Scale Prediction API Reference

NeqSim provides three complementary approaches for mineral scale prediction:

  1. Rigorous EOS-based (CheckScalePotential) — Uses Electrolyte CPA or Pitzer activity coefficients from a full thermodynamic flash. Best accuracy, requires complete fluid definition.
  2. High-salinity standalone (PitzerScaleActivityModel + MultiMineralScaleEquilibrium) — Uses published binary Pitzer parameters and coupled shared-ion precipitation for NaCl-dominated brines.
  3. Empirical standalone (ScalePredictionCalculator) — Uses Davies equation with ion pairing corrections. Fast dilute-brine screening, needs only water analysis data.

Both approaches share improved Ksp correlations, pressure corrections, and T-dependent parameters.

Architecture Overview

┌─────────────────────────────────────────────────────┐
│               Scale Prediction in NeqSim            │
├──────────────────────┬──────────────────────────────┤
│   Rigorous (EOS)     │     Empirical (Standalone)   │
│                      │                              │
│  SystemElectrolyte-  │  ScalePredictionCalculator   │
│  CPAstatoil          │  ├── Davies equation         │
│  ├── TPflash()       │  ├── Ion pairing (6 pairs)   │
│  ├── ion activities  │  ├── T-dep Ksp (Monnin etc.) │
│  └── CheckScale-     │  ├── Pressure correction ΔV° │
│      Potential       │  └── Debye-Hückel A(T)       │
│                      │                              │
│  SystemPitzer        │  Helper Classes              │
│  ├── PhasePitzer     │  ├── ScaleMassCalculator     │
│  ├── Pitzer params   │  ├── BariteCelestiteSolid-   │
│  └── T-dep β₀,β₁,Cᶲ │  │   Solution                │
│                      │  ├── WaterCompatibility-      │
│                      │  │   Screener                 │
│                      │  └── FlowlineScaleProfile    │
└──────────────────────┴──────────────────────────────┘

1. ScalePredictionCalculator (Empirical)

Package: neqsim.pvtsimulation.flowassurance

Fast standalone calculator for 5 mineral scales using water analysis input.

Setup and Usage

ScalePredictionCalculator calc = new ScalePredictionCalculator();
calc.setCalciumConcentration(400.0);     // mg/L
calc.setBariumConcentration(10.0);       // mg/L
calc.setStrontiumConcentration(5.0);     // mg/L
calc.setIronConcentration(2.0);          // mg/L
calc.setMagnesiumConcentration(1300.0);  // mg/L (for ion pairing)
calc.setSodiumConcentration(11000.0);    // mg/L (for ion pairing)
calc.setBicarbonateConcentration(150.0); // mg/L
calc.setSulphateConcentration(10.0);     // mg/L
calc.setTotalDissolvedSolids(35000.0);   // mg/L
calc.setTemperatureCelsius(80.0);        // °C
calc.setPressureBara(100.0);             // bara
calc.setCO2PartialPressure(2.0);         // bar
calc.enableAutoPH();                     // estimate pH from CO2
calc.calculate();

double siCaCO3 = calc.getCaCO3SaturationIndex();
double siBaSO4 = calc.getBaSO4SaturationIndex();
boolean risk = calc.hasScalingRisk();
String json = calc.toJson();

Python Usage

from neqsim import jneqsim
ScalePredictionCalculator = jneqsim.pvtsimulation.flowassurance.ScalePredictionCalculator

calc = ScalePredictionCalculator()
calc.setCalciumConcentration(400.0)
calc.setBariumConcentration(10.0)
calc.setStrontiumConcentration(5.0)
calc.setBicarbonateConcentration(150.0)
calc.setSulphateConcentration(10.0)
calc.setTotalDissolvedSolids(35000.0)
calc.setTemperatureCelsius(80.0)
calc.setPressureBara(100.0)
calc.setCO2PartialPressure(2.0)
calc.enableAutoPH()
calc.calculate()

print(f"CaCO3 SI = {calc.getCaCO3SaturationIndex():.3f}")
print(f"BaSO4 SI = {calc.getBaSO4SaturationIndex():.3f}")

