# Neqsim Pseudocomponent Split Characterization > Public plus-fraction (C7+) characterization by a controllable split factor: a Whitson three-parameter gamma molar split, a lumping split factor, delumping reconstruction, and the universal Paraffinic-Aromatic (P/A) heavy-lump split factor S (Uleberg 2026). USE WHEN: a task needs to divide a heavy end into pseudocomponents with one adjustable characterization/split factor, compute a delumping split factor from a reference fluid, reconstruct a detailed composition from a lumped one, or split heavy lumps into paraffinic/aromatic copies on a universal P/A set, before rigorous NeqSim characterization. - Skill: `equinor/neqsim-pseudocomponent-split-characterization` (Agent Skill, multi-file: 9 files) - Install (CLI): `npx skillmds@latest add equinor/neqsim-pseudocomponent-split-characterization` - Raw SKILL.md: https://api.skillmd.com/api/skills/equinor/neqsim-pseudocomponent-split-characterization/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: equinor (https://skillmd.com/u/equinor) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/equinor/neqsim-pseudocomponent-split-characterization --- # Pseudocomponent Split-Factor Characterization Use this skill to represent a reservoir fluid with a **small, controllable number of adjustable factors** instead of building a bespoke characterization for every fluid. It provides three plant-agnostic, dependency-free building blocks: 1. A **Whitson three-parameter gamma** molar split of a plus fraction (C7+), governed by a single split/characterization factor `alpha`. 2. A **lumping split factor** computed from a detailed reference composition. 3. A **delumping** reconstruction that turns a lumped composition back into detailed components using that split factor. These are screening-level helpers. For design-grade work, move to the rigorous NeqSim `neqsim.thermo.characterization` Java classes described below. ## When to Use - When a fluid must be described by detailed light components plus a heavy pseudocomponent set, and you want one factor to control the heavy-end split. - When you have a **reference fluid** and want to generate representative or synthetic fluids by adjusting the split factor. - When a **lumped** composition (few components) must be **delumped** back to a detailed composition using the internal distribution of a reference fluid. - When you need a transparent, reproducible split before running the rigorous NeqSim characterization for design-grade work. - When a fluid is represented on a **universal Paraffinic-Aromatic (P/A)** set (fixed 10 light + N paraffinic + N aromatic heavy lumps) and its heavy-end character is carried by a single **split factor S** (the paraffinic fraction of each heavy lump), per the Uleberg (2026) universal characterisation. ## Inputs - `z_plus`: total mole fraction of the plus fraction, in `(0, 1]`. - `m_plus`: average molar mass of the plus fraction (g/mol). - `boundaries`: increasing molar-mass boundaries (g/mol); `n+1` values give `n` pseudocomponents. The last boundary may be `math.inf`. - `alpha`: gamma shape / split factor (`> 0`; `1.0` = exponential heavy end). - `eta`: minimum molar mass of the distribution (g/mol). - `full_composition` and `lumping_scheme` for the split-factor / delumping path. ## Outputs - `GammaSplitResult`: per-pseudocomponent mole fractions and average molar masses. - `SplitFactorResult`: per-component split factors and per-lump totals. - Delumped detailed composition (tuple of mole fractions). ## Engineering Method ### Whitson gamma split The heavy end is described by the three-parameter gamma probability density $$ p(M) = \frac{(M-\eta)^{\alpha-1}\,\exp\!\left(-\frac{M-\eta}{\beta}\right)}{\beta^{\alpha}\,\Gamma(\alpha)}, \qquad \beta = \frac{M_{+}-\eta}{\alpha} $$ where `M` is molar mass, `eta` the minimum molar mass, `alpha` the split factor, and `M+` the plus-fraction average molar mass. Mole fractions in each molar-mass interval are obtained from the regularized lower incomplete gamma function `P(alpha, y)`, and each pseudocomponent's average molar mass from `P(alpha+1, y)`. `alpha = 1` yields an exponential (Pedersen-like) distribution; higher `alpha` narrows the distribution and lightens the tail. ### Lumping split factor and delumping For each lump, the split factor of a detailed component is its mole fraction divided by the lump total (each lump's factors sum to 1). A lumped composition is delumped by multiplying each lump value by its component split factors. This mirrors FluidMagic's `EOSConverter.calculate_split_factor` / `_delump`. ### Universal Paraffinic-Aromatic (P/A) split factor S The universal characterisation of Uleberg (2026) keeps one fixed component set (10 light + N paraffinic + N aromatic heavy lumps) and carries each feed's heavy-end character in a single split factor. For heavy lump `i` with total mole fraction `Z_i`, the split factor `S_i` (the paraffinic fraction) divides it into a paraffinic copy `P_i = S_i Z_i` and an aromatic copy `A_i = (1 - S_i) Z_i`, so moles are conserved. The recommended operational form is a **single constant `S`** applied to every heavy lump; a general two-endpoint form interpolates `S_i` linearly in molecular weight between `S1` (lightest heavy lump) and `Sn` (heaviest), clipped to `[eps, 1-eps]`. For a feed with no separator calibration, `S` is assigned provisionally from the screening correlation `S ~= 1.3298 - 0.003531 * MW_C7+` (MW in g/mol; clipped). `S` calibrates the heavy-end **stock-tank-oil density** (and only weakly GOR); the light/heavy molar ratio — hence the GOR — is set by the feed's own composition (its ZI), not by `S`. The fixed critical properties, acentric factors, volume shifts and BIPs of the paraffinic and aromatic families live in the universal EOS; this skill only performs the molar split. ## Python Usage Pattern ```python import math from pseudocomponent_split import ( gamma_mole_split, calculate_split_factor, delump_composition, ) # 1. Split a C7+ plus fraction into 4 pseudocomponents with split factor alpha. split = gamma_mole_split( z_plus=0.05, m_plus=220.0, boundaries=[90.0, 140.0, 200.0, 300.0, math.inf], alpha=1.0, eta=90.0, ) print(split.mole_fractions, split.molar_masses) # 2. Split factor from a detailed reference fluid, then delump a lumped fluid. full = [0.70, 0.10, 0.06, 0.04, 0.03, 0.07] scheme = [[0], [1, 2], [3, 4, 5]] sf = calculate_split_factor(full, scheme) detailed = delump_composition(list(sf.lump_totals), sf.split_factors, scheme) ``` ### Universal P/A split factor S ```python from pseudocomponent_split import ( c7plus_split_correlation, constant_pa_split, apply_constant_split_to_vector, ) # Provisional constant S when a feed has no separator calibration. S = c7plus_split_correlation(mw_c7plus=210.0) # ~0.588, clipped to [0.02, 0.98] # Split heavy-lump totals into paraffinic / aromatic copies (P_i, A_i). core_totals = [0.02, 0.015, 0.01, 0.005] paraffinic, aromatic = constant_pa_split(core_totals, S) # Re-split a fluid already on a universal P/A set (names end in P / A) with a # new constant S; light components pass through unchanged, moles conserved. names = ["N2", "C1", "C7P", "C7A", "C8P", "C8A"] zi = [0.02, 0.90, 0.03, 0.01, 0.03, 0.01] zi_new = apply_constant_split_to_vector(names, zi, split_factor=0.5) ``` Map a feed's **own** composition (its ZI) onto the universal P/A cores by nearest molecular weight (paper Section 2.2), so the light/heavy ratio — hence the GOR — comes from the feed, not from a template: ```python from pseudocomponent_split import map_source_to_pa_core_totals m = map_source_to_pa_core_totals( source_names=["C1", "CO2", "C7", "C10", "C30"], source_fractions=[0.80, 0.02, 0.10, 0.05, 0.03], source_molecular_weights=[16.0, 44.0, 96.0, 140.0, 400.0], light_names=["N2", "CO2", "C1", "C2", "C3"], core_labels=["C7", "C10-14", "C31-50"], core_molecular_weights=[96.0, 150.0, 500.0], ) # m.light_fractions -> light slots; m.core_totals -> heavy-lump Z_i to split by S ``` ## Related NeqSim Functionality For rigorous, design-grade characterization use the NeqSim Java classes in `neqsim.thermo.characterization`: - `PlusFractionModel` — gamma molar distribution with `alpha`, `eta`, and a Watson `Kw`-based auto-estimate of `alpha` (`estimateAlpha`, `setAlpha`, `setEta`, `setGammaParameters`). - `PlusCharacterize` / `Characterise` — drive the plus-fraction split from `SystemInterface` composition and plus-fraction molar mass / density. - `TBPCharacterize` / `TBPfractionModel` — true-boiling-point pseudocomponents. - `LumpingModel` + `LumpingConfigBuilder` and `PseudoComponentCombiner` — lumping of detailed components into pseudocomponents. - `Recombine` — recombine separator gas and oil to a reservoir fluid. In Python these are reachable through the `neqsim` package (for example `from neqsim import jneqsim`). ## Validation Checklist - Plus-fraction mole fraction (`z_plus`), molar mass (`m_plus`), `eta`, and the molar-mass boundaries are stated with sources. - The split factor `alpha` is documented, and the resulting pseudocomponent mole fractions sum to `z_plus`. - Per-pseudocomponent average molar masses increase monotonically and the mole-fraction-weighted average is close to `m_plus`. - For delumping, each lump's split factors sum to 1 and a round-trip reconstruction reproduces the reference composition. - Assumptions and limitations are recorded and qualified PVT review is planned. ## Common Mistakes - Choosing boundaries below `eta`, or a non-increasing boundary list (raises). - Setting `m_plus` at or below `eta` (the mean must exceed the minimum). - Treating the screening split as a tuned EOS; it sets a distribution, not critical properties. - Reusing one lump's split factor for a different reference fluid whose internal distribution differs. ## Limitations - The gamma split and split factor are screening-level. They do not tune an EOS, set critical properties, or guarantee phase-behavior accuracy. - Boundaries, `eta`, and `alpha` must be chosen with engineering judgement. - Results must be reviewed by a qualified PVT engineer before design or operational use. ## References - Uleberg, K. (2026). *Legacy Common-Slate vs. Universal Paraffinic-Aromatic Fluid Characterisation in Process Simulation: A Sleipner A Multi-Feed Case Study*, Equinor ASA (universal P/A split factor S, Eqs. 3-4, Section 2.2). - Whitson, C.H., Brulé, M.R. (2000). *Phase Behavior*, SPE Monograph 20. - Pedersen, K.S., Christensen, P.L., Shaikh, J.A. (2015). *Phase Behavior of Petroleum Reservoir Fluids*, 2nd ed. - NeqSim: https://github.com/equinor/neqsim