Subseismic pathway prediction by three-dimensional structural restoration and strain analysis based on seismic interpretation

Abstract

For both modeling and management of a reservoir, pathways to and through the seal into the overburden are of vital importance. Therefore, we suggest applying the presented structural modeling workflow that analyzes internal strain, elongation, and paleogeomorphology of the given volume. It is assumed that the magnitude of strain is a proxy for the intensity of subseismic scale fracturing. Zones of high strain may correlate with potential migration pathways. Because of the enhanced need for securing near-surface layer integrity when CO 2 storage is needed, an interpretation of three-dimensional (3-D) seismic data from the Cooperative Research Centre for Greenhouse Gas Technologies Otway site, Australia, was undertaken. The complete 3-D model was retrodeformed. Compaction-plus deformation-related strain was calculated for the whole volume. The strain distribution after 3-D restoration showed a tripartition of the study area, with the most deformation (30%-50%) in the southwest. Of 24 faults, 4 compartmentalize different zones of deformation. The paleomorphology of the seal formation is determined to tilt northward, presumably because of a much larger normal fault to the north. From horizontal extension analysis, it is evident that most deformation occurred before 66 Ma and stopped abruptly because of the production of oceanic crust in the Southern Ocean. Within

Controlling potential far‐field brine leakage from CO2 storage formations using deep extraction wells: Numerical and experimental testing

“…The latest advances in geophysics have enabled identifying the potential locations of pressure‐driven fractures in rock systems and predict their hydraulic properties by integrating seismic data with machine learning and geomechanical models 60–65 . By applying similar techniques in GCS sites, the location of the future damage zones in the caprock can be predicted, and the potential leakage rate via the fractures in these zones can be inferred.…”

“…The latest advances in geophysics have enabled identifying the potential locations of pressure-driven fractures in rock systems and predict their hydraulic properties by integrating seismic data with machine learning and geomechanical models. [60][61][62][63][64][65] By applying similar techniques in GCS sites, the location of the future damage zones in the caprock can be predicted, and the potential leakage rate via the fractures in these zones can be inferred. Therefore, in this analysis, the potential leakage sources (i.e., the injection ports in the soil tank) were predefined in the forward model as mass influx elements with an assumed injection/leakage rate of 7.9 ml/min and tracer concentration of ∼78 ppm.…”

Monitoring Brine Leakage From Deep Geologic Formations Storing Carbon Dioxide: Design Framework Validation Using Intermediate‐Scale Experiment

“…The approach investigated here also assumes that some prior information on the potential fracture and damage zone locations in the caprock, where brine leakage can occur, is available. Different geophysical-based methods were reported in the literature to identify the potential locations of the pressure-driven fractures in the top seal of confined hydrocarbon reservoirs and caprocks of carbon storage formations (Ingram & Urai, 1999;Krawczyk et al, 2015;Ligtenberg, 2005;Lohr et al, 2008;Ziesch et al, 2019). Recently, Feng et al (2018) predicted the apertures, porosities, and permeabilities of stress-induced fractures in a brittle confined reservoir with an error range of ∼11%-15%.…”

Fracture Characterisation Using 3-D Seismic Reflection Data for Advanced Deep Geothermal Exploration in the Ne German Basin

“…However, fault zones and fracture networks often bear an inherent small-scale complexity close to the seismic resolution, so that it is difficult to resolve them directly from the data. For this purpose, various options for attribute analyses e.g., coherence, curvature, RMS amplitude and instantaneous frequencies, ant-tracking analysis, spectral decomposition using wavelet transform, impedance inversion and crossplots (Gazar et al, 2011;Suo et al, 2012;Pussak et al, 2014;Marfurt, 2018;Ziesch et al, 2019;Bauer et al, 2020;Wadas and von Hartmann, 2022;Wadas et al, 2023) and numerical modelling, such as 3-D palinspastic reconstruction (LaPointe et al, 2002), exist to better explore the sub-seismic scale of fracture networks (Krawczyk et al, 2015).…”