EMRP1.16

We measured both drained (dry and saturated) and undrained elastic and poro-elastic moduli in a sample of thermally cracked Westerly granite. Measurements were made at nominal effective confining pressures of 30 MPa and 100 MPa. Differential stress was also applied to the sample in order to induce increasing anisotropy (transverse isotropy). Radial and axial stress were independently cycled at increasing levels of differential stress to enable the measurement of anisotropic moduli in a sample with increasing anisotropy. Measurements were first conducted under dry conditions in order to obtain the drained dynamic moduli before the sample was saturated with water. We used newly developed, miniature differential pressure transducers were located directly around the sample surface, which allowed for direct measurement of Skempton's radial coefficient Bx and axial coefficient Bz. Wavespeeds measured on the dry sample were inverted to obtain the compliances Cijkl. The coefficients Bx and Bz were then calculated indirectly from the compliances using poro-elasticy theory for comparison with the direct measurements. Our results show that the values of Bz calculated from the compliances compare well with those measured directly, but that the calculated values of Bx significantly overestimate those measured directly, particularly at an effective pressure of 30 MPa. The discrepancy between the direct measurements and those obtained from drained moduli is likely due to nonlinear elastic effects, such as crack closure and opening, which occur during the small but finite pressure and stress steps.

How to cite: Elsigood, B., Brantut, N., Meredith, P., Mitchell, T., and Healy, D.: Measurements of anisotropic poroelastic moduli in Westerly granite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11531, https://doi.org/10.5194/egusphere-egu21-11531, 2021.

Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.

 AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.

Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.

How to cite: Garia, S., Pal, A. K., Ravi, K., and Nair, A. M.: Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11824, https://doi.org/10.5194/egusphere-egu21-11824, 2021.

The ability to detect precursors of dynamic failure in brittle rocks has key implications for hazard forecasting at the field scale. In recent years, laboratory scale rock deformation experiments are providing a wealth of information on the physics of the fracture process ranging from fracture nucleation, crack growth and damage accumulation, to crack coalescence and strain localization. Parametric analysis of laboratory Acoustic Emission (AE) data has revealed periodic trends and precursory behaviour of the rupture source mechanisms as a fault zone enucleates and develops, suggesting these processes are somehow repeatable and forecastable. However, due to the inherent anisotropy of rock media and the range of environmental conditions in which deformation occurs, finding full consistency between AE datasets and a prediction of rupture mechanisms from AE analysis is still an open goal. Here we apply a Time Delay Neural Network (TDNN) to Acoustic Emission (AE) sets recorded during conventional triaxial rock deformation tests. We forecast the Time-to-Failure using the discrete, non-continuous timeseries of AE rate, amplitude, focal mechanism and forward scattering properties. 4x10 cm samples of Alzo granite, a homogeneous medium-grained plutonic rock from NW Italy with an initial porosity as low as 0.72%, were triaxially deformed at strain rates of 3.6mm/hr under dry conditions until dynamic failure at confining pressures of 5, 10, 20 and 40 MPa respectively. Each sample was positioned inside an engineered rubber jacket fitted with ports where an array of twelve 1 MHz single-component Piezo-Electric Transducers were embedded, allowing to record AE during the experimentation. Several parameters were considered for the TDNN training: AE rate, deformation stages prior failure (elasticity, inelasticity and coalescence), AE amplitude, source mechanisms and scattering. All these parameters are key indicators of the evolving damage in the medium. Our training input consists of simplified timeseries of the previously discussed AE parameters from the experiments carried out at the lowest confining pressure (5 MPa). The inputs are classified as the stress-until failure and strain-until-failure for each AE. Once trained we then simulate the model on the untrained datasets to test it as a forecasting tool at higher confinements. At each step the model is simulated on AE data from the previous 0.2% of strain. At 10 MPa we observe a reliable forecast of failure that starts with the anelastic phase and becomes more accurate during strain-softening. At higher confining pressure, an increased limit of forecasting the solution is observed and interpreted with more complexity in the coalescence process. Despite these limitations, the model shows that when trained even on a limited input it is able to forecast dynamic failure in unseen data with surprising accuracy. Future studies should investigate AE spatial distribution for the TDNN training.

