qfoldit-meor

qfoldit-meor (Microbial Enhanced Oil Recovery)

Kinetic + petrophysical model chaining four well-established mechanisms: microbial growth (Monod) -> biosurfactant production (Luedeking-Piret) -> interfacial tension reduction (Hill-type saturation) -> incremental oil recovery via the capillary desaturation curve (CDC), the standard petroleum-engineering relationship between capillary number and residual oil saturation.

Read references/model_documentation.md before answering -- it has the full equations, sources, a documented modeling pitfall found and fixed during development (see below), and calibration status.

Important: a documented pitfall (so you don't repeat it in reasoning)

During development, an early version of this model showed a large nonzero incremental recovery already at day 0, before any biosurfactant had been produced -- caused by comparing the model's Sor against an idealized textbook asymptote (Sor at Nc->0) rather than against the actual pre-treatment Sor implied by the scenario's own baseline capillary number. This was a real bug, not just a caveat, and was fixed: predict_meor_recovery now always computes "incremental recovery" relative to the scenario's own P=0 (pre-treatment) baseline Sor. If you build any independent recovery calculation on top of this skill's outputs, use the same self-consistent baseline principle, not an idealized reference constant.

How to handle a request

1. Gather inputs: microbial growth parameters (mu_max, Ks, yield coefficient) if known, or use provided defaults with a clear caveat; injection/reservoir flow velocity, oil viscosity, baseline IFT (sigma0, typically ~20-30 mN/m without surfactant for crude oil-brine systems).
2. Run the pipeline: predict_meor_recovery(...) in scripts/meor_kinetics.py -- returns biomass, substrate, biosurfactant, IFT, capillary number, Sor, and incremental recovery fraction over time.
3. Always report incremental recovery as a fraction of REMAINING oil after waterflood, not of OOIP directly -- converting to a %OOIP figure requires knowing the waterflood-residual oil saturation as a fraction of OOIP for the specific reservoir, which this model doesn't assume generically.
4. State calibration status: growth kinetics, Luedeking-Piret coefficients, and CDC parameters (Nc_critical, Sor_high/low_Nc) are generic literature-consistent defaults, not fitted to any specific qFoldIT reservoir/core-flood data. If real core-flood or field pilot data exists, use fit_ift_hill_params (nonlinear least squares, validated to recover known parameters accurately) to calibrate the IFT-biosurfactant relationship; growth and CDC parameters would need analogous fitting once such data exists (not yet implemented as separate functions).
5. Sanity-check self-consistency: if a computed scenario shows substantial "incremental recovery" at t=0 or before meaningful biosurfactant has accumulated, that indicates a baseline/parameter inconsistency (see pitfall above) -- flag it rather than reporting it.