Geostatistical concepts for regional pore pressure mapping and prediction

Abstract

Geostatistical concepts play a pivotal role in regional pore pressure mapping and prediction, offering advanced methodologies to address the spatial variability and uncertainty inherent in subsurface formations. This abstract explores the integration of geostatistical techniques for enhancing the accuracy and reliability of pore pressure predictions over large geological regions. Accurate pore pressure prediction is critical in the oil and gas industry, particularly for optimizing drilling operations and ensuring wellbore stability. Traditional methods, often limited by their reliance on sparse data and simplified models, can struggle to capture the complex spatial patterns of pore pressure distribution. Geostatistics provides a robust framework for addressing these limitations by leveraging spatial data analysis and probabilistic modeling techniques. Key geostatistical methods such as kriging, co-kriging, and stochastic simulation are employed to create high-resolution regional pore pressure maps. Kriging, a geostatistical interpolation technique, allows for the prediction of pore pressure at unsampled locations by utilizing the spatial correlation structure of the available data. Co-kriging extends this approach by incorporating secondary variables, such as seismic attributes and well log data, to improve prediction accuracy in areas with sparse primary data. Stochastic simulation generates multiple realizations of pore pressure distribution, providing a quantifiable measure of uncertainty and enabling risk assessment for drilling operations. The integration of seismic attributes and well log data through geostatistical methods enhances the spatial resolution and reliability of pore pressure models. This combined approach not only captures the heterogeneity of subsurface formations but also accounts for the varying scales of data sources, leading to more accurate and robust predictions. Several case studies illustrate the application of geostatistical techniques in regional pore pressure mapping. These studies highlight the improved accuracy and reduced uncertainty in pore pressure predictions, leading to more informed decision-making in drilling operations and enhanced wellbore stability. In conclusion, geostatistical concepts offer significant advancements in regional pore pressure mapping and prediction. By integrating diverse data sources and employing sophisticated spatial modeling techniques, geostatistics provides a comprehensive approach to addressing the challenges of pore pressure prediction in complex geological settings. This integration ultimately enhances operational safety, efficiency, and economic viability in the oil and gas industry. Continued research and development in geostatistical methods are essential for further improving pore pressure prediction capabilities and addressing emerging challenges in subsurface exploration.