AgentStack
Browse Sign in
Browse Why AgentStack Sell Docs
Sign in
SKILL verified Apache-2.0 Self-run

Neqsim Artificial Lift Screening

skill-equinor-neqsim-community-skills-artificial-lift-screening · by equinor

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.

No reviews yet
0 installs
27 views
0.0% view→install

Install

$ agentstack add skill-equinor-neqsim-community-skills-artificial-lift-screening

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

AgentStack Verified badge Links to your public security report.
[![AgentStack Verified](https://agentstack.voostack.com/badges/verified.svg)](https://agentstack.voostack.com/security/report/skill-equinor-neqsim-community-skills-artificial-lift-screening)

Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
Are you the author of Neqsim Artificial Lift Screening? Claim this listing to set pricing, connect Stripe payouts, and keep 70% of every sale.
Sign up to claim

About

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

No reviews yet, be the first.

Versions

  • v0.1.0 Imported from the upstream source.