Neqsim Production Chemistry

Production Chemistry with NeqSim

neqsim.process.chemistry is an open, standards-traceable production-chemistry stack: chemical inventory, compatibility rules, per-threat dose-response models, a flowsheet-visible injection point, and an explainable root-cause analyser. Every model exposes getStandardsApplied() and toMap() / toJson() so results drop straight into results.json.

When to Use This Skill

• Which chemical, which chemistry family, and what dose for a given threat
• Compatibility of a chemical cocktail with itself, with the produced water, and with the material
• Minimum inhibitor concentration (MIC), residual SI after treatment, inhibited corrosion rate
• H2S scavenger demand, capacity and breakthrough time
• Hydrate inhibitor (MEG/MeOH) injection rate for a target subcooling; KHI induction time
• Demulsifier dose vs oil-in-water spec, dosing lag, monthly compliance
• How a pH adjuster / scavenger shifts the brine chemistry and the scaling tendency
• Chemical incident root cause (deposit, emulsion, corrosion, pH excursion, H2S breakthrough)
• Uncertainty (P10/P50/P90) on any of the above

What This Skill Is NOT

• Thermodynamics of the threat — hydrate curve, WAT, asphaltene onset, SI from an ion table: load neqsim-flow-assurance, neqsim-wax-calculations, neqsim-electrolyte-systems. This skill consumes those results and answers what chemical, how much, and what happens.
• Rigorous reaction kinetics — load neqsim-reaction-engineering.
• Equipment root cause from historian data — load neqsim-root-cause-analysis. RootCauseAnalyser here is the chemical RCA (deposit / chemistry symptoms).

Applicable Standards

Load neqsim-standards-lookup and emit standards_applied in results.json.

1. Chemical Inventory — ProductionChemical

Every chemical is described once and reused by the compatibility, scenario and RCA models.

import neqsim.process.chemistry.ProductionChemical;

// Factory helpers set typical defaults for the family
ProductionChemical si = ProductionChemical.scaleInhibitor("SI-A", 20.0);      // 20 ppm
ProductionChemical ci = ProductionChemical.corrosionInhibitor("CI-B", 50.0);  // 50 ppm
ProductionChemical meg = ProductionChemical.thermodynamicHydrateInhibitor("MEG", 0.0);
ProductionChemical scav = ProductionChemical.h2sScavenger("Triazine", 100.0);

// Tune before evaluation
si.setActiveIngredient("phosphonate");
si.setActiveWtPct(35.0);
si.setIonicNature(ProductionChemical.IonicNature.ANIONIC);
si.setTemperatureRangeC(4.0, 150.0);
si.setPH(3.5);

boolean ok = si.isStableAt(120.0);

ChemicalType: SCALE_INHIBITOR, CORROSION_INHIBITOR, HYDRATE_INHIBITOR_THERMODYNAMIC, HYDRATE_INHIBITOR_LDHI, DEMULSIFIER, WAX_INHIBITOR, ASPHALTENE_INHIBITOR, BIOCIDE, OXYGEN_SCAVENGER, H2S_SCAVENGER, ANTIFOAM, DRAG_REDUCER, PH_ADJUSTER, ACID, CHELANT, OTHER.

IonicNature: CATIONIC, ANIONIC, NON_IONIC, AMPHOTERIC, UNKNOWN — this is what drives most incompatibility findings (anionic SI + cationic CI → precipitation).

2. Compatibility — ChemicalCompatibilityAssessor

Rule base is CSV-driven (/data/chemical_compatibility_rules.csv, loaded by ChemicalInteractionRule.loadDefaultRules()), combined with the operating conditions and water chemistry you supply.

import neqsim.process.chemistry.ChemicalCompatibilityAssessor;

ChemicalCompatibilityAssessor assessor = new ChemicalCompatibilityAssessor();
assessor.addChemical(si);
assessor.addChemical(ci);
assessor.addChemical(scav);
assessor.setTemperatureCelsius(85.0);
assessor.setPressureBara(90.0);
assessor.setCalciumMgL(1200.0);
assessor.setIronMgL(5.0);
assessor.setBicarbonateMgL(300.0);
assessor.setMaterial("carbon steel");
assessor.evaluate();

ChemicalCompatibilityAssessor.Verdict verdict = assessor.getVerdict(); // COMPATIBLE / CAUTION / INCOMPATIBLE
List<Map<String, Object>> issues = assessor.getIssues();               // mechanism + mitigation per issue
Map<String, Map<String, String>> matrix = assessor.getInteractionMatrix();
Map<String, Boolean> thermal = assessor.getThermalStability();         // per chemical at T
String json = assessor.toJson();

Always run this before recommending a dose. A dose that is right in isolation can be destroyed by the neighbouring injection point.

