Summary A joint inversion of P-wave first arrivals, surface wave dispersion curves and reflectivity image along picked horizons is proposed for estimating a high resolution P-wave (and S-wave) velocity model of the near-surface. The three inversion datasets are combined in a stochastic optimization process through normalization of cost function terms accounting for different data domains. The resulting velocity model is geologically consistent and reconciles P and S-wave velocities and shallow reflectivity as well.
We investigate the microseismic activity induced in the Arkema-Vauvert salt field by water injections. We focus particularly on the determination of the focal mechanisms providing information on the faults geometry and fracturing process. First, we perform preliminary tests showing that the determination of the double-couple focal mechanism from the two 3-components permanent sensors deployed in the field is reliable. Next, we study the swarm of microseismicity induced by the activity of the PA22-PA23 doublet. The spatio-temporal evolution of the microseismicity during 21 months is correlated to the water-injection operations. Most of the focal mechanisms are “dip-slip” fault type in accordance with geological structures identified in the reservoir.
This study investigates a microseismic swarm induced by injection operations in the Arkema-Vauvert salt field. The seismic activity in this field is monitored only by two permanent 3-component stations deployed in two wells. This study focuses on a period of 21 months (2004 January-2005 September) during which 1214 seismic events are located. The seismic activity is divided into three periods correlating with the water injection operations, highlighting a migration of the seismicity toward a thrust fault connecting the injection well and the production well. A waveform analysis reveals S-wave anisotropy, and focal mechanisms are computed using P, Sv and Sh amplitudes manually measured on anisotropy-corrected seismograms. First, synthetic resolution tests assess the reliability of the focal mechanisms determination from the two 3-component stations deployed in the field. Synthetic data are generated for 1056 earthquakes with various focal mechanisms and are perturbed with noise. The results indicate that the type of focal mechanism is correctly retrieved for 74 per cent of the synthetic earthquakes, but the uncertainties of the strike and rake are significant (from 15 to 45). Next, the focal mechanisms are computed for 532 real earthquakes. The solutions primarily correspond to a dip-slip/thrust fault type with subvertical NE-SW and subhorizontal N-S to NW-SE nodal planes. Correlations between the focal mechanisms and the spatio-temporal distribution of the seismic activity are noteworthy. The study shows it is possible to reliably retrieve double-couple focal mechanisms for some faulting geometries with two 3-component seismological stations. However, the reliability of the focal mechanism retrieval depends on the station configuration. Therefore, the addition of further stations would improve the results.
Summary We propose a laterally constrained surface wave inversion to obtain a reliable near-surface shear-wave velocity field from Rayleigh wave measurements. This workflow is targeted at dense 3D broadband wide-azimuth land surveys, aiming to obtain reliable and realistic lateral shear-wave velocity variations pertinent with regard to surface or sub-surface information. We applied our methodology to a dataset acquired by Petroleum Development of Oman in the Sultanate of Oman. The S-wave velocity model obtained can be easily correlated to surface data, satellite map and time-reflectivity volume, hence demonstrating the potential of our method to build reliable and geologically consistent near-surface velocity models.
Shallow stratigraphy in Southern Oman is characterized by the presence of an anhydrite layer (RUS formation) causing a strong velocity inversion which makes seismic imaging particularly difficult. This known shallow sharp velocity inversion cannot be easily captured with methods relying on reflection or diving wave energy. We propose here to use multi-wave inversion using first breaks and dispersion curves of surface waves to provide near-surface high resolution velocity models in the shallow range depths (0-400m). The success of Multi-Wave Inversion strongly depends on the reliability of the surface wave velocity picking, which could be much more challenging compared to the conventional first break picking. Heavy preconditioning is often the solution to increase dispersion curves quality and to obtain a narrower velocity corridor. To improve reliability, we use K-means clustering, an unsupervised machine learning method in order to filter out the outliers as well as to define geologically dependent corridors. The unsupervised machine learning clustering helps to define more stable dispersion curves picking corridors for different areas, in order to extract better quality surface wave dispersion curves's, especially at low frequencies where their quality is low. The multi-wave inversion, fed with the optimized phase velocity picks, captures the shallow velocity inversion, which is impossible to recover with either first break tomography only or diving wave full waveform inversion only. The combination of two recently developed technologies allows us to characterize accurately the near surface for the first time in the South of Oman. The velocity inversion caused by the RUS formation is well captured and the velocity trend of the updated model follows correctly the checkshot trend down to 500m, confirming the reliability of the dispersion curves picks at a very low frequency. By incorporating this shallow inversion layer into the velocity model, the resulting seismic image is significantly improved and more interpretable. Geological features such as faults appear clearly and seismic layering in the tilted blocks is significantly improved with the multi-wave Inversion machine learning-guided workflow.
P.-F. Roux, J. Kostadinovic, T. Bardainne, E. Rebel, M. Chmiel, M. Van Parys, R. Macault and L. Pignot present an acquisition and processing technique to further decrease the noise recorded at the surface of the Earth when monitoring hydraulic stimulation. It is well known that fluid injection into reservoirs, be it in the context of enhanced geothermal systems or for the stimulation of hydrocarbon reservoirs, generates so-called ‘induced’ seismic activity (Evans, 1966). Early on, the link between the stimulation and this activity has been established, and it has become increasingly obvious that measuring the microseismicity generated by the injection would provide a wealth of information on the mechanical processes at work during the stimulation. Historically, downhole geophone tools have been used to monitor microseismic activity during stimulation programmes. Such tools usually offer a very high sensitivity to the microseismic sources, provided that the observation well is close enough to the treated well (Rutledge and Phillips, 2003). However, this becomes limited when more information on the source mechanism (usually termed focal mechanism and represented by the infamous moment tensor) is required. This is because of the three-dimensional nature of the focal mechanism, which means it cannot be retrieved properly using a single observation point. In addition, a poorly situated observation well may indeed lead to a reduced detection capability.
Abstract The purpose of this article is to address the problem of the focal mechanism determination using few seismological records acquired by a sparse network of 3-component sensors. Such cases are frequently encountered in reservoir contexts for the monitoring of the fluid-induced microseismicity. Focal mechanisms of fluid-induced earthquakes are characterized by a non-double-couple part. However, we show and discuss that the double-couple moment tensor approximation is valid as a source model. Then, we propose a nonlinear inversion method of the direct P -, SV - and SH -wave amplitudes, based on a simulated annealing algorithm to determine double-couple focal mechanisms. Simultaneously, we determine the associated uncertainty. We take into account three sources of uncertainty related to the convergence process of the inversion to the amplitude picking uncertainty caused by the noise level and to the uncertainty of the event location. First, we test our method on synthetic data. Second, we apply the method on four events induced in the Soultz-sous-Forets geothermal field whose focal mechanisms were already determined by Charlety et al. (2007). We obtain focal mechanisms with uncertainties containing the solutions previously determined. Finally, we evaluate the required minimum number of sensors and their geometrical configuration to obtain a focal mechanism. The direction of the nodal planes and the type of mechanism are retrieved for data sets as small as three 3-component stations. The tests also reveal that the reliability of the fault plane solution depends on the configuration the stations used. It also seems that the coverage of the focal sphere by the stations, that is, the opening angle of the network and the coverage of several quadrants, has an influence on the reliability of the fault plane solution retrieval. The use of only one 3-component sensor allows retrieval of the type of focal mechanism in most of the cases studied.
