Fluid-driven Cyclic Reorganisation in Shallow Basaltic Fault Zones

Faults represent a critical heterogeneity in basaltic sequences, yet their architectural and hydromechanical evolution is poorly constrained. We present a detailed multi-scale characterisation of passively exhumed fault zones from the layered basalts of the Faroe Islands, which reveals cyclic stages of fault evolution. Outcrop-scale structures and fault rock distribution within the fault zones were mapped in the field and in 3D virtual outcrop models, with detailed characterisation of fault rock microstructure obtained from optical and SE-microscopy. The fault zones record localisation from decametre-wide Riedel shear zones into metre-wide fault cores, containing multiple cataclastic shear bands and low strain lenses organised around a central principal slip zone (PSZ). Shear bands and the PSZ consist of (ultra-) cataclasites with a zeolite-smectite assemblage replacing the original plagioclase-pyroxene host rock composition. Low-strain lenses are hydrothermal breccias of weakly altered host rock, or reworked fault rocks. PSZ-proximal zones show significant late-stage dilatation in the form of hydrothermal breccias or tabular veins with up to decimetre apertures. We interpret these structures as evolving from alternating shear-compaction and dilation through hydrofracture. The fault core preserves PSZ reworking, evidencing repeated shear zone locking and migration. The alternating deformation styles of shear compaction and dilatation suggest episodic changes in deformation mechanisms driven by transient overpressure and release. The fault zone mechanical properties are thus governed by the combined effects of permanent chemical weakening and transient fluid-mediated mechanical weakening, alternating with cementation and healing.

Application of CO2-water-rock reaction transport simulation in NPs-CO2 flooding and storage

As a hot issue in geological engineering, CO2 flooding and sequestration still face many challenges. Injection of nanoparticles into CO2 can improve the injectability and effective reserves of CO2. However, the migration law of the mixed fluid of CO2 and nanoparticles (NPs-CO2) in the reservoir under the condition of chemical reaction is still unclear. Based on chemical reaction kinetics, a mass transfer model of NPs-CO2 nanofluid in reservoir is established by combining the micro-pore structure change of porous media under CO2-water-rock reactions condition and the migration law of NPs-CO2 fluid. The geochemical reaction process between CO2 and reservoir and the influence of heterogeneity caused by rock microstructure on the miscibility and migration of NPs-CO2 brine fluid are simulated. The results show that the CO2-water-rock reaction increases the heterogeneity of reservoir, and the porosity and permeability are rising as a whole; the increase of reservoir heterogeneity caused by chemical reaction can makes the migration of NPs-CO2 selective. The local accumulation of NPs-CO2 in the unconnected pores will weaken the original oil displacement efficiency to some extent; in the process of CO2 sequestration, the density difference between NPs-CO2 and formation water can not only promote the miscibility of NPs-CO2-brine fluid, but also inhibit the acid fluid under buoyancy. The upward diffusion is moved to the cover layer to prevent the chemical reaction of the rocks in the cap layer, so as ensuring the permanent storage of greenhouse gases.

Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

Cullet-Filled Concrete

The paper considers how cullet of different particle-size distribution affects the concrete strength. Experiments have proven that large-particle cullet (1.25 cm or larger) could be used as an aggregate; the concrete strength will be on par with those of ordinary natural/crushed sand concrete. The paper proves the feasibility of injecting highly dispersed silica fume in combination with effective polycarboxylate-based superplasticizers in cullet-based concrete mixtures. Highly dispersed silica fume will positively affect the strength characteristics of concrete, as silica fume in cement rock reacts with Са (ОН)2, which is released upon the hydration of the clinker minerals С3S and С2S; the reaction produces very strong compounds. Concretes containing up to 30% silica fume in combination with a superplasticizer will feature very high early strength. Use of strong aggregates with a 30% cullet content can produce strong concretes; after steamed, a concrete containing silica fume and polycarboxylate-based superplasticizer will reach 90% of the graded strength. Cement-rock microstructure studies show that the polymer component of the STACHEMENT 2280 superplasticizer will gradually transcend from the glass grains to the cement rock. The interface between the polymer-coated glass grains and the cement rock is blurred and barely present. This strengthens the glass-rock adhesion and improves the concrete strength. This is why cullet is recommended for use in the production of curb stones.

On the role of microgeometry in conductivity substitution

Conductivity substitution is the process of predicting the change in the effective electrical conductivity of a rock upon a change in conductivity of the mineral or fluid phase. Conductivity substitution is nonunique — only a range of conductivities can be predicted from knowledge of the initial effective conductivity, the porosity, and the initial and final compositions. The precise change depends strongly on the rock microstructure, which is seldom adequately known. Rigorous bounds on the change in effective conductivity upon changes in the phase conductivities for two-phase isotropic composites are used to gain insights into the roles of microgeometry and phase conductivity contrast. When the conductivity contrast between phases is high, the conductivity substitution predicted by Archie’s law corresponds approximately to the upper bound on the change of conductivity upon substitution. Inclusion modeling suggests that vuggy, highly tortuous, or partially disconnected pore space could account for conductivity changes smaller than those predicted by Archie’s law. Substitution behavior computed analytically for known microgeometries correlates with measures of microgeometry, including the fraction of connected fluid phase and variance of electric field strength in each phase. Comparison of the conductivity substitution bounds with brine-saturated sandstone data reveals that the position of measured data with respect to the conductivity substitution bounds can be indicative of the effective clay content. The bounds provide a template for better prediction of effective conductivity if we have at least some knowledge of the pore microstructure. Similarly, multiple conductivity measurements on the same rock might be used to extract more information about the rock and pore space properties than is possible with only a single measurement.

Multi‐scale simulation of rock compaction through breakage models with microstructure evolution

Regional subsidence due to fluid depletion includes the interaction among multiple physical processes. Specifically, rock compaction is governed by coupled hydro-mechanical feedbacks involving fluid flow, effective stress change and pore collapse. Although poroelastic models are often used to explain the delay between depletion and subsidence, recent evidence indicates that inelastic effects could alter the rock microstructure, thus exacerbating coupling effects. Here, a constitutive law built within the framework of Breakage Mechanics is proposed to account for the inherent connection between rock microstructure, hydraulic conductivity, and pore compaction. Furthermore, it is embedded into a 1-D hydromechanical coupled finite element analysis (FEA) to explore the effects of micro-structure rearrangement on the development of reservoir compaction. Numerical examples with the proposed model are compared with simulations under constant hydraulic conductivity to illustrate the model capability to capture the non-linear processes of reservoir compaction induced by fluid depletion.