neqsim-artificial-lift-screening

name	neqsim-artificial-lift-screening
version	0.1.0
description	Educational artificial-lift screening that compares a simple straight-line IPR natural deliverability against a target rate and screens gas lift and ESP feasibility from a required bottomhole-pressure reduction. USE WHEN: a task needs a public, screening-level recommendation of natural flow, gas lift, or ESP for a well before detailed inflow/outflow (nodal) analysis and lift design.
last_verified	2026-06-25
requires	{"python_packages":[],"java_packages":[],"env":[],"network":[]}

Artificial Lift Screening

Use this skill for public, educational artificial-lift triage. It estimates natural deliverability from a straight-line inflow performance relationship (IPR), checks it against a target rate, and screens gas lift and electric submersible pump (ESP) feasibility from the bottomhole-pressure reduction required to meet the target.

When to Use

• When a user asks whether a well can flow naturally or needs artificial lift to hit a target rate.
• When an agent needs a quick gas-lift vs ESP recommendation before nodal analysis.
• When the reservoir may be unable to deliver the target even at zero bottomhole pressure.
• When examples must run without proprietary well models, vendor pump curves, or field data.

Inputs

• reservoir_pressure_bar: average reservoir pressure in bar.
• bottomhole_flowing_pressure_bar: current flowing bottomhole pressure in bar.
• productivity_index_sm3_d_bar: straight-line productivity index in Sm3/d per bar drawdown.
• target_rate_sm3_d: required production rate in Sm3/d.
• water_cut: produced water fraction in [0, 1), default 0.0.
• gas_lift_available: whether gas lift injection is available, default True.
• max_injection_gas_sm3_d: optional available lift-gas injection rate in Sm3/d.
• esp_max_head_m: optional maximum ESP head in metres.
• fluid_gradient_bar_per_m: hydrocarbon fluid gradient in bar/m, default 0.09.
• well_depth_m: producing depth in metres, default 2000.

Outputs

• natural_rate_sm3_d: deliverability from the straight-line IPR at the current bottomhole pressure.
• required_pwf_bar: bottomhole pressure required to deliver the target rate.
• required_pressure_reduction_bar: bottomhole-pressure reduction needed beyond the current flowing pressure.
• esp_required_head_m: ESP head needed for the pressure reduction, or null.
• gas_lift_feasible: True/False gas-lift feasibility flag.
• esp_feasible: True/False ESP feasibility flag.
• recommended_method: natural-flow, gas-lift, esp, or infeasible.
• warning: same value as recommended_method for quick triage.
• assumptions: public assumptions used by the placeholder model.

Engineering Method

The Python class ArtificialLiftModel uses open well relations only:

• natural deliverability uses a straight-line IPR: q = PI * (Pr - Pwf).
• the bottomhole pressure for the target rate is Pwf_req = Pr - target / PI; if Pwf_req <= 0 the reservoir cannot deliver the target and the result is infeasible.
• the required pressure reduction is dP = Pwf_current - Pwf_req (zero when natural flow already meets the target).
• an effective gradient blends fluid and water gradients: grad = fluid_gradient * (1 - water_cut) + 0.0981 * water_cut (bar/m).
• ESP head uses H = dP / grad, feasible when esp_max_head_m is supplied and H <= esp_max_head_m.
• gas lift can lighten the column up to about half the hydrostatic head, dP_max = 0.5 * grad * well_depth, feasible when lift gas is available and dP <= dP_max.

This is educational and screening-only logic. It assumes a straight-line IPR (no turbulence or saturation effects), a static effective gradient, and a fixed gas-lift lightening fraction. It is not a replacement for validated nodal analysis or qualified lift design.

Python Usage Pattern

from artificial_lift_screening import ArtificialLiftModel

model = ArtificialLiftModel()
result = model.evaluate(
    reservoir_pressure_bar=250.0,
    bottomhole_flowing_pressure_bar=200.0,
    productivity_index_sm3_d_bar=8.0,
    target_rate_sm3_d=800.0,
    esp_max_head_m=2500.0,
)

print(result.natural_rate_sm3_d)
print(result.required_pressure_reduction_bar)
print(result.recommended_method)

Related NeqSim Functionality

For validated inflow/outflow and lift modelling, redirect to NeqSim resources:

• neqsim.process.equipment.reservoir.WellFlow — well inflow with a productivity-index based IPR.
• neqsim.process.equipment.reservoir.SimpleReservoir — material-balance reservoir for production forecasting.
• the neqsim-production-optimization skill — gas lift allocation and bottleneck analysis guidance.

This skill is a public triage layer that decides when to invoke validated nodal analysis.

Validation Checklist

• Reservoir and flowing pressures, productivity index, and target rate are positive.
• Water cut is in [0, 1) and depth and gradient are positive.
• An infeasible reservoir (required Pwf at or below zero) is reported as infeasible.
• Tests cover natural flow, gas-lift, ESP, infeasible, and invalid input.
• Real lift selection is redirected to validated NeqSim resources and qualified design.

Common Mistakes

Symptom	Cause	Fix
Always esp never gas-lift	gas_lift_available left False	Set gas lift availability to True
ESP feasibility always False	esp_max_head_m not supplied	Provide a maximum ESP head
Recommendation infeasible	Target rate exceeds PI x reservoir pressure	Lower the target or revisit PI

Limitations

• No proprietary well models, vendor pump curves, or field data are included.
• A straight-line IPR is used; no bubble-point or turbulence effects are modelled.
• Gas-lift and ESP feasibility use single fixed-fraction screening rules.

References

• Beggs, H. D., Production Optimization Using Nodal Analysis.
• Economides, M. J. et al., Petroleum Production Systems.
• API RP 11S, Recommended Practice for the Operation, Maintenance, and Troubleshooting of Electric Submersible Pump Installations.
• NeqSim repository: https://github.com/equinor/neqsim