ACS Omega, Год журнала: 2025, Номер 10(6), С. 5430 - 5448

Опубликована: Фев. 5, 2025

Establishing a potential site characterization for carbon dioxide (CO2) storage in geological formations anticipates the appropriate reservoir properties, such as porosity, permeability, and so forth. Well logs seismic data were utilized to determine key including volume of shale, water saturation. These properties cross validated with core sets ensure accuracy. To enhance permeability estimation, sophisticated machine learning (ML) methods employed, categorizing into five classes ranging from extremely good (0) very low (4). Two ML models, Naïve Bayes (NB) multilayer perceptron (MLP), applied predict permeability. The MLP model outperformed NB model, achieving 99% training accuracy 93% testing accuracy, compared 78 73%, respectively, model. resulting comprehensive revealed distribution across three stratigraphic layers: B100 zone exhibited suitable caprock, while D35-1 D35-2 zones demonstrated excellent indicating CO2 reservoirs. "X" field reservoir, located at depths exceeding 1300 m, meets depth requirements (1000–1500 m) storage. Our integrated approach, combining empirical ML-based calculations well logs, proved effective characterizing reservoir. lithological defined nonreservoir sections between clay silt lines, identifying important caprocks interbedded shale/clay intervals. Seismic profiling confirmed continuous caprock overlying D group zone, crucial preventing upward migration. This analysis supports Malay Basin viable storage, contributing ongoing efforts capture research.

Язык: Английский