Carbon Capture Utilization and Trapping Efficiency Modelling: A Case Study of Gas Flare Site in Niger Delta
炭素回収・利用・貯留のトラップ効率モデリング：ニジェールデルタのガスフレアサイトを事例として (AI 翻訳)
Okon Udo Frank, Julius U. Akpabio, Aniefiok Livinus
🤖 gxceed AI 要約
日本語
本研究は、AI（ANNおよびSVR）を用いてCO2トラップ効率を高精度に予測するモデルを開発。ニジェールデルタのガスフレアサイトの260データで検証し、ANNモデルはR2=0.9993と優れた性能を示した。パラメータ重要度ではCO2質量分率が最大の影響を持つ。これにより、CCSプロジェクト初期の複雑なシミュレーションを不要にし、リアルタイムの現場判断を可能にする。
English
This study develops AI models (ANN and SVR) to predict CO2 trapping efficiency with high accuracy, validated on 260 data points from a gas flare site in the Niger Delta. The ANN model achieved R2=0.9993, and CO2 mass fraction was the most influential parameter. The models enable real-time field decisions, eliminating complex simulations in early CCS projects.
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の実用化が進む中、AIによる迅速なトラップ効率評価は、貯留層選定やモニタリングの効率化に貢献し得る。SSBJ開示におけるCCS関連の定量的評価にも応用可能。
In the global GX context
Globally, this work demonstrates the value of AI/ML in CCS site assessment, aligning with the growing need for cost-effective and scalable carbon capture solutions. It provides a practical tool for early-stage project screening, supporting climate disclosure and transition finance.
👥 読者別の含意
🔬研究者:AI/ML手法をCCS評価に適用する具体的事例として、モデル構築とパラメータ重要度分析の方法論が参考になる。
🏢実務担当者:CCSプロジェクトの初期評価や現場でのリアルタイム判断に活用できる予測モデルを提供。
🏛政策担当者:CCS推進政策の効果的な実施に向け、AI活用による評価コスト削減の可能性を示す。
📄 Abstract（原文）
Most significant challenges in recent decades have been the rise in global average temperature, which is mostly caused by greenhouse gas (GHG) emissions. Previous research used complex numerical simulations processes that uses iterative procedures to obtain a certain operating condition, which are time-consuming, tiresome, and require expensive devices that are limited in locations where real-time decisions are required. These methods do not produce models that are flexible, generalizable, accurate, or robust. Smart digital technology concepts such as artificial intelligence (AI) and machine learning (ML) are becoming increasingly popular and are being used in a variety of applications including carbon capture. This study primarily aims to create models for predicting CO2 trapping efficiency using AI such as artificial neural network (ANN) and support vector regression (SVR) to address these problems. The models were built using 260 CO2 trapping efficiency data from a gas flare site in the Niger Delta. The performance of the developed models was analysed using the statistical metrics. The goodness of fit (R2), mean square error (MSE), root mean square error (RMSE), and average percentage relative error (APRE) were 0.9993, 9.0×10–4, 9.53×10–3 and 0.18282 for the ANN model, whereas for the SVR model, the R2, MSE, RMSE, and APRE were 0.9766, 3.095×10–3, 5.563×10–2 and 1.4137 respectively. The parametric importance results for CO2 trapping efficiency show that while CO2 mass fraction had the greatest influence (29.70%), temperature, density, emission rate, pressure and activity rate followed with contributions of 17.29%, 12.41%, 11.30%, 11.06%, and 10.58%, respectively, while time had the least effect of 7.62%. The models were explicitly provided to make them easy to implement into software programmes. The explicitness, accuracy, and suggestion for using the models in the field are among the features of the models given in this study for which uniqueness is claimed. The proposed models would eliminate the need for complicated and time-consuming reservoir simulation at the early stage of the Carbon capture and storage (CCS) project, allowing for real-time findings in the field.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.56201/ijemt.vol.12.no7.2026.pg92.115first seen 2026-08-13 05:07:08
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