Efficient integration of multiscale image and petrophysical data is becoming increasingly important to tackle emerging reservoir characterization challenges associated with complex carbonate and unconventional reservoirs. The scope of projects in this theme includes developing novel fit-for-for purpose tools and protocols that are scalable to large reservoir data sets consisting of many wells, for accurate characterization of complex carbonate and shale reservoirs, which are becoming increasingly reliant on high resolution imaging techniques for pore space characterization.
Microporosity is by volume the most important porosity type in the giant carbonate reservoirs of Arabia with an estimated percentage of above 60%. With much of the historic production having drained the macro-pores, most of the remaining oil resides in micro-pores. Future production and maintenance of production profiles will hence heavily rely on successfully draining oil from micropores. Despite the economic significance of microporosity, the origin and diagenesis of the carbonate micro-crystals that define this porosity are poorly understood. Moreover, the impact of the variable types and distributions of microporosity on enhanced oil recovery is not well known. The scope of this project includes visualization and classification of the microporosity types in the Jurassic and Cretaceous reservoirs of Saudi Arabia with due consideration to the origin and evolution of the host micrite and study their impact on multi-phase fluid flow.
This project explores the application of image-based machine learning and deep learning tools to improve the prediction of multi-scale geological features. The scope of this projects includes identification and characterization of reservoir rock pore space using a wide array of multi-scale image data obtained from thin sections and core plugs using optical and electron microscopy imaging and x-ray CT scanning techniques.
Integration of multi-scale image and petrophysical data analysis methods for reservoir characterization and rock-typing
In this project we aim to develop fit-for-purpose data analysis methods that integrate multi-scale petrophysical and image data to obtain reservoir rock types that address the crucial geological-petrophysical heterogeneities critically impacting hydrocarbon production and enhanced recovery. The scope of research includes addressing the representative elementary volume (REV) issues in carbonates and upscaling sub-grid scale heterogeneities that are key contributors to flow from pore- to core- to reservoir grid-block scale.
KAUST: Hussein Hoteit, Tad Patzek
Heriot-Watt University: Sebastian Geiger, Hanna Menke
Imperial College London: Prof. Matt Jackson, Dr. Carl Jacquemyn
Industrial collaborators: Aramco, ThermoFisher, Tescan-XRE