← 論文一覧に戻る

簡易物理ベーススクリーニング手法を用いた既存坑井沿いのCO2漏出フラックス推定

Estimating CO2 Leakage Fluxes along Legacy Wells Using a Rapid Physics-Based Screening Approach (原題)

Ramachandran, Hariharan, De Jonge-Anderson, Iain, Pullen, Benjamin, Cahill, Aaron

EarthArXivプレプリント2026-09-02#CCUSOrigin: Global対象セクター: energy
DOI: 10.31223/x5cr4x
原典: https://eartharxiv.org/repository/object/14756/download/25658/

🤖 gxceed AI 要約

日本語

CO2地中貯留の拡大には既存坑井の漏出リスク評価が不可欠だが、高精度シミュレーションは計算負荷が高い。本論文は、簡易物理モデルQWellRATEを提案し、深度依存のCO2物性を考慮しつつ、坑井の漏出フラックスを迅速に推定する。セメントプラグの劣化が漏出に大きく影響し、地域スクリーニングや不確実性評価に有効である。

English

Expanding CO2 storage requires assessing leakage risks from legacy wells, but high-fidelity simulations are computationally intensive. This paper presents QWellRATE, a simplified physics-based model that rapidly estimates CO2 leakage fluxes along wellbores, accounting for depth-dependent CO2 properties. It shows cement plug degradation is a key control and enables regional screening and uncertainty quantification.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本ではCCS事業の具体化が進み、洋野町や苫小牧などでの貯留候補地選定が急務。既存坑井のリスク評価は、事業認可や地域住民説明に不可欠であり、本手法は初期段階のスクリーニングに有用。

In the global GX context

Globally, CCS deployment is accelerating, and legacy well integrity is a key containment risk. This screening tool supports tiered workflows, helping operators prioritize wells for detailed simulation, aligning with best practices for CO2 storage site selection and risk management.

👥 読者別の含意

🔬研究者:Provides a computationally efficient method for bounding CO2 leakage fluxes, useful for risk assessment and uncertainty quantification in CCS.

🏢実務担当者:Enables rapid screening of legacy well inventories to prioritize detailed assessments, supporting CCS project feasibility and permitting.

🏛政策担当者:Highlights the importance of legacy well integrity in CCS regulation and the need for efficient screening tools in storage site approval.

📄 Abstract(原文)

Achieving net-zero targets demands the rapid expansion of CO2 geological storage, but legacy wells in mature petroleum provinces represent a significant containment risk when engineered barriers degrade. At the early assessment stage, storage projects need to rapidly bound plausible leakage magnitudes across large legacy-well inventories when integrity data are sparse; however, suitable modelling approaches remain limited. High-fidelity simulations capture leakage physics in detail but are computationally demanding, while simpler analytical models neglect depth-dependent CO2 property variations arising from realistic pressure–temperature gradients. We present QWellRATE, a simplified physics-based screening model for rapidly estimating upper-bound steady-state CO2 leakage fluxes along legacy wellbore pathways. The model evaluates depth-dependent CO2 density and viscosity using the Span–Wagner equation of state, represents leakage pathways as an equivalent Darcy continuum, and operates on readily available inputs without requiring coupled reservoir simulation. Predicted flux magnitudes are consistent with published field analogues from abandoned wells across configurations ranging from intact multi-plug systems to unplugged pathways. Cement plug permeability is the primary control on leakage flux: two intact cement plugs reduce flux by approximately 760-fold relative to an unplugged well, while simultaneous degradation of all three plugs produces an approximately 500-fold increase. The model’s computational efficiency enables sensitivity analysis, uncertainty quantification, and regional spatial screening. Uncertainty quantification via Latin Hypercube Sampling yields a five-order-of-magnitude flux range driven by multiplicative interactions between degraded cement and elevated mud permeability. Regional demonstration in the Southern North Sea illustrates how QWellRATE can support tiered workflows by prioritising legacy wells for targeted follow-up using high-fidelity multiphase simulators.

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。

gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。