ChemicalCompatibilityAssessor.fromStream(stream) pre-fills T, P and the water chemistry from a live NeqSim stream — see §8.

3. Dose-Response per Threat

All performance models follow the same pattern: setters → evaluate() → getters + getWarnings() + getStandardsApplied() + toMap().

3.1 Scale inhibitor — ScaleInhibitorPerformance

import neqsim.process.chemistry.scale.ScaleInhibitorPerformance;

ScaleInhibitorPerformance sip = new ScaleInhibitorPerformance();
sip.setScaleType(ScaleInhibitorPerformance.ScaleType.BASO4);      // CACO3 BASO4 SRSO4 CASO4 FECO3
sip.setInhibitorChemistry(ScaleInhibitorPerformance.InhibitorChemistry.PHOSPHONATE);
sip.setTemperatureCelsius(95.0);
sip.setSaturationRatio(12.0);      // from the SI/SR calculation (see neqsim-flow-assurance)
sip.setTdsMgL(90000.0);
sip.setCalciumMgL(1200.0);
sip.setAvailableDoseMgL(15.0);     // what the squeeze/umbilical actually delivers
sip.evaluate();

double mic = sip.getMinimumInhibitorConcentrationMgL();
double recommended = sip.getRecommendedDoseMgL();
double eff = sip.getEfficiency();
boolean adequate = sip.isAdequate();   // available dose >= MIC

InhibitorChemistry: PHOSPHONATE, POLYMALEATE, POLYACRYLATE, PHOSPHATE_ESTER, VINYL_SULPHONATE.

3.2 Whole-brine scale control — ScaleControlAssessor

Couples a ScalePredictionCalculator with one inhibitor model per mineral and reports the residual SI and a kinetic risk index.

import neqsim.pvtsimulation.flowassurance.ScalePredictionCalculator;
import neqsim.process.chemistry.scale.ScaleControlAssessor;

ScalePredictionCalculator pred = new ScalePredictionCalculator();
pred.setTemperatureCelsius(95.0);
pred.setPressureBara(150.0);
pred.setCalciumConcentration(1200.0);
pred.setBariumConcentration(250.0);
pred.setSulphateConcentration(1800.0);
pred.setBicarbonateConcentration(300.0);
pred.setTotalDissolvedSolids(90000.0);
pred.setPH(6.2);

ScaleControlAssessor control = new ScaleControlAssessor(pred);
control.addInhibitor(ScaleInhibitorPerformance.ScaleType.BASO4, sip);
control.evaluate();

double residual = control.getResidualSI(ScaleInhibitorPerformance.ScaleType.BASO4);
double worst = control.getWorstResidualSI();
boolean controlled = control.isControlled(0.0);          // residual SI <= threshold
boolean kinetic = control.isKineticallyControlled(1.0);

3.3 Corrosion inhibitor — CorrosionInhibitorPerformance

Langmuir adsorption with a van 't Hoff temperature term, shear-stripping, and penalties for O2, organic acid and H2S.

import neqsim.process.chemistry.corrosion.CorrosionInhibitorPerformance;

CorrosionInhibitorPerformance cip = new CorrosionInhibitorPerformance();
cip.setChemistry(CorrosionInhibitorPerformance.InhibitorChemistry.IMIDAZOLINE);
cip.setDoseMgL(50.0);
cip.setBaseCorrosionRateMmYr(2.4);      // from NORSOK M-506 / de Waard-Milliams
cip.setTemperatureCelsius(70.0);
cip.setWallShearStressPa(35.0);
cip.setOrganicAcidPpm(200.0);
cip.setH2SPartialPressureBar(0.02);
cip.setOxygenPpb(10.0);
cip.evaluate();

double efficiency = cip.getEfficiency();                       // 0..1
double inhibited = cip.getInhibitedCorrosionRateMmYr();
double minDose = cip.getMinimumEffectiveDoseMgL();
Map<String, String> warn = cip.getWarnings();                  // e.g. O2 > 50 ppb kills film

InhibitorChemistry: IMIDAZOLINE, QUATERNARY_AMMONIUM, AMIDO_AMINE, PHOSPHATE_ESTER, PYRIDINE, MERCAPTAN. setFromDeWaardMilliams(baseline) takes the uninhibited rate directly from a DeWaardMilliamsCorrosion object.

