Geophysical Prospecting - Volume 72, Issue 7, 2024

We adopt extreme value theory to estimate the upper limit of the next record‐breaking magnitudes of induced seismic events. The methodology is based on order statistics and does not rely on knowledge of the state of the subsurface reservoir or injection strategy. The estimation depends on the history of record‐breaking events produced by the anthropogenic activities. We apply the methodology to three different types of industrial operations: natural gas production, saltwater disposal and hydraulic fracturing. We show that the upper limit estimate provides a reliable and realistic upper bound for magnitudes of the record‐breaking events in investigated datasets including 15 publicly available datasets. The predicted magnitudes do not overestimate the observed magnitudes by more than 1.0 magnitude unit and underestimation is rare, probably resulting from insufficient sampling of the statistical distribution of the induced seismicity. The richest dataset, sourced from downhole and surface monitoring of the Preston New Road hydraulic fracturing, provides reliable estimates of the magnitudes over three orders of magnitudes with only slight underprediction of the largest observed event. While the detection of weaker events improves the performance of the method, we show that it can be applied even with a few observed record‐breaking events to provide reliable estimates of magnitudes. However, care must be taken to ensure that event catalogues are estimated consistently across a range of magnitudes.

To meet the demand for long‐distance and highly accurate detection in tunnel construction, a vibroseis source has been introduced into tunnel forward prospecting. However, serious noise in tunnels reduces the resolution of vibroseis signals, and small‐scale structure recognition in tunnels requires higher signal resolution. Thus, we develop a time‐frequency domain correlation method based on the synchrosqueezed wavelet transform for tunnel vibroseis data. Time‐frequency domain correlation may capture more information than a single time or frequency domain correlation method, and its effectiveness depends on the time‐frequency transformation method. The synchrosqueezed wavelet transform allows us to obtain high‐resolution time‐frequency spectra and thus helps to extract high‐resolution effective signals. The advantages and disadvantages of different correlation methods are highlighted using numerical examples, in which the high resolution and strong robustness of the time‐frequency domain correlation method based on the synchrosqueezed wavelet transform are demonstrated. A detailed field case study in an iron mine tunnel further demonstrates the reliability of the time‐frequency domain correlation method based on the synchrosqueezed wavelet transform for practical data.

The interlayer wave‐induced fluid flow is an important mechanism for seismic attenuation and dispersion, as well as frequency‐dependent anisotropy, in the fluid‐saturated porous layered medium. This mechanism is closely related to the medium physical properties, and thus quantifying this mechanism is of significance for the seismic inversion of medium physical properties. Although numerous models have been proposed to quantify this mechanism, most models do not consider the effects of layer intrinsic anisotropy. To solve this problem, the effective complex‐valued and frequency‐dependent stiffness coefficients are derived for the fluid‐saturated porous medium composed of periodic transversely isotropic layers. Using the derived solutions, we study the effects of layer intrinsic anisotropy on seismic dispersion and attenuation, as well as frequency‐dependent anisotropy. It has been found that different matrix or fluid property contrasts between adjacent layers lead to different effects of intrinsic anisotropy. In addition, the effects of intrinsic anisotropy are also influenced by the fluid distribution when both matrix and fluid properties contrast among adjacent layers exist. In the low‐ and high‐frequency limits of wave‐induced fluid flow, our model reduces to the previous known results, which validates the correctness of our model. Our model can be applied in the seismic inversion of physical properties of reservoirs with intrinsic anisotropy, such as shale and tight sandstone reservoirs. In addition, our model can also be extended to cases with more complex intrinsic anisotropy and, thus, can be applied to complex anisotropic fractured reservoirs in the future.

The construction of an accurate and high‐resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band‐limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre‐stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three‐dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre‐stack seismic inversion. The main innovation of the proposed method is the three‐dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S‐wave velocity well logging data.

The fractal formalisms are well known for providing new understandings regarding the geometrical, spatial, and temporal behaviour of seismicity. Particularly, the fractal dimensions give information about the seismic events self‐organization and self‐similarity. On the other hand, the Gutenberg–Richter value, known as the b‐value, has shown through the years to give handy information regarding the statistical distribution of earthquakes, on‐site physical parameters, and geomechanical inputs. The Gutenberg–Richter value (b) and the capacity and correlation fractal dimensions, (D0 and D2), of the spatial distribution of earthquake hypocentres interact mathematically for micro‐ and macro‐events. From this interaction, it is possible to obtain new insights into the fracture network development and the microseismicity source characterization in terms of single fractures, fault planes, or densely fractured volumetric spaces. Here we show this interaction for the open‐source Decatur CO2 project seismicity catalogue, comparing it with the results obtained for a natural earthquake catalogue of Illinois, in the United States. The fractal dimension D0 is calculated using two different methodologies: box‐counting and correlation integral partitioning. This last method is also used to calculate D2. The results presented in this study allow us to describe how the fracture network geometry influences the earthquake complexity. Together with the calculation of the b‐value, we present clear indications which show that seismicity recorded in the Illinois tectonic environment partially follows the Aki relationship D0 ∼ 2b, which is not the case for induced events. In addition, the induced earthquake dataset shows that D2 > D0, an anomalous behaviour in terms of the fractal formalisms. All these facts might be used to establish spatial fracture network control techniques and seismicity‐type distinctions in CO2 injection sites located in highly active tectonic areas, respectively.