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gabrielserrao

pyResToolbox MCP Server

oil_bubble_point

Calculate bubble point pressure for oil reservoirs using API gravity, temperature, and gas-oil ratio to determine when gas evolves from solution, essential for reservoir analysis and production planning.

Instructions

Calculate oil bubble point pressure (Pb).

CRITICAL PVT PROPERTY - The bubble point is the pressure at which gas first begins to evolve from solution in oil. Essential for all oil reservoir calculations.

Parameters:

  • api (float, required): Oil API gravity in degrees. Valid range: 0-100. Typical values: 20-50. Example: 35.0 for medium gravity crude.

  • degf (float, required): Reservoir temperature in degrees Fahrenheit. Valid range: -460 to 1000. Typical: 100-300°F. Example: 180.0.

  • rsb (float, required): Solution gas-oil ratio at bubble point in scf/stb. Must be ≥ 0. Typical: 100-3000 scf/stb. Example: 800.0.

  • sg_g (float, optional, default=0.0): Gas specific gravity (air=1.0). Valid range: 0-3. Typical: 0.6-1.2. Example: 0.75 for associated gas.

  • method (str, optional, default="VALMC"): Correlation method. Options: "STAN", "VALMC", "VELAR". VALMC recommended for wider applicability.

Method Selection:

  • VALMC (Valko-McCain 2003): Recommended. Best for wide range of conditions. Use for: Most applications, high GOR oils, wide temperature ranges.

  • STAN (Standing 1947): Classic correlation. Use for: Standard conditions, quick estimates, compatibility with older methods.

  • VELAR (Velarde 1997): Alternative method. Use for: Specific regional correlations, comparison studies.

Returns: Dictionary with:

  • value (float): Bubble point pressure in psia

  • method (str): Method used

  • units (str): "psia"

  • inputs (dict): Echo of input parameters

Common Mistakes:

  • Using separator temperature instead of reservoir temperature

  • Confusing rsb (solution GOR at bubble point) with separator GOR

  • Using gas gravity from wrong separator stage

  • Temperature in Celsius instead of Fahrenheit

Example Usage:

{
    "api": 35.0,
    "degf": 180.0,
    "rsb": 800.0,
    "sg_g": 0.75,
    "method": "VALMC"
}

Expected result: Pb ≈ 3000-4000 psia for typical oil.

Note: If Pb > reservoir pressure, reservoir is undersaturated (no free gas). If Pb < reservoir pressure, reservoir is saturated (gas cap present).

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 fully discloses behavioral traits: it explains the calculation's significance, lists parameter valid ranges and typical values, details method options with recommendations, describes the return dictionary structure, and warns of common errors. This covers all necessary aspects like input validation and output format.

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

Conciseness4/5

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

The description is well-structured with clear sections (Parameters, Method Selection, Returns, etc.), but it is somewhat lengthy. Every sentence adds value (e.g., the 'Common Mistakes' and 'Note' sections are highly informative), though it could be more front-loaded by moving key usage guidelines earlier.

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 (multiple parameters with specific ranges, method choices) and no annotations, the description is complete: it explains the tool's purpose, parameters, methods, return values (though an output schema exists, it still clarifies the dictionary structure), usage context, and common pitfalls. No significant gaps remain.

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 (parameters are nested under 'request' with minimal schema descriptions), the description comprehensively documents all parameters: it defines each with units, valid ranges, typical values, examples, and practical notes (e.g., distinguishing rsb from separator GOR). This fully compensates for the schema's lack of detail.

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 clearly states the tool calculates oil bubble point pressure (Pb), specifying it's a critical PVT property with a precise definition. It distinguishes itself from siblings like 'oil_rs_at_bubble_point' or 'oil_solution_gor' by focusing on pressure calculation rather than gas-oil ratio or other properties.

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

Usage Guidelines5/5

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

It provides explicit guidance on when to use each correlation method (VALMC for wide applicability, STAN for standard conditions, VELAR for regional studies) and includes a 'Common Mistakes' section warning against misusing parameters like temperature units or GOR values. The note on Pb vs. reservoir pressure clarifies practical application contexts.

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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