gxceed
← 論文一覧に戻る

CO2-Assisted Cold Production: A Sustainable Pathway Achieving Heavy Oil Recovery and Carbon Neutrality

CO2支援低温採収法:重質油回収とカーボンニュートラルを実現する持続可能な経路 (AI 翻訳)

Yao Wang, Liguo Zhong, Aiwu Yuan, Yixin Fu, Zhou Yang, Yanli Liu

📚 査読済 / ジャーナル2026-05-18#CCUSOrigin: CN
DOI: 10.2118/232583-ms
原典: https://doi.org/10.2118/232583-ms

🤖 gxceed AI 要約

日本語

本研究は、CO2と界面活性剤の複合システムを用いた低温採収法により、重質油の効率的回収と炭素隔離を実現。52坑井のフィールド試験で日産量66.7%増加、11,700トンのCO2貯留、投入産出比1:5.1の高い経済性を達成した。多パラメータ最適化から大規模展開までの技術チェーンを構築。

English

This study integrates a surfactant-CO2 composite system for heavy oil recovery with carbon sequestration. Field trials in 52 wells achieved a 66.7% increase in daily oil production, 11,700 tons of CO2 storage, and an input-output ratio of 1:5.1, far surpassing conventional thermal methods. It provides a complete technical chain from reservoir screening to field deployment, offering a replicable low-carbon development model.

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

本論文は中国の油田での実証だが、CO2-EORとCCSの統合モデルは、日本が推進するCCS事業やグリーン成長戦略における技術選択肢の参考となる。特に、高い水油比の油層への適用可能性は、日本国内の重質油資源の活用にも示唆を与える。

In the global GX context

This paper demonstrates a scalable CO2-EOR with carbon storage in high-water-cut heavy oil reservoirs, achieving both enhanced oil recovery and significant carbon sequestration. The integrated approach from laboratory to field deployment provides a practical pathway for the oil industry to align with net-zero goals, informing global CCUS deployment strategies and carbon accounting frameworks.

👥 読者別の含意

🔬研究者:Provides a multi-parameter optimization framework and field validation for CO2-assisted cold production, advancing CCUS research in heavy oil reservoirs.

🏢実務担当者:Offers a proven technical chain for CO2-EOR with carbon storage, including injection parameters and field protocols, applicable for oil companies seeking low-carbon development.

🏛政策担当者:Highlights the potential of CO2-EOR as a carbon utilization and storage mechanism, supporting policy development for CCS incentives and carbon credits.

📄 Abstract(原文)

Abstract This study addresses the critical need for green and efficient heavy oil development, targeting the technical limitations of high energy consumption in conventional thermal recovery and low recovery efficiency in traditional cold production. For the first time, this research integrates a surfactant-CO2 composite system based on the dual liquid film mechanism, multi-reservoir 3D numerical simulation, and field trials across 52 wells to construct a complete technical chain from laboratory to field application for CO2-assisted cold production. First, reservoir applicability analysis was conducted to clarify the geological and fluid boundary conditions, providing scientific criteria for target reservoir screening. Second, the developed composite system effectively resolves the critical issue of foamy oil instability and significantly enhances displacement efficiency, with the oil-water interfacial tension reduced to 13.87 mN/m and the ultimate recovery rate increased to 40.5%. Third, 3D numerical simulation enabled precise optimization of injection parameters for three typical reservoir types, establishing differentiated technical protocols for various conditions. Field trials in high-water-cut heavy oil reservoirs achieved a 66.7% increase in daily oil production per well and large-scale carbon sequestration of 11,700 tons, with an input-output ratio of 1:5.1, which is far superior to the thermal expansion method (the benchmark method) with an input-output ratio of only 1:1.8. This research fills the gap in multi-parameter coupled optimization and large-scale application of CO2-assisted cold production technology, and forms a complete technical chain from reservoir screening, composite CO2-surfactant design, numerical optimization to 52-well field deployment, providing a replicable and scalable low-carbon development model for high-water-cut heavy oil reservoirs.

🔗 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。