L1 ⚛ L1-462 ⊙ Testnet

Enhanced Oil Recovery (EOR) Modeling

Petroleum Engineering · EOR simulation

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⬇ L1-462.md

# ⚛ L1 Principle — Enhanced Oil Recovery (EOR) Modeling **ID:** `L1-462` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Petroleum Engineering — *EOR simulation* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** 3D saturation composition > **📡 Carrier:** acoustic · **🌫 Noise:** measurement gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555280 --- ## 🧠 1. Introduction **Enhanced Oil Recovery (EOR) Modeling** is a **parameter-estimation problem** whose unknown lives in **3D saturation composition** space, within the **EOR simulation** sub-domain of **Petroleum Engineering**. Measurements consist of acoustic pressure waves recorded by transducers via a **eor displacement modeling** sensing mechanism. The forward operator applies, in order: gradient / divergence with respect to position; S · fdm · convection diffusion operator; O · recovery factor · prediction operator. Observations are corrupted by measurement gaussian. Existence of the recovered 3D_saturation_composition 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 ~= 500); viscous_fingering_onset 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** → Spatial derivative → S · fdm · convection diffusion → O · recovery factor · prediction → **y** (detector). ``` y = `O.recovery_factor.prediction` `S.fdm.convection_diffusion` ∇ x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `D.space` | Gradient / divergence with respect to position | | `S.fdm.convection_diffusion` | S · fdm · convection diffusion operator | | `O.recovery_factor.prediction` | O · recovery factor · prediction operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Petroleum Engineering | | Sub domain | EOR simulation | | Carrier | acoustic | | Problem class | parameter_estimation | | Solution space | 3D_saturation_composition | | Noise model | measurement_gaussian | | Integration axis | displacement_front | | Difficulty delta | 5 | | L dag | 3.8 | ## 📡 4. Measurement Model Existence of the recovered 3D_saturation_composition 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 ~= 500); viscous_fingering_onset dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Measurement gaussian sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | recovery_factor_RMSE_percent | | Secondary | breakthrough_time_error_days | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `eor_displacement_modeling` **Center point:** | Parameter | Unit | Value | |---|---|---| | Injection pv | — | 1 | | Temperature f | — | 200 | | Mobility ratio m | m | 1.5 | | Dykstra parsons v dp | — | 0.7 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Injection pv | — | 0.1 – 3.0 | | Temperature f | — | 100 – 350 | | Mobility ratio m | m | 0.1 – 10.0 | | Dykstra parsons v dp | — | 0.3 – 0.9 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 3 recovery_factor_RMSE_percent | Metric | Range | |---|---| | Recovery factor rmse percent | 0.5 – 15.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **recovery_factor_RMSE_percent**, with κ = `10000.0` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xdccae997ffffe88e0348c6a68d69078a4400bf7e16a97a54a9745d82052909e1` - **Chain tx hash:** `0xd01614d48b3a184fc85c0931004dd06c5268ea039febc35ef0f081ac37daf2e1` - **Chain block:** `41555280` --- ## File Mapping This bundle consists of: `L1-462.md`, `L1-462.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-462.md` | Source of truth — edit this | Human or LLM | | `L1-462.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 `L1-462`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-462-001 Enhanced Oil Recovery (EOR) Modeling — Nominal + Mismatch Specs

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📋 JSON metadata

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  "chain_hash": "0xdccae997ffffe88e0348c6a68d69078a4400bf7e16a97a54a9745d82052909e1",
  "chain_tx_hash": "0xd01614d48b3a184fc85c0931004dd06c5268ea039febc35ef0f081ac37daf2e1",
  "domain": "Petroleum Engineering",
  "hardness_fn": {
    "delta": 5,
    "kappa": 10000.0,
    "metric": "recovery_factor_RMSE_percent",
    "type": "epsilon_fn"
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    "regime": "Existence of the recovered 3D_saturation_composition 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 ~= 500); viscous_fingering_onset dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Measurement gaussian sets the irreducible data-fidelity floor.",
    "secondary": "breakthrough_time_error_days"
  },
  "physics_fingerprint": {
    "L_DAG": 3.8,
    "carrier": "acoustic",
    "difficulty_delta": 5,
    "domain": "Petroleum Engineering",
    "integration_axis": "displacement_front",
    "noise_model": "measurement_gaussian",
    "primitives": [
      "D.space",
      "S.fdm.convection_diffusion",
      "O.recovery_factor.prediction"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "eor_displacement_modeling",
    "solution_space": "3D_saturation_composition",
    "sub_domain": "EOR simulation",
    "title": "Enhanced Oil Recovery (EOR) Modeling"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "eor_displacement_modeling"
    ],
    "allowed_omega_dimensions": [
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      "Dykstra_Parsons_V_DP",
      "injection_PV",
      "temperature_F"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "3 recovery_factor_RMSE_percent",
      "forward_operator": "eor_displacement_modeling",
      "input_format": "measurement_only",
      "omega": {
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        "injection_PV": 1.0,
        "mobility_ratio_M": 1.5,
        "temperature_F": 200
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "recovery_factor_RMSE_percent": [
        0.5,
        15.0
      ]
    },
    "omega_bounds": {
      "Dykstra_Parsons_V_DP": [
        0.3,
        0.9
      ],
      "injection_PV": [
        0.1,
        3.0
      ],
      "mobility_ratio_M": [
        0.1,
        10.0
      ],
      "temperature_F": [
        100,
        350
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "EOR simulation",
  "title": "Enhanced Oil Recovery (EOR) Modeling"
}

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