Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Abstract

Estimation of petrophysical properties holds significant importance in evaluating the feasibility of hydrocarbon extraction and enhancing production efficiency. Therefore, theintegration of seismic data with well data for reservoir characterization has garnered significant attention in the oil and gas industry. This approach provides a comprehensive view of the lateral variations across the entire reservoir. In this study, our goal is to establish a relationship between seismic attributes and the petrophysical properties of the reservoir, enabling the prediction of property distribution across the entire reservoir. We employed geostatistical inversion to generate detailed P-impedance maps of the reservoir, capturing its spatial heterogeneity. A total of 100 realizations were produced and ranked into P10, P50, and P90 scenarios, representing different probabilistic outcomes. The P90 scenario, considered the most representative of the reservoir's average properties, was selected for interpretation, enabling a comprehensive analysis of reservoir heterogeneities and their distribution. Subsequently, we estimated porosity using various seismic attributes, employing a Data-driven Extreme Gradient Boosting (Xgboost) approach within the reservoir analysis. This method enabled us to estimate a high-resolution acoustic impedance and porosity with minimal root mean square error of 3.8 and r2score of 66 % across the spatial gap which ranges from 8468.84 to 9367.85(g/cc) *(m/s) and 10 to 45 % between wells, revealing the lateral variation of acoustic impedance and porosity within the reservoir interval of interest. The information provided was found to be of significant value in the identification of potential sweet spots and plays a pivotal role in the development of a reservoir management strategy for future hydrocarbon exploration.

Keywords

• Reservoir characterization
• Porosity prediction
• Machine learning
• Extreme gradient boosting
• Geostatistical inversion
• McKee