Ksp Correlations

Scale Source Formula
CaCO3 (calcite) Plummer & Busenberg (1982) $\log_{10} K_{sp} = -171.9065 - 0.077993T + 2839.319/T + 71.595\log_{10} T$
BaSO4 (barite) Monnin (1999) $\log_{10} K_{sp} = 136.035 - 7680.41/T - 48.595\log_{10} T$
SrSO4 (celestite) Monnin (1999) $\log_{10} K_{sp} = 155.889 - 7862.38/T - 56.625\log_{10} T$
CaSO4 (anhydrite) Blount & Dickson (1973) $\log_{10} K_{sp} = 85.685 - 4279.82/T - 30.219\log_{10} T$
FeCO3 (siderite) Greenberg & Tomson (1992) $\log_{10} K_{sp} = -59.3498 - 0.041377T + 2.1963/T + 24.5724\log_{10} T + 2.518 \times 10^{-5} T^2$

Pressure Correction

All Ksp values are corrected for pressure using molar volume change:

\[\ln\frac{K_{sp}(P)}{K_{sp}(P_0)} = -\frac{\Delta V^\circ (P - P_0)}{RT}\]

Where $\Delta V^\circ$ (cm³/mol) values are: CaCO3 = -58.4, BaSO4 = -46.4, SrSO4 = -47.0, CaSO4 = -52.4, FeCO3 = -52.9.

Ion Pairing

Six aqueous ion pairs are modelled with association constants (25°C):

Ion Pair $\log_{10} K_{assoc}$ Effect
CaSO4⁰ 2.31 Reduces free Ca²⁺ and SO4²⁻
MgSO4⁰ 2.37 Reduces free Mg²⁺ and SO4²⁻
NaSO4⁻ 0.70 Reduces free Na⁺ and SO4²⁻
CaHCO3⁺ 1.11 Reduces free Ca²⁺ and HCO3⁻
MgHCO3⁺ 1.16 Reduces free Mg²⁺ and HCO3⁻
CaCO3⁰ 3.22 Reduces free Ca²⁺ and CO3²⁻

Temperature dependence uses van’t Hoff correction with $\Delta H / R$ coefficients.

2. CheckScalePotential (EOS-Based)

Package: neqsim.thermodynamicoperations.flashops.saturationops

Uses full thermodynamic model (Electrolyte CPA or Pitzer) to compute activity-based ion activity products.

Usage via ThermodynamicOperations

// Create electrolyte system
SystemInterface brine = new SystemElectrolyteCPAstatoil(273.15 + 80.0, 100.0);
brine.addComponent("CO2", 0.01);
brine.addComponent("water", 0.90);
brine.addComponent("Na+", 0.03);
brine.addComponent("Cl-", 0.035);
brine.addComponent("Ca++", 0.005);
brine.addComponent("Ba++", 0.001);
brine.addComponent("SO4--", 0.002);
brine.addComponent("HCO3-", 0.005);
brine.chemicalReactionInit();
brine.createDatabase(true);
brine.setMixingRule(10);
brine.setMultiPhaseCheck(true);

ThermodynamicOperations ops = new ThermodynamicOperations(brine);
ops.TPflash();
brine.initProperties();

// Check scale potential
int aqPhase = brine.getPhaseNumberOfPhase("aqueous");
ops.checkScalePotential(aqPhase);
String[][] table = ops.getResultTable();

// table rows: [saltName, scalePotentialFactor, ""]
// scalePotentialFactor > 1.0 means supersaturated
for (int i = 1; i < table.length; i++) {
    System.out.println(table[i][0] + " : " + table[i][1]);
}

