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