O2 ingress dominates. Above ~50 ppb the model warns that film-forming CI is undermined — the answer is an oxygen scavenger or eliminating the ingress, not more CI.

3.4 Thermodynamic hydrate inhibitor (MEG / MeOH) — ThermodynamicHydrateInhibitorPerformance

import neqsim.process.chemistry.hydrate.ThermodynamicHydrateInhibitorPerformance;

ThermodynamicHydrateInhibitorPerformance thi =
    new ThermodynamicHydrateInhibitorPerformance();
thi.setInhibitorChemistry(ThermodynamicHydrateInhibitorPerformance.InhibitorChemistry.MEG);
thi.setTargetSubcoolingC(8.0);          // hydrate T minus minimum operating T
thi.setWaterFlowKgPerHour(1500.0);      // free + condensed water
thi.setInhibitorPurityWtPct(90.0);      // rich/lean MEG purity
thi.setLeanInhibitorWtPctInWater(0.0);  // already present in the water
thi.evaluate();

double wtPct = thi.getRequiredInhibitorWtPctInWater();
double kgHr = thi.getRequiredInjectionKgPerHour();

InhibitorChemistry: METHANOL, MEG, DEG, TEG (Hammerschmidt K built in).

Hammerschmidt is a screening correlation. For the design number, compute the inhibited hydrate curve with SystemSrkCPAstatoil + hydrateFormationTemperature() (neqsim-flow-assurance) and use this model for the injection-rate bookkeeping and lean/rich balance.

3.5 Kinetic hydrate inhibitor (KHI) — KineticHydrateInhibitorPerformance

import neqsim.process.chemistry.hydrate.KineticHydrateInhibitorPerformance;

KineticHydrateInhibitorPerformance khi = new KineticHydrateInhibitorPerformance();
khi.setSubcoolingC(6.0);
khi.setDoseWtPct(0.5);
khi.setTargetInductionTimeHours(24.0);
khi.evaluate();

double tInd = khi.getPredictedInductionTimeHours();
double required = khi.getRequiredDoseWtPct();

Calibrate with setCoefficients(a, b, c) against vendor rocking-cell data before quoting.

3.6 Wax and asphaltene inhibitors

import neqsim.process.chemistry.wax.WaxInhibitorPerformance;
import neqsim.process.chemistry.asphaltene.AsphalteneInhibitorPerformance;

WaxInhibitorPerformance wax = new WaxInhibitorPerformance();
wax.setInhibitorChemistry(WaxInhibitorPerformance.InhibitorChemistry.EVA);
wax.setBasePourPointC(24.0);
wax.setBaseWaxAppearanceTemperatureC(32.0);   // untreated WAT from neqsim-wax-calculations
wax.setDoseMgL(300.0);
wax.evaluate();
double ppd = wax.getPourPointDepressionC();
double treatedWat = wax.getInhibitedWaxAppearanceTemperatureC();

AsphalteneInhibitorPerformance asp = new AsphalteneInhibitorPerformance();
asp.setInhibitorChemistry(AsphalteneInhibitorPerformance.InhibitorChemistry.ALKYLPHENOL_RESIN);
asp.setBaseColloidalInstabilityIndex(1.2);
asp.setBaseAsphalteneOnsetPressureBara(280.0);
asp.setDoseMgL(150.0);
asp.evaluate();
boolean stable = asp.isStableAfterTreatment();

A wax inhibitor depresses the pour point far more than the WAT — do not sell a WAT shift the model does not give you.