Python Usage

from neqsim import jneqsim
SystemElectrolyteCPAstatoil = jneqsim.thermo.system.SystemElectrolyteCPAstatoil
ThermodynamicOperations = jneqsim.thermodynamicoperations.ThermodynamicOperations

brine = SystemElectrolyteCPAstatoil(273.15 + 80.0, 100.0)
brine.addComponent("CO2", 0.01)
brine.addComponent("water", 0.90)
brine.addComponent("Na+", 0.03)
brine.addComponent("Cl-", 0.035)
brine.addComponent("Ca++", 0.005)
brine.addComponent("SO4--", 0.002)
brine.addComponent("HCO3-", 0.005)
brine.chemicalReactionInit()
brine.createDatabase(True)
brine.setMixingRule(10)
brine.setMultiPhaseCheck(True)

ops = ThermodynamicOperations(brine)
ops.TPflash()
brine.initProperties()

aq_phase = brine.getPhaseNumberOfPhase("aqueous")
ops.checkScalePotential(aq_phase)
table = ops.getResultTable()

for i in range(1, len(table)):
    print(f"{table[i][0]:20s} SR = {table[i][1]}")

Key Features

3. Pitzer Activity Coefficient Model

Package: neqsim.thermo.phase.PhasePitzer, neqsim.thermo.component.ComponentGePitzer

Enhanced Pitzer model with temperature-dependent parameters loaded from database.

Temperature-Dependent Debye-Hückel

The Debye-Hückel osmotic coefficient $A_\phi$ is computed from:

\[A_\phi = \frac{1.4006 \times 10^6 \sqrt{\rho_w}}{(\varepsilon \cdot T)^{3/2}}\]

Where $\rho_w$ uses Kell (1975) water density and $\varepsilon$ uses Archer & Wang (1990) dielectric constant.

Temperature-Dependent Binary Parameters

Parameters follow the form:

\[\beta(T) = \beta_{25} + T_1 \left(\frac{1}{T} - \frac{1}{298.15}\right) + T_2 \ln\left(\frac{T}{298.15}\right)\]

The Pitzer parameter database (PitzerParameters.csv) currently contains 30 cation-anion rows. Of these, 23 non-estimated rows have populated binary parameters and are covered by regression tests for database loading plus finite mean ionic activity and osmotic coefficients. The covered ions include Na, K, Ca, Mg, Ba, Sr, Fe and H with Cl, SO4, HCO3, CO3 and OH. NaCl is additionally benchmarked against Robinson & Stokes / Pitzer 25 C mean ionic activity and osmotic-coefficient data.

Using the Pitzer Model

SystemInterface pitzer = new SystemPitzer(273.15 + 80.0, 100.0);
pitzer.addComponent("water", 0.90);
pitzer.addComponent("Na+", 0.03);
pitzer.addComponent("Cl-", 0.035);
pitzer.addComponent("Ca++", 0.005);
pitzer.addComponent("SO4--", 0.002);
pitzer.setMixingRule("classic");

ThermodynamicOperations ops = new ThermodynamicOperations(pitzer);
ops.TPflash();
pitzer.initProperties();

4. WaterCompatibilityScreener

Package: neqsim.pvtsimulation.flowassurance

Screens formation water and injection water for compatibility by evaluating scale risk at every mixing ratio.

Usage

WaterCompatibilityScreener screener = new WaterCompatibilityScreener();

// Formation water (high Ba, low SO4)
screener.setFormationWater(
    400,  // Ca mg/L
    200,  // Ba mg/L
    50,   // Sr mg/L
    2,    // Fe mg/L
    150,  // HCO3 mg/L
    10,   // SO4 mg/L
    50000,// TDS mg/L
    90,   // T °C
    200,  // P bara
    3.0,  // CO2 pp bar
    6.2   // pH
);

// Injection water (seawater: low Ba, high SO4)
screener.setInjectionWater(
    400, 0, 5, 0, 140, 2700, 35000, 15, 200, 0.3, 8.1
);

screener.setMixingRatios(new double[]{0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100});
screener.calculate();

System.out.println("Worst case: " + screener.getWorstCaseScale()
    + " at " + screener.getWorstCaseRatio() + "% IW, SI = "
    + screener.getWorstCaseSI());

5. FlowlineScaleProfile

Package: neqsim.pvtsimulation.flowassurance

Computes scale saturation indices along a pipeline with linearly varying T and P.

