Asphaltene Modeling in NeqSim

Introduction

Asphaltenes are the heaviest and most polar fraction of crude oil, defined operationally as the fraction soluble in aromatic solvents (toluene) but insoluble in paraffinic solvents (n-heptane or n-pentane). Asphaltene precipitation during production can cause:

Wellbore plugging - Reduced production rates or complete blockage
Reservoir damage - Near-wellbore permeability reduction
Pipeline deposition - Flow restriction and increased pressure drop
Equipment fouling - Reduced heat transfer and separation efficiency

NeqSim provides multiple approaches to assess asphaltene stability, ranging from simple empirical correlations to rigorous thermodynamic modeling.

Asphaltene Structure and Properties

Molecular Characteristics

Property	Typical Range	Notes
Molecular Weight	500 - 10,000 g/mol	Polydisperse distribution
H/C Ratio	0.9 - 1.2	Lower than other oil fractions
Heteroatom Content	0.5 - 10 wt%	N, S, O, metals (V, Ni)
Aromaticity	40 - 60%	Polyaromatic core structures

Colloidal Model

Asphaltenes exist in crude oil as colloidal particles stabilized by resins (maltenes). The Colloidal Instability Index (CII) quantifies this balance:

\[\text{CII} = \frac{\text{Saturates} + \text{Asphaltenes}}{\text{Aromatics} + \text{Resins}}\]

Where:

CII < 0.7: Stable oil (asphaltenes well-dispersed)
CII 0.7 - 0.9: Metastable (precipitation possible under stress)
CII > 0.9: Unstable oil (high precipitation risk)

Resin-to-Asphaltene Ratio

Another stability indicator:

\[\text{R/A} = \frac{\text{Resins wt\%}}{\text{Asphaltenes wt\%}}\]

R/A > 3: Generally stable
R/A 1.5 - 3: Moderate risk
R/A < 1.5: High precipitation risk

SARA Analysis

SARA fractionation separates crude oil into four pseudo-component groups:

Fraction	Description	Typical Range
Saturates	Alkanes and cycloalkanes	40-80%
Aromatics	Mono/poly-aromatic hydrocarbons	10-35%
Resins	Polar, heteroatom-containing	5-25%
Asphaltenes	Heaviest polar fraction	0-15%

Using SARA in NeqSim

import neqsim.thermo.characterization.AsphalteneCharacterization;

// Create characterization from SARA analysis
AsphalteneCharacterization sara = new AsphalteneCharacterization(
    0.45,  // Saturates weight fraction
    0.30,  // Aromatics weight fraction
    0.20,  // Resins weight fraction
    0.05   // Asphaltenes weight fraction
);

// Get stability indicators
double cii = sara.getColloidalInstabilityIndex();
double ra = sara.getResinToAsphalteneRatio();
String stability = sara.evaluateStability();

System.out.println("CII: " + cii);
System.out.println("R/A Ratio: " + ra);
System.out.println("Stability: " + stability);

Precipitation Triggers

Asphaltene precipitation occurs when the solubility parameter of the oil phase changes sufficiently to destabilize the asphaltene colloids:

Pressure Depletion

As pressure drops below the bubble point:

Light components evolve into vapor phase
Remaining liquid becomes denser and more aliphatic
Asphaltene solubility decreases
Maximum instability typically occurs near bubble point

Temperature Changes

Cooling: Can increase or decrease stability (system-dependent)
Heating: Generally increases asphaltene solubility

Composition Changes

Gas injection (CO₂, hydrocarbon gas): Reduces solubility
Blending with light crudes: Dilution destabilizes asphaltenes
Acid stimulation: pH changes affect polar interactions

Modeling Approaches in NeqSim

1. Empirical Screening (De Boer)

Fast, conservative screening based on field correlations. See De Boer Screening.

Advantages:

Quick calculation (milliseconds)
No fluid characterization needed
Conservative for screening purposes

Limitations:

No onset pressure prediction
No compositional effects
Cannot model remediation strategies

2. Thermodynamic Modeling (CPA EOS)

Rigorous equation of state approach. See CPA Calculations.