3.7 H2S scavenger — H2SScavengerPerformance

import neqsim.process.chemistry.scavenger.H2SScavengerPerformance;

H2SScavengerPerformance scv = new H2SScavengerPerformance();
scv.setChemistry(H2SScavengerPerformance.ScavengerChemistry.MEA_TRIAZINE);
scv.setActiveWtPct(40.0);
scv.setScavengerInventoryKg(20000.0);
scv.setGasFlowMSm3PerDay(4.0);
scv.setH2SInletPpm(35.0);
scv.setH2STargetPpm(4.0);       // sales-gas spec
scv.setTemperatureCelsius(40.0);
scv.setPressureBara(70.0);
scv.evaluate();

double removeKgD = scv.getH2SToRemoveKgPerDay();
double demandKgD = scv.getScavengerDemandKgPerDay();
double capacity = scv.getCapacityKgH2SPerKgActive();
double breakthroughDays = scv.getBreakthroughDays();

ScavengerChemistry: MEA_TRIAZINE, MMA_TRIAZINE, IRON_CHELATE, IRON_SPONGE, ALDEHYDE. For a solid packed bed with an axial profile use neqsim.process.chemistry.scavenger.PackedBedScavengerReactor (1D plug flow, per-cell inventory depletion, breakthrough time). For a gas contactor unit operation inside a flowsheet, use neqsim.process.equipment.absorber.H2SScavenger.

Triazine overdose forms amorphous dithiazine deposits. If the symptom is a soft, sticky, sulphur-smelling deposit downstream of a scavenger skid, check ScaleRemediationAdvisor (it carries the amorphous scavenger-deposit case) and feed the symptom to RootCauseAnalyser — it has an explicit H2S_SCAVENGER_UNDER_CAPACITY / overdose candidate.

4. Injection Point as Flowsheet Equipment — InhibitorInjectionPoint

Makes chemical injection visible, snapshot-able and adjustable in a ProcessSystem.

import neqsim.process.chemistry.equipment.InhibitorInjectionPoint;

InhibitorInjectionPoint inj = new InhibitorInjectionPoint("CI injection", wellStream);
inj.setChemical(ci);
inj.setDoseInPpmOnWater(50.0);     // or setDoseInKgPerHour(...)
process.add(inj);
process.run();

double ppmInWater = inj.getActiveIngredientPpmInWater();
double kgPerHour = inj.getInjectionRateKgPerHour();

DoseMode: PPM (on water mass), PPM_TOTAL (on total fluid mass), KG_PER_HOUR.

It deliberately does not run an electrolyte flash on every call — it tracks the dose at the outlet and hands it to the dedicated chemistry models. Do not expect it to change the hydrate curve or pH by itself.

5. Produced Water — Demulsifier Dose vs Oil-in-Water

import neqsim.process.equipment.watertreatment.DemulsifierDoseResponseModel;
import neqsim.process.equipment.watertreatment.OilInWaterDoseOptimizer;

DemulsifierDoseResponseModel dr = new DemulsifierDoseResponseModel();
double rms = dr.calibrate(dosePpmArray, observedOiwArray, 250.0);  // untreated OiW mg/L
double oiw = dr.predictOilInWater(250.0, 12.0);                    // effective dose ppm

OilInWaterDoseOptimizer opt = new OilInWaterDoseOptimizer();
opt.setDoseResponseModel(dr);
opt.setDoseRange(2.0, 40.0, 0.5);
opt.setSafetyMarginMgL(3.0);
OilInWaterDoseOptimizer.DoseRecommendation rec = opt.recommendDose(250.0, 900.0, 18);
double setpoint = rec.getSetpointDosePpm();
boolean feasible = rec.isFeasible();

The optimiser accounts for dosing lag (ChemicalDoseLagModel — first-order chemical inventory after a setpoint change), analyser drift, and the OSPAR 30 mg/L monthly average via addMonthlySample(oiwMgL, volumeM3) — so a mid-month excursion is traded against the running average rather than the instantaneous reading. Overdosing is modelled (the response curve turns over past the optimum): more demulsifier is not monotonically better.

Emulsion viscosity for the hydraulics side: EmulsionViscosityCalculator (neqsim.pvtsimulation.flowassurance) has a demulsifierPresent / efficiency correction.

