Seismic technology has been used successfully to detect geomechanically induced signals in repeated seismic experiments from more than a dozen fields. To explain geomechanically induced time-lapse (4D) seismic signals, we use results from coupled reservoir and geomechanical modeling. The coupled simulation yields the 3D distribution, over time, of subsurface deformation and triaxial stress state in the reservoir and the surrounding rock. Predicted changes in triaxial stress state are then used to compute changes in anisotropic P- and S-wave velocities employing a stress sensitive rock-physics transform. We predict increasing vertical P-wave velocities inside the reservoir, accompanied by a negative change in P-wave anisotropy [Formula: see text]. Conversely, in the overburden and underburden, we have predicted a slowdown in vertical P-wave velocity and an increase in horizontal velocities. This corresponds to positive change in P-wave anisotropy [Formula: see text]. A stress sensitive rock-physics transform that predicts anisotropic velocity change from triaxial stress change offers an explanation for the apparent difference in stress sensitivity of P-wave velocity between the overburden and the reservoir. In a modeled example, the vertical velocity speedup per unit increase in vertical stress [Formula: see text] is more than twice as large in the overburden as in the reservoir. The difference is caused by the influence of the stress path [Formula: see text] (i.e., the ratio [Formula: see text] between change in minimum horizontal effective stress [Formula: see text] and change in vertical effective stress [Formula: see text]) on vertical velocity. The modeling suggests that time-lapse seismic technology has the potential to become a monitoring tool for stress path, a critical parameter in failure geomechanics.
Mudrocks, defined to be fine‐grained siliclastic sedimentary rocks such as siltstones, claystones, mudstones and shales, are often anisotropic due to lamination and microscopic alignments of clay platelets. The resulting elastic anisotropy is often non‐negligible for many applications in the earth sciences such as wellbore stability, well stimulation and seismic imaging. Anisotropic elastic properties reported in the open literature have been compiled and statistically analysed. Correlations between elastic parameters are observed, which will be useful in the typical case that limited information on a rock's elastic properties is known. For example, it is observed that the highest degree of correlation is between the horizontal elastic stiffnesses C11 and C66. The results of statistical analysis are generally consistent with prior observations. In particular, it is observed that Thomsen's ɛ and γ parameters are almost always positive, Thomsen's ɛ and γ parameters are well correlated, Thomsen's δ is most frequently small and Thomsen's ɛ is generally larger than Thomsen's δ. These observations suggest that the typical range for the elastic properties of mudrocks span a sub‐space less than the five elastic constants required to fully define a Vertical Transversel Isotropic medium. Principal component analysis confirms this and that four principal components can be used to span the space of observed elastic parameters.
We present a case study demonstrating the use of an “L”-shaped downhole fibre-optic array to monitor microseismicity. We use a relatively simple method to detect events from continuous waveform data, and develop a workflow for manual event location. Locations are defined with a cylindrical coordinate system, with the horizontal axis of the DAS cable being the axis of symmetry. Events are located using three manual “picks”, constraining (1) the zero-offset “broadside” channel to the event (2) the P-S wave arrival time difference at the broadside channel, and (3) the angle, ? of the event from the array. Because the one-component DAS array is unable to record P-wave energy on the broadside channel, the P-wave pick is made indirectly by ensuring that the modeled P- and S-wave moveout curves match the observed data. The ? angle requires that signal is observed on the vertical part of the array, in our case this is possible because an engineered fiber, rather than standard telecommunications fiber, provided a significant reduction in the noise level. Because only three picks need to be made, our manual approach is significantly more efficient than equivalent manual processing of downhole geophone data, where picks for P- and S-waves must be made for each receiver. We find that the located events define a tight cluster around the injection interval, indicating that this approach provides relatively precise and accurate event locations. A surface microseismic array was also used at this site, which detected significantly fewer events, the locations of which had significantly greater scatter than the DAS array locations. We conclude by examining some other aspects of the DAS microseismic data, including the presence of multiple events within very short time windows, and the presence of converted phases that appear to represent scattering of energy from the hydraulic fractures themselves.
This study presents the first demonstration of the transferability of a convolutional neural network (CNN) trained to detect microseismic events in one fiber-optic distributed acoustic sensing (DAS) data set to other data sets. DAS is being increasingly used for microseismic monitoring in industrial settings, and the dense spatial and temporal sampling provided by these systems produces large data volumes (approximately 650 GB/day for a 2 km long cable sampling at 2000 Hz with a spatial sampling of 1 m), requiring new processing techniques for near-real-time microseismic analysis. We have trained the CNN known as YOLOv3, an object detection algorithm, to detect microseismic events using synthetically generated waveforms with real noise superimposed. The performance of the CNN network is compared to the number of events detected using filtering and amplitude threshold (short-term average/long-term average) detection techniques. In the data set from which the real noise is taken, the network is able to detect >80% of the events identified by manual inspection and 14% more than detected by standard frequency-wavenumber filtering techniques. The false detection rate is approximately 2% or one event every 20 s. In other data sets, with monitoring geometries and conditions previously unseen by the network, >50% of events identified by manual inspection are detected by the CNN.
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