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gabrielserrao

pyResToolbox MCP Server

oil_solution_gor

Calculate solution gas-oil ratio (Rs) for petroleum engineering analysis. Determines gas dissolved in oil at specified pressure and temperature using industry-standard correlations.

Instructions

Calculate solution gas-oil ratio (Rs) at specified pressure.

CRITICAL PVT PROPERTY - Computes volume of gas dissolved in oil at given pressure and temperature. Rs increases with pressure up to bubble point, then remains constant (equal to rsb) above bubble point.

Parameters:

  • api (float, required): Oil API gravity in degrees. Valid: 0-100. Example: 35.0.

  • degf (float, required): Reservoir temperature in °F. Valid: -460 to 1000. Example: 180.0.

  • p (float or list, required): Pressure(s) in psia. Must be > 0. Can be scalar or array. Example: 3000.0 or [2000, 3000, 4000].

  • sg_g (float, optional, default=0.0): Separator gas specific gravity (air=1). Valid: 0-3. Typical: 0.6-1.2. Example: 0.75.

  • pb (float, optional, default=0.0): Bubble point pressure in psia. If 0, will be calculated. Must be ≥ 0. Example: 3500.0.

  • rsb (float, optional, default=0.0): Solution GOR at bubble point in scf/stb. If 0 and pb provided, will be calculated. Must be ≥ 0. Example: 800.0.

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

Pressure Behavior:

  • p < pb: Rs calculated from correlation (increases with pressure)

  • p ≥ pb: Rs = rsb (constant, no additional gas dissolves)

Method Selection:

  • VELAR (Velarde 1997): Default, good accuracy. Use for most cases.

  • STAN (Standing 1947): Classic, widely used. Use for compatibility.

  • VALMC (Valko-McCain 2003): Alternative method.

Returns: Dictionary with:

  • value (float or list): Rs in scf/stb (matches input p shape)

  • method (str): Method used

  • units (str): "scf/stb"

  • inputs (dict): Echo of input parameters

Common Mistakes:

  • Using separator gas gravity instead of separator gas gravity (sg_g parameter)

  • Not providing pb when p > pb (will calculate incorrectly)

  • Pressure in barg/psig instead of psia (must be absolute)

  • Confusing rsb (at bubble point) with separator GOR

Example Usage:

{
    "api": 35.0,
    "degf": 180.0,
    "p": [2000, 3000, 4000],
    "sg_g": 0.75,
    "pb": 3500.0,
    "rsb": 800.0,
    "method": "VELAR"
}

Result: Rs increases from ~400 scf/stb at 2000 psia to 800 scf/stb at 3500 psia, then remains 800 scf/stb at 4000 psia (above bubble point).

Note: Always provide pb and rsb when available for accurate results. If unknown, set pb=0 and rsb=0 to auto-calculate, but accuracy may be reduced.

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 the tool's behavior: how Rs changes with pressure (increases up to bubble point then constant), method options and their characteristics, return format, accuracy considerations, and critical implementation details like pressure units and parameter dependencies.

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, Pressure Behavior, Method Selection, Returns, Common Mistakes, Example Usage, Note) but is quite lengthy. While every section adds value, some redundancy exists (e.g., parameter details are repeated in example). It could be more concise while maintaining clarity.

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?

The description is exceptionally complete for this complex tool. It covers purpose, parameters, behavior, method selection, return format, common pitfalls, and usage examples. Despite having an output schema, the description usefully explains the return structure. No significant gaps exist given the tool's complexity and lack of annotations.

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?

Given 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. Each parameter is explained with purpose, valid ranges, examples, default values, and practical guidance. The description adds significant meaning beyond what the bare schema provides, including parameter interactions and calculation implications.

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's purpose: 'Calculate solution gas-oil ratio (Rs) at specified pressure.' It specifies the verb ('calculate'), resource ('solution gas-oil ratio'), and scope ('at specified pressure'), distinguishing it from sibling tools like 'oil_rs_at_bubble_point' which focuses only on bubble point conditions.

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?

The description provides explicit guidance on when to use this tool, including method selection recommendations (e.g., 'VELAR... Use for most cases'), pressure behavior rules ('p < pb' vs 'p ≥ pb'), and common mistakes to avoid. It also distinguishes usage from related tools by explaining the pressure-dependent behavior of Rs.

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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