6. Treatment Effect on Brine Chemistry — ProductionChemicalScaleScenario

Answers "if I inject this pH stabiliser / scavenger, what happens to the scaling tendency?" It converts chemical alkalinity to a closed carbonate balance, consumes dissolved sulphide by scavenger capacity, and reports SI before and after.

import neqsim.process.chemistry.scale.ProductionChemicalScaleScenario;

ProductionChemicalScaleScenario sc = new ProductionChemicalScaleScenario();
sc.addChemical(ProductionChemical.causticPHAdjuster("NaOH", 500.0))
  .setTemperatureCelsius(80.0)
  .setPressureBara(60.0)
  .setPH(5.8)
  .setCalciumMgL(1200.0)
  .setBicarbonateMgL(300.0)
  .setSulphateMgL(1800.0)
  .setBariumMgL(250.0)
  .setTotalDissolvedSolidsMgL(90000.0)
  .setCO2PartialPressureBar(1.5);
sc.evaluate();

double treatedPH = sc.getTreatedPH();
double before = sc.getBaselineSaturationIndex("CaCO3");
double after = sc.getTreatedSaturationIndex("CaCO3");
double delta = sc.getSaturationIndexChange("CaCO3");

Supported pH actives: NaOH, soda ash, MDEA, MEA. This is the model that catches the classic own-goal: a pH stabiliser injected for corrosion control pushes CaCO3 into scaling.

7. Chemical Root Cause — RootCauseAnalyser

Rule-based and explainable — every candidate carries an evidence narrative and a score, tagged PRIMARY / CONTRIBUTING / POSSIBLE / RULED_OUT.

import neqsim.process.chemistry.rca.RootCauseAnalyser;
import neqsim.process.chemistry.rca.Symptom;

RootCauseAnalyser rca = new RootCauseAnalyser();
rca.addSymptom(new Symptom(Symptom.Category.DEPOSIT, "hard white deposit in choke")
        .withMeasurement("deposit_thickness_mm", 3.0)
        .withConfidence(0.9));
rca.addSymptom(new Symptom(Symptom.Category.FLOW_RESTRICTION, "choke Cv down 25%"));
rca.addChemical(si);
rca.addChemical(ci);
rca.setCompatibilityAssessor(assessor);
rca.setChemicalTreatmentScenario(sc);
rca.setTemperatureCelsius(85.0);
rca.setPH(5.8);
rca.setCalciumMgL(1200.0);
rca.setBariumMgL(250.0);
rca.setSulphateMgL(1800.0);
rca.setOxygenPpb(15.0);
rca.setMaterial("carbon steel");
rca.analyse();

RootCauseCandidate primary = rca.getPrimary();
List<RootCauseCandidate> ranked = rca.getCandidates();
List<String> gaps = rca.getDataGaps();   // put these in results.json assumptions/gaps

Symptom.Category: DEPOSIT, CORROSION, EMULSION, PH_EXCURSION, FLOW_RESTRICTION, H2S_BREAKTHROUGH, SAMPLE_APPEARANCE, OFF_SPEC, OTHER.

Add measurement evidence to sharpen the ranking:

Map<String, Double> likelihoods = new LinkedHashMap<String, Double>();
likelihoods.put("BASO4_SCALE", 0.8);
likelihoods.put("CACO3_SCALE", 0.2);
rca.addEvidence(likelihoods);
Map<String, Double> posteriors = rca.getBayesianPosteriors();

Then close the loop with the remediation side:

ScaleRemediationAdvisor advisor = new ScaleRemediationAdvisor();
List<ScaleRemediationAdvisor.RemediationOption> options = advisor.recommendFor("BaSO4");

ScaleRemediationAdvisor covers CaCO3, FeCO3, FeS, BaSO4, SrSO4, CaSO4, NaCl and the amorphous H2S-scavenger deposit, with dissolver, concentration, method, temperature window, cautions and a standard reference per option. Details in neqsim-flow-assurance.

8. Getting Inputs from a Live Stream — StreamChemistryAdapter

Do not hand-transcribe the water analysis. Pull it from the flowsheet:

import neqsim.process.chemistry.util.StreamChemistryAdapter;

StreamChemistryAdapter ad = new StreamChemistryAdapter(stream);
double tC = ad.getTemperatureCelsius();
double pCO2 = ad.getPartialPressureBara("CO2");
double ca = ad.getCalciumMgL();
double tds = ad.getTdsMgL();
double h2sPpm = ad.getH2SInGasPpm();
double gasSm3d = ad.getGasFlowSm3PerDay();
double tau = ad.estimateWallShearStressPa(0.2, 4.0);   // pipe ID m, velocity m/s

Convenience constructors that do this for you:

The stream must carry an aqueous phase with the ions — build it with SystemElectrolyteCPAstatoil (neqsim-electrolyte-systems). A dry-gas stream returns zeros; check before trusting a "no scale risk" verdict.

