Integrated analysis of wireline logs analysis, seismic interpretation, and machine learning for reservoir characterisation: Insights from the late Eocene McKee Formation, onshore Taranaki Basin, New Zealand

Abstract

This study aims to integrate seismic and well data for re-assessment and delineating the geological and petrophysical characteristics of the Late Eocene McKee Formation. Despite the McKee Formation being a hydrocarbon-producing field, there have been no previous integrated studies conducted to characterize its deposits in the Taranaki Basin. To execute these tasks, the process involves the interpretation of seismic data, wireline analysis, and 3D porosity volume prediction using multi-layered feed-forward neural network models. The seismic data was interpreted and found to contain low displacement faults due to intricate geometric complexities, the contour map that was generated shows lateral extension and thickness variation, and the region of direct hydrocarbon indicators was detected using the root-mean-square. Additionally, the seismic inversion provides a comprehensive understanding of the acoustic impedance variations from low to high value, which signifies the presence of mudstone clasts content. Furthermore, in the wireline log analysis, the reservoir was found to be a Silici-clastic and carbonate field owing to the presence of dolomite, calcite, and quartz. After applying cutoffs, petrophysical parameters were evaluated. The estimated values for effective porosity, reservoir thickness, shale volume, and water and hydrocarbon saturation were 15.04 % to 17.29 %, 85.44 m to 122.41 m, 8.99 to 14.87 % and 12.97–27.10 %, and 72.9 % to 87.03 %, respectively. The outcome of this extensive research aids in accurately characterizing this region for the future prospectively of the field. This integrated study can be applied and adapted to other basins in New Zealand and the southwestern Pacific. The proposed workflow is also suitable for analogous scenarios in various basins worldwide.

Keywords

• Seismic inversion, Porosity
• Neural network model
• McKee Formation
• Kapuni field
• Taranaki Basin