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Society of Exploration Geophysicists (SEG)

Modelling physical properties of rocks based on microstructure derived from X-ray microtomographic images (known as digital rock physics) is an important technology in geophysical rock characterization. However, these images are most commonly obtained at room pressure and temperature conditions. Consequently, most digital rock physics models are not representative of the rocks at depth. Reservoir rocks are at such depth that they experience high stresses and temperatures. The thermodynamic properties of the fluids inside the reservoir are pressure and temperature dependent, therefore transport properties are also temperature and pressure dependent. Moreover, it is well established that elastic rock properties of rocks are strongly affected by stress and/or fluid distribution. Thus, in order to acquire realistic pore network structures and fluid distributions (including, but not limited to, residual saturation) and to reliably estimate transport and elastic properties from micro images, rocks with fluids inside have to be imaged at reservoir pressure and temperature conditions. In this lecture, we will discuss the ways how to obtain 3D images under elevated temperature and stress conditions, as well the challenges with the imaging and further image processing. Finally, we will provide some results to demonstrate how microstructure of the rocks can be link to transport and elastic properties of rocks measured on bigger samples. The lecture maybe useful to rock physicist, petrophysics, and reservoir engineers.

Society of Exploration Geophysicists (SEG)

GPRPy is a free and open source ground penetrating radar processing and visualization software. It is compatible with GPR data collected using Sensors&Software, GSSI, and Mala GPR systems. GPRPy can also read ENVI BSQ data files. Other GPR systems may be supported as well if the data can be exported as a binary file with a separate textfile header. GPRPy has three major components: (1) profile, (2) CMP / WARR, and (3) interpolation of profiles to create 3D data cubes. The first two components come with a graphical user interface. The third component requires scripting. One of the main features of GPRPy is that it can automatically create processing scripts from the graphical user interface. These processing scripts contain all steps carried out by the user in the graphical user interface. They can be executed directly from the command line. This allows for reproducible research and creating batch jobs to process large numbers of similar profiles, such as for interpolating 3D profiles. GPRPy is built on the programming language Python and can thus be installed on any Python-compatible operating system including Windows, Mac, and Linux.

Society of Exploration Geophysicists (SEG)

Stress field of the Earth's crust has been studied for many decades as a part of seismology and geomechanics. Traditional stress inversion methods utilize focal mechanisms of earthquakes as this is typically the only data available at regional scale and lower crust. With earthquake mechanisms, it's possible to reconstruct principal stress directions and a ratio of principal stress magnitudes, but not the full stress tensor. However, a combination of data from reservoir injections and focal mechanisms from induced microseismicity overcomes this limitation. Various reservoir data, such as the temporal dependency of injection pressure, density logs, and in situ stress measurements, complement microseismic data and allow to reconstruct the full stress tensor: stress orientation and principal stress magnitudes. A joint-inversion technique that combines different data from various depths may also account for stress variations with depth and reconstruct vertical stress gradient tensor. Ultimately, the full stress tensor together with the focal mechanisms provide an estimate of pore pressure at the location of every event, which can be mapped in time and space. I will show how we applied joint stress inversion to a geothermal and unconventional dataset and illustrate how microseismicity can be used to map pore pressure.

Modelling physical properties of rocks based on microstructure derived from X-ray microtomographic images (known as digital rock physics) is an important technology in geophysical rock characterization. However, these images are most commonly obtained at room pressure and temperature conditions. Consequently, most digital rock physics models are not representative of the rocks at depth.

In this lecture, we will discuss the ways how to obtain 3D images under elevated temperature and stress conditions, as well the challenges with the imaging and further image processing. Finally, we will provide some results to demonstrate how microstructure of the rocks can be link to transport and elastic properties of rocks measured on bigger samples.

The American Geosciences Institute (AGI)

Earth Science Week 2023 will explore established and emerging technologies and techniques used by geoscientists as they play a growing role in society through developing stable infrastructure; fostering sustainable water, mineral and energy resources; analyzing and applying data about the Earth; and keeping people safe from natural hazards. The celebration also will highlight the wide range of fascinating and rewarding careers in the geosciences, and the ways that those careers are vital to understanding, monitoring, and living on the planet.

Society of Exploration Geophysicists

This lecture is based on Australian CCS projects, such as CO2CRC Otway Project and CSIRO In-situ Lab Project, which showcase evolution of the seismic monitoring technology from conventional land 4D seismic to continuous or on demand monitoring using permanent downhole and near-surface geophone and distributed acoustic sensing (DAS) arrays. We discuss how monitoring objectives can be achieved using various acquisition geometries, including land 4D, 4D vertical seismic profiling (VSP), and offset VSP, all of which can be implemented using conventional and permanently mounted seismic sources. Also covered is automation of data acquisition and analysis, as well as passive data analysis.