How to cite: Vinciguerra, S., King, T., and Benson, P.: Using AE based Machine Learning Approaches to Forecast Rupture during Rock Deformation Laboratory Experiments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12261, https://doi.org/10.5194/egusphere-egu21-12261, 2021.

The combination of electrical and elastic measurements can be useful in lowering subsurface characterisation uncertainty. The majority of rock physics relations which relate a rock’s electrical and elastic properties, however, rely on the estimation of porosity as an intermediate step. By combining differential effective medium schemes which relate a rock’s electrical and elastic properties to porosity and pore shape, we obtain cross-property expressions which are independent of porosity, depending only on pore aspect ratio. Analysing published joint electrical-elastic measurements shows the cross-property model works well for clean sandstones, and models V_p/V_s ratios as a function of resistivity without porosity. Although clay-bearing sandstones are more complex, our model can still identify the correct trends. On theoretical grounds, it seems our approach has the potential to produce additional cross-property relations; a topic upon which we speculate.

How to cite: Cilli, P. and Chapman, M.: Electrical-elastic modelling of rocks using cross-property DEM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12490, https://doi.org/10.5194/egusphere-egu21-12490, 2021.

In any domain involving some stressed solids, that is, from seismology to rock physics or general engineering, the strength of matter is a paramount feature to understand.  The global failure of a mechanically loaded solid is usually dictated by the growth of its internal micro-cracks and dislocations. When this growth is rather smooth and distributed, the solid is considered to be in ductile condition. Alternatively, an abrupt propagation of localized defects leads to a brittle rupture of the full matrix.
It is then critical to understand what the physics and dynamics of isolated cracks are, when their tips are loaded at a given stress level. While the general elasticity theory predicts such stress to diverge, it is  acknowledged that some area around the crack fronts is rather plastic. In other words, some dissipation of mechanical energy, in a so-called process zone around a crack tip, prevents the - unphysical - stress divergence and shields the fronts from excessive load levels.

In this work, we focus on the local Joule heating, that significantly contributes to the energy dissipation. Analysing experimental data of the rupture of many materials, we indeed show that the scale for the thermal release around crack tips explains why the toughness of different media spans over orders of magnitude (we analysed materials spanning over 5 decades of energy release rate), whereas the covalent energy to separate two atoms does not.

We here discuss the ability of this simple thermally activated sub-critical model, which includes the auto-induced thermal evolution of crack stips [1], to predict the catastrophic failure of a vast range of materials [2]. It is in particular shown that the intrinsic surface energy barrier, for breaking the atomic bonds of many solids, can be easily deduced from the slow creeping dynamics of a crack. This intrinsic barrier is however higher than the macroscopic load threshold at which brittle matter brutally fails, possibly as a result of thermal activation and of a thermal weakening mechanism. We propose a novel method to compute the macroscopic critical energy release rate of rupture, Gc macroscopic, solely from monitoring slow creep, and show that this reproduces the experimental values within 50% accuracy over twenty different materials (such as glass, rocks, polymers, metals), and over more than four decades of fracture energy. We also infer the characteristic energy of rupturing bonds, and the size of an intense heat source zone around crack tips, and show that it scales as the classic process zone size, but is significantly (10⁵ to 10⁷ times) smaller.

References:

[2] Vincent-Dospital, T., Toussaint, R., Cochard, A., Flekkøy, E. G., & Måløy, K. J. (2020). Is breaking through matter a hot matter? A material failure prediction by monitoring creep. arXiv preprint arXiv:2007.04866.  https://arxiv.org/abs/2007.04866

How to cite: Toussaint, R., Vincent-Dospital, T., Cochard, A., Flekkøy, E. G., and Måløy, K. J.: Is breaking through matter a hot matter? How to predict material failure by monitoring creep, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15915, https://doi.org/10.5194/egusphere-egu21-15915, 2021.