Advantages:

Predicts onset pressure/temperature
Captures compositional effects
Can model inhibitor effectiveness
Uses PhaseType.ASPHALTENE for accurate phase identification

Limitations:

Requires fluid characterization
More computationally intensive
Needs tuning to experimental data

PhaseType.ASPHALTENE

NeqSim uses a dedicated PhaseType.ASPHALTENE enum value to distinguish precipitated asphaltenes from other solid phases (wax, hydrate). This enables:

Accurate phase identification in multi-phase flash calculations
Correct physical property calculations using asphaltene-specific correlations
Proper phase ordering (asphaltene as heaviest phase)
Easy API access via fluid.hasPhaseType("asphaltene")

import neqsim.thermo.phase.PhaseType;

// Check for asphaltene precipitation
if (fluid.hasPhaseType(PhaseType.ASPHALTENE)) {
    PhaseInterface asphaltene = fluid.getPhaseOfType("asphaltene");
    
    // Access asphaltene phase properties
    double density = asphaltene.getDensity("kg/m3");        // ~1150 kg/m³
    double Cp = asphaltene.getCp("kJ/kgK");                 // ~0.9 kJ/kgK
    double viscosity = asphaltene.getViscosity("Pa*s");     // ~10,000 Pa·s
    double thermalCond = asphaltene.getThermalConductivity("W/mK"); // ~0.20 W/mK
    double soundSpeed = asphaltene.getSoundSpeed("m/s");    // ~1745 m/s
}

3. Pedersen Classical Cubic EOS Approach

A simpler approach using classical cubic equations of state (SRK/PR) without association terms, developed by K.S. Pedersen and presented at GOTECH Dubai 2025.

Reference: Pedersen, K.S. (2025). “The Mechanisms Behind Asphaltene Precipitation – Successfully Handled by a Classical Cubic Equation of State.” SPE-224534-MS, GOTECH, Dubai.

Theoretical Background

The key insight from Pedersen’s work is that asphaltene precipitation can be successfully modeled as a liquid-liquid phase split using classical cubic equations of state. The approach treats asphaltene as a heavy pseudo-component characterized using the same correlations as C7+ fractions.

Critical Property Correlations:

\[T_c = a_0 + a_1 \ln(M) + a_2 M + \frac{a_3}{M}\] \[\ln(P_c) = b_0 + b_1 \rho^{0.25} + \frac{b_2}{M} + \frac{b_3}{M^2}\] \[\omega = c_0 + c_1 \ln(M) + c_2 \rho + c_3 M\]

Where:

$M$ = molecular weight (g/mol)
$\rho$ = density (g/cm³)
$T_c$ = critical temperature (K)
$P_c$ = critical pressure (bar)
$\omega$ = acentric factor

Basic Usage

import neqsim.thermo.characterization.PedersenAsphalteneCharacterization;
import neqsim.thermo.system.SystemSrkEos;
import neqsim.thermodynamicoperations.ThermodynamicOperations;

// Create SRK system (classical cubic EOS)
SystemSrkEos fluid = new SystemSrkEos(373.15, 200.0);
fluid.addComponent("methane", 0.40);
fluid.addComponent("n-heptane", 0.45);
fluid.addComponent("nC20", 0.10);

// Create Pedersen asphaltene characterization
PedersenAsphalteneCharacterization asphChar = new PedersenAsphalteneCharacterization();
asphChar.setAsphalteneMW(750.0);        // Molecular weight (g/mol)
asphChar.setAsphalteneDensity(1.10);    // Density (g/cm³)
asphChar.addAsphalteneToSystem(fluid, 0.05);  // Add 0.05 mol

// IMPORTANT: Set mixing rule AFTER adding all components
fluid.setMixingRule("classic");
fluid.init(0);

// Print characterization results
System.out.println(asphChar.toString());

Complete Oil Characterization with TBP Fractions

For realistic oil systems, combine asphaltene characterization with TBP (True Boiling Point) fraction characterization:

import neqsim.thermo.characterization.PedersenAsphalteneCharacterization;
import neqsim.thermo.system.SystemSrkEos;
import neqsim.thermodynamicoperations.ThermodynamicOperations;

// Create system and set Pedersen TBP model
SystemInterface oil = new SystemSrkEos(373.15, 200.0);
oil.getCharacterization().setTBPModel("PedersenSRK");

