L1 ⚛ L1-459 ⊙ Testnet

Compositional Reservoir Simulation

Petroleum Engineering · EOS-based simulation

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⬇ L1-459.md

# ⚛ L1 Principle — Compositional Reservoir Simulation **ID:** `L1-459` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Petroleum Engineering — *EOS-based simulation* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** 3D composition pressure > **📡 Carrier:** acoustic · **🌫 Noise:** measurement gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555279 --- ## 🧠 1. Introduction **Compositional Reservoir Simulation** is a **parameter-estimation problem** whose unknown lives in **3D composition pressure** space, within the **EOS-based simulation** sub-domain of **Petroleum Engineering**. Measurements consist of acoustic pressure waves recorded by transducers via a **compositional eos flow** sensing mechanism. The forward operator applies, in order: applies a smooth nonlinear function element-wise; S · flash · rachford rice operator; O · material balance · compositional operator. Observations are corrupted by measurement gaussian. Existence of the recovered 3D_composition_pressure is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 5000); EOS_tuning_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Measurement gaussian sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Pointwise nonlinearity → S · flash · rachford rice → O · material balance · compositional → **y** (detector). ``` y = `O.material_balance.compositional` `S.flash.rachford_rice` f(·) x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `N.pointwise` | Applies a smooth nonlinear function element-wise | | `S.flash.rachford_rice` | S · flash · rachford rice operator | | `O.material_balance.compositional` | O · material balance · compositional operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Petroleum Engineering | | Sub domain | EOS-based simulation | | Carrier | acoustic | | Problem class | parameter_estimation | | Solution space | 3D_composition_pressure | | Noise model | measurement_gaussian | | Integration axis | 3d_compositional | | Difficulty delta | 5 | | L dag | 4.5 | ## 📡 4. Measurement Model Existence of the recovered 3D_composition_pressure is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 5000); EOS_tuning_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Measurement gaussian sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | GOR_prediction_RMSE_percent | | Secondary | dew_point_pressure_error_psi | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `compositional_eos_flow` **Center point:** | Parameter | Unit | Value | |---|---|---| | N cells | — | 5000 | | Gor scf stb | — | 1000 | | N components | — | 6 | | Reservoir pressure psi | — | 5000 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N cells | — | 100 – 100000 | | Gor scf stb | — | 100 – 100000 | | N components | — | 2 – 20 | | Reservoir pressure psi | — | 500 – 15000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 10 GOR_prediction_RMSE_percent | Metric | Range | |---|---| | Gor prediction rmse percent | 2 – 50 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **GOR_prediction_RMSE_percent**, with κ = `1000000.0` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x7c7a6c2b230eb5a08a88ad03ecdbd0879fcf36f3644e44abffa1964814dc9300` - **Chain tx hash:** `0xbf6fee84fbb5f60f894e27831bebdf564c6ea7e0ece0931e965d45b0721588f4` - **Chain block:** `41555279` --- ## File Mapping This bundle consists of: `L1-459.md`, `L1-459.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-459.md` | Source of truth — edit this | Human or LLM | | `L1-459.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-459`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-459.md).
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L2-459-001 Compositional Reservoir Simulation — Nominal + Mismatch Specs

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📋 JSON metadata

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  "chain_block": 41555279,
  "chain_hash": "0x7c7a6c2b230eb5a08a88ad03ecdbd0879fcf36f3644e44abffa1964814dc9300",
  "chain_tx_hash": "0xbf6fee84fbb5f60f894e27831bebdf564c6ea7e0ece0931e965d45b0721588f4",
  "domain": "Petroleum Engineering",
  "hardness_fn": {
    "delta": 5,
    "kappa": 1000000.0,
    "metric": "GOR_prediction_RMSE_percent",
    "type": "epsilon_fn"
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  "initiator_dataset": [
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      "license_hash": null,
      "name": "primary",
      "weight": 1.0
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  "observable_profile": {
    "metric": "GOR_prediction_RMSE_percent",
    "regime": "Existence of the recovered 3D_composition_pressure is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 5000); EOS_tuning_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Measurement gaussian sets the irreducible data-fidelity floor.",
    "secondary": "dew_point_pressure_error_psi"
  },
  "physics_fingerprint": {
    "L_DAG": 4.5,
    "carrier": "acoustic",
    "difficulty_delta": 5,
    "domain": "Petroleum Engineering",
    "integration_axis": "3d_compositional",
    "noise_model": "measurement_gaussian",
    "primitives": [
      "N.pointwise",
      "S.flash.rachford_rice",
      "O.material_balance.compositional"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "compositional_eos_flow",
    "solution_space": "3D_composition_pressure",
    "sub_domain": "EOS-based simulation",
    "title": "Compositional Reservoir Simulation"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "compositional_eos_flow"
    ],
    "allowed_omega_dimensions": [
      "N_cells",
      "N_components",
      "reservoir_pressure_psi",
      "GOR_scf_stb"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "10 GOR_prediction_RMSE_percent",
      "forward_operator": "compositional_eos_flow",
      "input_format": "measurement_only",
      "omega": {
        "GOR_scf_stb": 1000,
        "N_cells": 5000,
        "N_components": 6,
        "reservoir_pressure_psi": 5000
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "GOR_prediction_RMSE_percent": [
        2,
        50
      ]
    },
    "omega_bounds": {
      "GOR_scf_stb": [
        100,
        100000
      ],
      "N_cells": [
        100,
        100000
      ],
      "N_components": [
        2,
        20
      ],
      "reservoir_pressure_psi": [
        500,
        15000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "EOS-based simulation",
  "title": "Compositional Reservoir Simulation"
}

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