.

No PVT data: build a best guess and say so

A reservoir model needs a fluid long before a laboratory PVT study exists, and often before a sample has been taken. The alternative to guessing silently is to guess explicitly.

from reference_fluid import build_analogue_fluid_basis

basis = build_analogue_fluid_basis(
    depth_tvdss_m=3590.0,
    province="northern_north_sea",
    formation="Brent",
    water_depth_m=381.0,
    measured_temperature_C=129.0,     # anything measured is used and labelled
    measured_pressure_bara=542.0,     # ... and anything absent is derived
)
basis.targets["gor_sm3_sm3"]      # value + provenance + GOR DEFINITION
basis.seed["c7_plus_cuts"]        # ready for addTBPfraction / addPlusFraction
basis.assumptions                 # each with the measurement that retires it
basis.acquisition_plan            # ranked by how much it reduces the answer

Derive temperature. Do not derive pressure.

This is the asymmetry that matters, and it is evidence-backed. On a Middle Jurassic northern North Sea reservoir at 3590 m TVDSS:

A seabed temperature and a provincial geothermal gradient reproduce reservoir temperature to about a degree. Overpressure cannot be predicted from depth at all, and 100–200 bar of it is routine in deep sections. So estimate_conditions() returns a derived temperature marked derived with a confidence note, and a derived pressure marked placeholder carrying a warning and the list of what it blocks: undersaturation, the depletion path, the well count, the drive mechanism.

Never present a study built on a derived pressure as anything but conditional on an RFT/MDT measurement.

Fluid type before anything else

classify_fluid_type(gor, api) places the fluid in the conventional GOR bands (heavy oil → black oil → volatile oil → gas condensate → wet/dry gas), reports whether a black-oil PVTO/PVDG table is adequate, and cross-checks the GOR against the stock-tank gravity. A GOR and a gravity that disagree mean one of them is wrong or the GOR is quoted on a different basis — which is the next trap.

The GOR definition trap

An unqualified "GOR" is ambiguous, and the spread is not small. The same tuned volatile oil gave:

A spread of 58 Sm3/Sm3, about 20 % of the quoted number. build_analogue_fluid_basis takes a gor_definition argument and, when it is not supplied, records "UNSTATED" plus an assumption-register entry. reconcile_gor_definitions() reports the spread once several are known, so the size and direction of the correction stay visible.

Seed composition, and how to tune it

seed_composition() returns a light-ends template for the classified fluid type plus a nine-cut C7+ set with molar masses and densities. Two rules travel with it because both were learned the hard way:

• Tune the C7+ mole fraction against GOR and a common density offset against stock-tank density, each by bisection on a bracketed interval. Do not tune molar masses. A 2-D Newton step on (C7+ fraction, molar-mass multiplier) runs to its bounds and produces a "C7" of 53 g/mol — a fluid that still flashes and is not a C7.
• NeqSim addTBPfraction/addPlusFraction take kg/mol, not g/mol. Passing g/mol fails silently: critical temperatures in the thousands of kelvin, an acentric factor near −1, and a standard-condition flash returning one phase typed GAS with a liquid density.

Grade the result honestly

basis_confidence(basis) counts how much of the basis is measured and returns data-driven, partially data-driven, analogue, or conditional when a placeholder is present. A production forecast inherits the grade of its fluid regardless of how good the static model is.

Inputs

• objective(factor) -> scalar: caller-supplied non-negative objective (for example sum of squared relative residuals against measured data).
• low, high: bounds of the split-factor search interval.
• builder(factor) -> composition: caller-supplied fluid builder.
• weighted_compositions: (molar_rate, composition) pairs for blending.

Outputs

• MatchResult: the best factor, objective, iterations, and convergence flag.
• A list of generated composition cases.
• BlendResult: the normalized field composition, total molar rate, and weights.

Engineering Method

Common reference fluid and split factor

A single common reference EOS/fluid (a validated, detailed characterization) is the basis. Field or per-case fluids are produced by re-splitting the heavy end with an adjustable factor rather than characterizing every fluid from scratch. This is the "common EOS" concept: FluidMagic pairs a common eos_full with a lumped eos_pseudo and converts between them; NeqSim recommends a consistent characterization model via TBPfractionModel.recommendTBPModel.

Matching the factor to measured data

match_split_factor minimizes a caller-supplied objective by golden-section search — no gradients, robust for the noisy, feasibility-gated forward models produced by flash/PVT calculations. Wrap the measured targets (saturation pressure, GOR, stock-tank density) as a sum of squared relative residuals.

Blending by allocation

blend_compositions converts each stream's mole fractions to moles using its molar rate, sums per component, and renormalizes — the correct way to combine allocated well or fluid streams into a field composition.

Python Usage Pattern

from reference_fluid import match_split_factor, generate_fluid_cases, blend_compositions

# 1. Calibrate the split factor against a measured saturation pressure.
def objective(alpha):
    predicted_psat = my_forward_model(alpha)   # e.g. NeqSim saturation pressure
    rel = (predicted_psat - measured_psat) / measured_psat
    return rel * rel

match = match_split_factor(objective, low=0.5, high=3.0)

# 2. Generate representative low/base/high cases from a reference fluid.
cases = generate_fluid_cases([0.8, match.factor, 1.2], build_fluid_from_reference)

# 3. Blend three wells into one field fluid by molar rate.
field = blend_compositions([
    (3200.0, well_1_composition),
    (1500.0, well_2_composition),
    (900.0, well_3_composition),
])

Related NeqSim Functionality

• neqsim.thermo.characterization.PlusFractionModel / PlusCharacterize — the heavy-end split whose alpha/eta is the factor matched here.
• neqsim.thermo.characterization.TBPfractionModel.recommendTBPModel — pick a consistent characterization model (PedersenSRK/PR, Twu, RiaziDaubert).
• neqsim.thermo.characterization.Recombine — recombine separator gas and oil.
• Rigorous saturation-pressure / GOR / density come from a SystemInterface fluid with ThermodynamicOperations (for example bubblePointPressureFlash).

Pair this skill with neqsim-pseudocomponent-split-characterization for the split itself and neqsim-pvt-regression-characterization-factor for a multi-target regression.

Validation Checklist

• The common reference fluid and its source are documented.
• The objective used by match_split_factor is stated (which measured targets, which residual form) and the returned converged flag is checked.
• Generated fluid cases span a justified factor range (for example low/base/high).
• Blend molar rates are non-negative, their source is stated, and the blended composition sums to 1.
• Assumptions and limitations are recorded and qualified PVT review is planned.

Common Mistakes

• Passing low >= high to match_split_factor (raises).
• Using an objective that is not smooth/unimodal over the interval, so the golden-section search returns a non-global point.
• Blending mole fractions without weighting by molar rate (loses allocation).
• Assuming one factor represents a whole field when the fluid varies by region.

Limitations

• Screening-level; the caller's forward model determines physical accuracy.
• A single factor may not represent a whole field — evaluate per-region or per-well factors when the fluid varies significantly.
• Results require qualified PVT review before design or operational use.

References

• Whitson, C.H., Brulé, M.R. (2000). Phase Behavior, SPE Monograph 20.
• Pedersen, K.S. et al. (2015). Phase Behavior of Petroleum Reservoir Fluids, 2nd ed.
• NeqSim: https://github.com/equinor/neqsim