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gabrielserrao

pyResToolbox MCP Server

generate_aquifer_influence

Calculate Van Everdingen & Hurst aquifer influence functions for reservoir simulation. Generates dimensionless pressure-time tables to model water influx from surrounding aquifers into hydrocarbon reservoirs for ECLIPSE/Intersect simulations.

Instructions

Generate Van Everdingen & Hurst aquifer influence functions.

CRITICAL AQUIFER MODELING TOOL - Creates dimensionless aquifer influence functions for AQUTAB keyword in ECLIPSE/Intersect. These functions quantify water influx from surrounding aquifers into hydrocarbon reservoirs.

Parameters:

  • res (float or list, required): Dimensionless aquifer radius (ReD). Must be > 1.0. Can be scalar or list. Typical: 5-100. Example: 10.0. ReD = re/rw where re = aquifer outer radius, rw = reservoir radius.

  • start (float, required): Minimum dimensionless time (tD_min). Must be > 0. Typical: 0.01-1.0. Example: 0.1.

  • end (float, required): Maximum dimensionless time (tD_max). Must be > start. Typical: 10-1000. Example: 100.0.

  • rows (int, required): Number of time points in table. Must be > 0. Typical: 20-100. Example: 50. More rows = smoother curves.

Background: Van Everdingen & Hurst (1949) developed analytical solutions for aquifer influx using diffusivity equation. These functions relate:

  • Dimensionless time (tD) = (k × t) / (φ × μ × ct × rw²)

  • Dimensionless pressure (pD) = aquifer response function

  • Dimensionless radius (ReD) = aquifer geometry

Influence Function: The influence function pD(tD, ReD) represents the dimensionless pressure response at the reservoir-aquifer boundary. It depends on:

  • Aquifer geometry (radial vs linear, finite vs infinite)

  • Boundary conditions (constant rate vs constant pressure)

  • Aquifer properties (permeability, porosity, compressibility)

Applications:

  • Material Balance: Quantify aquifer support in material balance analysis

  • Pressure Maintenance: Evaluate aquifer pressure support

  • Water Influx: Calculate cumulative water influx over time

  • History Matching: Match production history with aquifer model

  • Production Forecasting: Predict future aquifer influx

Integration Method: Uses numerical integration (Gaussian quadrature) of diffusivity equation with high-resolution integration (M=8) for accuracy. The solution is computed at specified dimensionless time points.

Returns: Dictionary with:

  • dimensionless_time (list): Dimensionless time values (tD)

  • dimensionless_pressures (list): List of pD arrays (one per ReD)

  • rows (int): Number of time points

  • dimensionless_radii (list): ReD values used

  • time_range (dict): Start and end dimensionless times

  • note (str): Usage guidance for ECLIPSE

  • inputs (dict): Echo of input parameters

Common Mistakes:

  • ReD < 1.0 (aquifer radius must be > reservoir radius)

  • tD_max < tD_min (end must be > start)

  • Too few rows (<10) causing poor resolution

  • Wrong dimensionless radius (must match aquifer geometry)

  • Confusing dimensionless time with actual time

  • Not accounting for aquifer compressibility

Example Usage:

{
    "res": 10.0,
    "start": 0.1,
    "end": 100.0,
    "rows": 50
}

Result: Table with 50 time points from tD=0.1 to tD=100.0 for ReD=10.0.

Note: AQUTAB keyword is ready for direct inclusion in ECLIPSE DATA file. The influence functions are dimensionless and must be scaled using reservoir and aquifer properties. For multiple aquifers, generate separate tables for each aquifer with different ReD values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains what the tool does (creates dimensionless aquifer influence functions), the integration method (numerical integration with Gaussian quadrature), output format (dictionary with specific keys), and practical considerations like common mistakes and scaling requirements. This provides comprehensive behavioral context beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Parameters, Background, Applications, etc.), but it's quite lengthy with multiple paragraphs that could be more streamlined. While all content is relevant, some sections (like extensive background theory) might be more detailed than necessary for tool selection, affecting conciseness despite good organization.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (aquifer modeling with multiple parameters), no annotations, and an output schema that exists but isn't detailed here, the description provides exceptional completeness. It covers purpose, parameters, methodology, outputs, applications, common mistakes, examples, and integration instructions, making it fully self-contained for understanding and using the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage (the schema has only one parameter 'request' with nested properties), the description compensates fully by detailing four key parameters (res, start, end, rows) with their meanings, constraints, typical ranges, examples, and physical interpretations. It also covers additional parameters from the schema (like infl, ei, piston) in the background and application sections, providing substantial semantic value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool generates Van Everdingen & Hurst aquifer influence functions for AQUTAB keyword in ECLIPSE/Intersect, specifying both the action ('generate') and resource ('aquifer influence functions'). It clearly distinguishes this from sibling tools by focusing on aquifer modeling rather than fluid properties or other reservoir engineering calculations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (aquifer modeling, material balance, pressure maintenance, etc.) and mentions integration with ECLIPSE. However, it does not explicitly state when NOT to use it or name specific alternatives among the sibling tools, though the specialized nature implies it's for aquifer-specific calculations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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