9. Uncertainty — ChemistryUncertaintyAnalyzer

Doses are quoted with far more precision than the inputs justify. Report a band.

import neqsim.process.chemistry.util.ChemistryUncertaintyAnalyzer;

ChemistryUncertaintyAnalyzer unc = new ChemistryUncertaintyAnalyzer();
unc.setNumberOfTrials(2000);
unc.setRandomSeed(42L);
unc.addParameter(unc.triangular("baseRateMmYr", 1.5, 2.4, 4.0));
unc.addParameter(unc.triangular("shearPa", 20.0, 35.0, 60.0));
unc.run(new java.util.function.ToDoubleFunction<double[]>() {
  public double applyAsDouble(double[] x) {
    CorrosionInhibitorPerformance m = new CorrosionInhibitorPerformance();
    m.setChemistry(CorrosionInhibitorPerformance.InhibitorChemistry.IMIDAZOLINE);
    m.setDoseMgL(50.0);
    m.setBaseCorrosionRateMmYr(x[0]);
    m.setWallShearStressPa(x[1]);
    m.setTemperatureCelsius(70.0);
    m.evaluate();
    return m.getInhibitedCorrosionRateMmYr();
  }
});

double p10 = unc.getP10();
double p50 = unc.getP50();
double p90 = unc.getP90();
List<Map<String, Object>> tornado = unc.getTornado();

Feed p10/p50/p90 and tornado straight into the uncertainty block of results.json (neqsim-professional-reporting). Java 8: use an anonymous ToDoubleFunction, not a lambda, if the surrounding code style requires it — both compile.

10. MCP Tools

{
  "analysis": "mechanisticCorrosion",
  "temperature_C": 60, "pressure_bara": 80, "co2_mol": 0.05,
  "velocity_ms": 2.0, "diameter_m": 0.15, "dose_mgL": 50
}

Python Pattern (task notebooks)

ProductionChemical = ns.JClass("neqsim.process.chemistry.ProductionChemical")
Assessor = ns.JClass("neqsim.process.chemistry.ChemicalCompatibilityAssessor")
CIP = ns.JClass("neqsim.process.chemistry.corrosion.CorrosionInhibitorPerformance")

si = ProductionChemical.scaleInhibitor("SI-A", 20.0)
ci = ProductionChemical.corrosionInhibitor("CI-B", 50.0)

a = Assessor()
a.addChemical(si); a.addChemical(ci)
a.setTemperatureCelsius(85.0); a.setCalciumMgL(1200.0)
a.evaluate()

import json
report = json.loads(str(a.toJson()))
print(report["verdict"])

Enums through jpype: CIP.InhibitorChemistry.valueOf("IMIDAZOLINE").

Offshore Workflow

1. Basis — build the produced-water stream (SystemElectrolyteCPAstatoil) from the ion analysis; get T/P/rates from the flowsheet or historian.
2. Threat magnitude — SI, uninhibited corrosion rate, hydrate subcooling, WAT, CII (neqsim-flow-assurance, neqsim-wax-calculations).
3. Chemical selection + dose — the §3 performance models.
4. Compatibility of the full cocktail — §2. Repeat per injection point, because the cocktail differs at the wellhead, the manifold and the topside inlet.
5. Second-order effects — §6 (does the pH adjuster create a scale problem?), §5 (does the demulsifier meet the OiW spec?).
6. Uncertainty + gaps — §9 plus getWarnings() / getDataGaps() from every model.
7. Report — standards_applied, key_results, uncertainty, risk_evaluation.

Agent Cooperation

Gotchas

Reference Documentation

• docs/chemistry/index.md — capability matrix and standards
• docs/chemistry/chemical_compatibility_guide.md
• docs/chemistry/mechanistic_corrosion.md
• docs/chemistry/packed_bed_scavenger.md
• Notebooks: examples/notebooks/chemical_integrity_digital_twin.ipynb, chemistry_corrosion_inhibitor_design.ipynb
• Tests / regression baseline: src/test/java/neqsim/process/chemistry/