What's the most cost-effective way to build reservoir models using reservoir simulation software for multiple producing assets?

The most cost-effective way is usually not to build a fully detailed, hand-tuned simulation model for every asset. Instead, use a tiered, standardized workflow so you only spend high effort where it pays back.

Best-practice cost-effective approach

1. Screen and prioritize assets

   • Classify fields by value, uncertainty, and decision impact.
   • Use simple decline, material balance, or proxy models first.
   • Only move assets into full dynamic simulation if the expected value of better decisions justifies the cost.

2. Use a standard model framework

   • Create a repeatable template for geology, fluid PVT, relperm, aquifer, wells, and forecasting.
   • Reuse grid definitions, property functions, and history-match workflows across assets.
   • Standardization reduces engineering hours and software setup time.

3. Build the simplest model that answers the business question

   • For development planning or reserves, a coarse black-oil model is often enough.
   • Reserve compositional or thermal models only for cases where phase behavior or temperature effects materially matter.
   • Avoid over-gridding and over-detailing unless it changes the forecast.

4. Automate data loading and preprocessing

   • Automate well, production, pressure, PVT, and static data transfer from databases/spreadsheets into the simulator.
   • Use scripts or workflows to handle repeated tasks like unit conversion, QC, and file generation.
   • This is one of the biggest cost savers across multiple assets.

5. Use proxy models or reduced-order models for many-scenario work

   • For uncertainty studies and optimization, run a full simulator on a limited set of cases.
   • Build proxy models, emulators, or history-matched reduced models for fast forecasting across many assets.
   • This cuts CPU and engineering time significantly.

6. Adopt a centralized modeling team with local asset support

   • Keep core reservoir engineering standards, templates, and QA/QC centralized.
   • Let asset teams provide field-specific knowledge.
   • This avoids duplication and inconsistent modeling practices.

7. Leverage existing models before rebuilding

   • Start from prior models, neighboring analogs, or similar asset templates.
   • Update only the parts that matter: wells, boundaries, PVT, faults, and key static properties.

8. Right-size software and compute

   • Use commercial simulators only where their capabilities are needed.
   • Run batch jobs on shared HPC/cloud resources rather than individual desktops.
   • Use license-efficient scheduling if licenses are a bottleneck.

9. Focus history matching on high-value observables

   • Match the variables that influence decisions: rates, pressures, GOR/water cut, and key surveillance data.
   • Don’t over-invest in perfect geological detail if it doesn’t improve forecast accuracy.

10. Maintain model governance

• Version control inputs and assumptions.
• Document what changed and why.
• This reduces rework when assets are revisited.

Most cost-effective overall strategy

For multiple producing assets, the best value usually comes from:

• Tier 1: simple screening models for all assets
• Tier 2: standardized coarse simulation models for promising assets
• Tier 3: detailed full-field models only for high-value/high-uncertainty cases

In one sentence

Use a standardized, automated, tiered modeling workflow that starts simple, scales detail only where needed, and reuses templates and data pipelines across assets.

If you want, I can also give you:

• a cost-effective workflow diagram, or
• a comparison of simulator types and when to use each.