Petrophysical and mechanical rock properties are essential for reservoir characterization of the deep subsurface and are commonly used for the population of numerical models or the interpretation of geophysical data. The database presented here aims at providing easily accessible information on rock properties and chemical analyses complemented by extensive metadata (location, stratigraphy, petrography) covering volcanic, sedimentary, metamorphic and igneous rocks from Jurassic to Holocene age.

Fluids (like water or gases) within the Earth's crust often flow and interact with rock through fractures. The efficiency with which these fluids may flow through this void space is controlled by the width of the fracture(s). In this study, three different physical methods to measure fracture width were applied and compared and their predictive accuracy was evaluated. As a result, the mobile methods tested may well be applied in the field if a number of limitations and requirements are observed.

Petrophysical properties are key to populating numerical models of subsurface process simulations and the interpretation of many geophysical exploration methods. The P³ database presented here aims at providing easily accessible, peer-reviewed information on physical rock properties in one single compilation. The uniqueness of P³ emerges from its coverage and metadata structure. Each measured value is complemented by the corresponding location, petrography, stratigraphy and original reference.

We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.

The presented study is conducted within the scope of the joint research project "Hessen 3D 2.0" (BMWI-FKZ: 0325944) and aims at assessing the hydrothermal potential of the Pechelbronn Group for direct heat use by means of an integrated 3-D structural-geothermal model that serves to locate potential exploration areas. The assessment is based on reservoir temperature, (net)thickness of the reservoir horizon as well as on petrophysical, thermal and hydraulic rock properties.

This study focuses on the assessment of the geothermal potential of two extensive upper Devonian aquifer systems within the Alberta Basin (Canada). Our work provides a first database on geothermal rock properties combined with detailed facies analysis (outcrop and core samples), enabling the identification of preferred zones in the reservoir and thus allowing for a more reliable reservoir prediction. This approach forms the basis for upcoming reservoir studies with a focus on 3-D modelling.

Machine learning techniques are a promising alternative for processing (phase segmentation) of 3-D X-ray computer tomographic rock images. Here the performance and accuracy of different machine learning techniques are tested. The aim is to classify pore space, rock grains and matrix of four distinct rock samples. The porosity obtained based on the segmented XCT images is cross-validated with laboratory measurements. Accuracies of the different methods are discussed and recommendations proposed.