Geological modeling of CO2 reservoir heterogeneity based on Deep Learning Networks

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K05396
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 31020:Earth resource engineering, Energy sciences-related
• Research Institution: Akita University
• Principal Investigator: Chiyonobu Shun 秋田大学, 国際資源学研究科, 教授 (40526430)
• Co-Investigator(Kenkyū-buntansha): 間所 洋和 岩手県立大学, ソフトウェア情報学部, 准教授 (10373218)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: CCS / 二酸化炭素地下貯留 / 地質不均質性 / セマンティックセグメンテーション / 深層学習

Outline of Final Research Achievements

Carbon capture and storage (CCS) is an epoch-making approach to reduce　greenhouse gases in the atmosphere. This study specifically examines outcrops because geological layer measurements　can lead to production of a highly accurate geological model for feasible CCS inspections. Using a digital monocular　RGB camera, we obtained outcrop images annotated with four classes along with strata. Subsequently, we compared　segmentation accuracies with changing input image sizes of three types and semantic segmentation methods of four　backbones.　Experimentally obtained results　demonstrated that data expansion with random sampling improved　the accuracy. Regarding evaluation metrics, global accuracy and local accuracy are higher than the　mean intersection over union (mIoU)　for our outcrop image dataset with unequal numbers of pixels in the respective classes. These experimentally obtained　results revealed that resizing for input images is unnecessary for our method.

Free Research Field

地球資源工学

Academic Significance and Societal Importance of the Research Achievements

地質が複雑な堆積盆を対象としたCCSのモデル構築へ向けて，実際の露頭から得られた地質学的情報を，セマンティックセグメンテーション技術を用いて数値化することができた．これは，今後本格化するCCS事業において，CO2貯留量の算定や，地下挙動予測計算において精度向上となることが確実である．また，事業化に向けた地下地質の不均質性に依存した地質リスクの顕在化にも寄与することになる．これらCCSにおける貯留層の基礎的データの公表は，事業が本格化しつつある現在は社会的意義が大きいと言える．