Usage

FlowlineScaleProfile profile = new FlowlineScaleProfile();
profile.setWaterChemistry(400, 10, 5, 0, 150, 10, 40000, 2.0, 6.5);
profile.setInletConditions(90.0, 250.0);   // wellhead: 90°C, 250 bara
profile.setOutletConditions(15.0, 50.0);   // host: 15°C, 50 bara
profile.setNumberOfSegments(50);
profile.calculate();

// Find worst location for each scale
System.out.println("Max CaCO3 SI: " + profile.getMaxSI("CaCO3"));
System.out.println("Max BaSO4 SI: " + profile.getMaxSI("BaSO4"));

String json = profile.toJson();

6. ScaleMassCalculator

Package: neqsim.pvtsimulation.flowassurance

Estimates the mass of scale precipitated per litre of produced water when SI > 0.

Usage

// First compute SIs
ScalePredictionCalculator calc = new ScalePredictionCalculator();
// ... set concentrations, T, P ...
calc.calculate();

// Then compute mass
ScaleMassCalculator massCal = new ScaleMassCalculator(calc);
massCal.setWaterVolume(1000.0);  // litres

// Individual mass calculations (mol/L inputs)
double caMolL = 400.0 / 40078.0;    // 400 mg/L Ca / MW
double co3MolL = 150.0 / 61017.0;   // approximate
double mass = massCal.calcCaCO3Mass(caMolL, co3MolL, calc.getCaCO3SaturationIndex());

7. BariteCelestiteSolidSolution

Package: neqsim.pvtsimulation.flowassurance

Regular solution model for (Ba,Sr)SO4 co-precipitation using Margules 1-parameter model.

At equilibrium:

\[x_{Ba} \gamma_{Ba}^{(s)} K_{sp,BaSO_4} = a_{Ba^{2+}} \cdot a_{SO_4^{2-}}\] \[x_{Sr} \gamma_{Sr}^{(s)} K_{sp,SrSO_4} = a_{Sr^{2+}} \cdot a_{SO_4^{2-}}\]

Where solid activity coefficients: $\ln \gamma_i^{(s)} = W (1 - x_i)^2$

Usage

BariteCelestiteSolidSolution ss = new BariteCelestiteSolidSolution();
ss.setAqueousActivities(0.001, 0.005, 0.01);  // aBa, aSr, aSO4
ss.setEndMemberKsp(1.08e-10, 3.44e-7);        // Ksp_BaSO4, Ksp_SrSO4
ss.setMargules(2.3);                           // W/(RT) parameter
ss.calculate();

System.out.println("BaSO4 in solid: " + (ss.getBaSO4MoleFraction() * 100) + "%");
System.out.println("Total SI: " + ss.getTotalSaturationIndex());

Comparison of thermodynamic routes

Feature Davies standalone Pitzer binary + coupled solids Electrolyte EOS / full Pitzer
Activity coefficients Charge-based Davies Ion-specific binary Pitzer Full fluid speciation/model
Ion pairing 6 explicit pairs 6 pairs plus trace-ion activity mapping Thermodynamic phase model
Input Water analysis Water analysis + salinity/molality Full fluid definition
Best for Dilute screening NaCl-dominated oilfield-brine screening Detailed design, complex brines
Ionic strength Approximately <=0.5 mol/kg Validated against NaCl data to 6 mol/kg Model/database dependent

See High-Salinity Mineral Scale and Production-Chemical Validation for published-data errors, treatment scenarios and limitations.