Spatio-temporal sequence prediction of CO2 flooding and sequestration potential under geological and engineering uncertainties

Xinyu Zhuang, Wendong Wang, Yuliang Su, Yuan Li, Zhenxue Dai and Bin Yuan

Applied Energy, 2024, vol. 359, issue C, No S0306261924000746

Abstract: CO2 injection for subsurface hydrocarbon development not only enhances oil and gas recovery but also enables CO2 sequestration in the subsurface. It is essential to develop effective methods for evaluating the potential of CO2 flooding and sequestration. Despite the existence of methods for predicting the effectiveness of hydrocarbon development using historical production data, insufficient emphasis has been placed on adequately incorporating geological and engineering uncertainty information to enhance prediction accuracy. To address this issue, a novel spatial-temporal ResNet (ST-ResNet) model is proposed for predicting hydrocarbon production, CO2 sequestration volume and CO2 diffusion pattern in the subsurface, which represent the CO2 flooding and sequestration potential. First, high-dimensional reservoir property fields are parameterized using the combined method of principal component analysis and discrete cosine transform (PCA-DCT). Second, the spatial sequence information of various reservoir property fields is extracted with features based on residual neural network (ResNet). Then, the time series information such as dynamic well control parameters is encoded with stacked BiLSTM (SBiLSTM). Specifically, the ST-ResNet model incorporates the above modules to overcome the limitations of collaborative consideration of temporal and spatial information involved in subsurface hydrocarbon development. Comparison between simulation and prediction results on the 2D/3D reservoir model reveals a significant achievement in prediction accuracy by the ST-ResNet model (with R2 and SSIM scores of 0.947, 0.911 and 0.937, 0.922, respectively). In comparison to CNN, LSTM and their combined approach CNN-LSTM, the ST-ResNet model demonstrates an improvement of 3% to 5% in R2, along with reductions of 20% to 30% in MAE and 15% to 25% in RMSE, respectively. These results highlight the superior stability and generalization of the ST-ResNet model. The contribution of this work is to provide a more accurate and efficient prediction tool to guide the integrated development of CO2 flooding and sequestration in subsurface hydrocarbon reservoirs, which facilitates decision-making processes for engineers.

Keywords: CO2 flooding and sequestration; Spatio-temporal features; Convolution neural network; Long short-term memory network (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924000746
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:359:y:2024:i:c:s0306261924000746

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.122691

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().