// Add light components (defined compounds)
oil.addComponent("nitrogen", 0.005);
oil.addComponent("CO2", 0.02);
oil.addComponent("methane", 0.35);
oil.addComponent("ethane", 0.08);
oil.addComponent("propane", 0.05);
oil.addComponent("i-butane", 0.01);
oil.addComponent("n-butane", 0.02);
oil.addComponent("i-pentane", 0.015);
oil.addComponent("n-pentane", 0.015);
oil.addComponent("n-hexane", 0.02);

// Add C7+ fractions as TBP pseudo-components
// Parameters: name, moles, MW (kg/mol), density (g/cm³)
oil.addTBPfraction("C7", 0.10, 96.0 / 1000.0, 0.738);
oil.addTBPfraction("C8", 0.08, 107.0 / 1000.0, 0.765);
oil.addTBPfraction("C9", 0.06, 121.0 / 1000.0, 0.781);
oil.addTBPfraction("C10", 0.04, 134.0 / 1000.0, 0.792);
oil.addTBPfraction("C11-C15", 0.06, 180.0 / 1000.0, 0.825);
oil.addTBPfraction("C16-C20", 0.03, 260.0 / 1000.0, 0.865);
oil.addTBPfraction("C21+", 0.02, 450.0 / 1000.0, 0.920);

// Add asphaltene using Pedersen characterization
PedersenAsphalteneCharacterization asphChar = new PedersenAsphalteneCharacterization();
asphChar.setAsphalteneMW(850.0);
asphChar.setAsphalteneDensity(1.12);
asphChar.addAsphalteneToSystem(oil, 0.015);

// Set mixing rule and initialize
oil.setMixingRule("classic");
oil.init(0);

// Perform flash calculation
ThermodynamicOperations ops = new ThermodynamicOperations(oil);
ops.TPflash();
oil.prettyPrint();

Tuning to Experimental Data

The class provides tuning multipliers for fitting to experimental onset pressure data:

// Create characterization
PedersenAsphalteneCharacterization asphChar = new PedersenAsphalteneCharacterization();
asphChar.setAsphalteneMW(750.0);
asphChar.setAsphalteneDensity(1.10);

// Apply tuning multipliers to match experimental onset pressure
asphChar.setTcMultiplier(1.03);     // Increase Tc by 3%
asphChar.setPcMultiplier(0.97);     // Decrease Pc by 3%
asphChar.setOmegaMultiplier(1.05);  // Increase ω by 5%

// Or use convenience method for all at once
asphChar.setTuningParameters(1.03, 0.97, 1.05);

// Characterize and add to system
asphChar.characterize();
asphChar.addAsphalteneToSystem(fluid, 0.05);

// Reset to defaults if needed
asphChar.resetTuningParameters();

Tuning Guidelines:

Parameter	Effect on Onset Pressure	Typical Range
`tcMultiplier`	Higher Tc → lower onset pressure	0.95 - 1.10
`pcMultiplier`	Higher Pc → higher onset pressure	0.85 - 1.15
`omegaMultiplier`	Higher ω → complex effects	0.90 - 1.20

Distributed Asphaltene Pseudo-Components

For heavy oils, represent asphaltene as multiple pseudo-components with distributed MW:

// Add distributed asphaltene (3 pseudo-components)
asphChar.setAsphalteneMW(1000.0);   // Average MW
asphChar.setAsphalteneDensity(1.15);
asphChar.addDistributedAsphaltene(heavyOil, 0.08, 3);

// This creates 3 components: Asph_1_PC, Asph_2_PC, Asph_3_PC
// with MW ranging from 0.5x to 2x the average MW

Typical Property Ranges

Based on the Pedersen correlations, asphaltene components show these typical properties:

Property	Light Asphaltene (MW=500)	Medium (MW=750)	Heavy (MW=1500)
Tc	950-1000 K	990-1010 K	1040-1060 K
Pc	17-19 bar	15-17 bar	14-16 bar
ω	0.9-1.0	0.9-1.0	1.2-1.4
Tb	700-800 K	800-850 K	900-950 K

Advantages:

Uses standard cubic EOS (simpler than CPA)
Consistent with Pedersen C7+ characterization already in NeqSim
Models precipitation as liquid-liquid split
Tunable to experimental data
Suitable for routine engineering calculations

Limitations:

Does not capture association effects explicitly
May require different tuning than CPA models
L-L split detection depends on flash algorithm convergence

4. Combined Approach

Use De Boer for initial screening, then CPA or Pedersen method for detailed analysis of flagged cases. See Method Comparison.

Best Practices

For Field Development

Early Screening: Use De Boer on all fluids
Focused Analysis: Apply CPA to flagged samples
Lab Validation: Confirm predictions with HPM/AOP tests
Parameter Tuning: Use AsphalteneOnsetFitting to match measured onset data
Monitoring Strategy: Plan for real-time detection

For Process Design

Operating Envelope: Generate precipitation PT curves
Injection Studies: Model CO₂/gas injection effects
Blending Optimization: Predict compatibility issues
Inhibitor Screening: Evaluate chemical effectiveness

Recommended Workflow

┌─────────────────────────────────────────────────────────────────┐
│                    ASPHALTENE ANALYSIS WORKFLOW                 │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 1: DE BOER SCREENING                                      │
│  • Fast empirical screening                                     │
│  • Input: P_res, P_bub, density                                 │
│  • Output: Risk category (NO/SLIGHT/MODERATE/SEVERE)            │
└─────────────────────────────────────────────────────────────────┘
                              │
              ┌───────────────┴───────────────┐
              │                               │
              ▼                               ▼
     ┌──────────────┐              ┌──────────────────────┐
     │  LOW RISK    │              │  ELEVATED RISK       │
     │  • Monitor   │              │  • Proceed to CPA    │
     │  • Document  │              │  analysis            │
     └──────────────┘              └──────────────────────┘
                                              │
                                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 2: CPA THERMODYNAMIC ANALYSIS                             │
│  • Create fluid with asphaltene component                       │
│  • Calculate onset pressure/temperature                         │
│  • Generate precipitation envelope                              │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 3: LABORATORY VALIDATION                                  │
│  • Measure AOP at multiple temperatures                         │
│  • SARA analysis for composition                                │
│  • HPM microscopy for onset detection                           │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 4: PARAMETER TUNING (AsphalteneOnsetFitting)              │
│  • Fit CPA parameters to lab data                               │
│  • Validate predictions at other conditions                     │
│  • Document fitted parameters for field use                     │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│  Step 5: FIELD APPLICATION                                      │
│  • Predict onset during depletion/injection                     │
│  • Design mitigation strategies                                 │
│  • Establish monitoring program                                 │
└─────────────────────────────────────────────────────────────────┘

References

De Boer, R.B., et al. (1995). “Screening of Crude Oils for Asphalt Precipitation: Theory, Practice, and the Selection of Inhibitors.” SPE Production & Facilities. SPE-24987-PA
Mullins, O.C. (2010). “The Modified Yen Model.” Energy & Fuels, 24(4), 2179-2207.
Leontaritis, K.J., and Mansoori, G.A. (1988). “Asphaltene Deposition: A Survey of Field Experiences and Research Approaches.” Journal of Petroleum Science and Engineering.
Victorov, A.I., and Firoozabadi, A. (1996). “Thermodynamic Micellization Model of Asphaltene Precipitation from Petroleum Fluids.” AIChE Journal.
Kontogeorgis, G.M., and Folas, G.K. (2010). “Thermodynamic Models for Industrial Applications.” Wiley.
Li, Z., and Firoozabadi, A. (2010). “Modeling Asphaltene Precipitation by n-Alkanes from Heavy Oils and Bitumens Using Cubic-Plus-Association Equation of State.” Energy & Fuels, 24, 1106-1113.
Vargas, F.M., et al. (2009). “Development of a General Method for Modeling Asphaltene Stability.” Energy & Fuels, 23, 1140-1146.
Pedersen, K.S. (2025). “The Mechanisms Behind Asphaltene Precipitation – Successfully Handled by a Classical Cubic Equation of State.” SPE-224534-MS, GOTECH, Dubai.
Pedersen, K.S., Christensen, P.L. (2007). “Phase Behavior of Petroleum Reservoir Fluids.” CRC Press.
Pedersen, K.S., Fredenslund, A., Thomassen, P. (1989). “Properties of Oils and Natural Gases.” Gulf Publishing.