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The generation of microseismic events is often associated with induced fractures/faults during the extraction/injection of fluids. A full characterization of the spatiotemporal distribution of microseismic events provides constraints on fluid migration paths in the formations. We have developed a high-resolution source imaging method — a hybrid multiplicative time-reversal imaging (HyM-TRI) algorithm, for automatically tracking the spatiotemporal distribution of microseismic events. HyM-TRI back propagates the data traces from groups of receivers (in space and time) as receiver wavefields, multiplies receiver wavefields between all groups, and applies a causal integration over time to obtain a source evolution image. Using synthetic and field-data examples, we revealed the capability of the HyM-TRI technique to image the spatiotemporal sequence of asynchronous microseismic events, which poses a challenge to standard TRI methods. Moreover, the HyM-TRI technique is robust enough to produce a high-resolution image of the source in the presence of noise. The aperture of the 2D receiver array (azimuth coverage in 3D) with respect to the microseismic source area plays an important role on the horizontal and vertical resolution of the source image. The HyM-TRI results of the field data with 3D azimuthal coverage further verify our argument by producing a superior resolution of the source than TRI.

Summary Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence HiEKF is computationally efficient and applicable to 3D TLFWI problems. Then we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI-HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2D and 3D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.

Accuracy and efficiency are most urgent problems in elastic wave simulation. Staggered‐grid is an effective method to improve the accuracy with high efficiency. By the combination of variable grids and locally variable time‐steps, a staggered‐grid high‐order finite‐difference method with oddly arbitrarily variable spatial grids and arbitrarily variable local time‐steps is presented. The numerical results show that the simulation accuracy and efficiency are increased effectively by avoiding oversampling both in space and time domain. Additionally, compared with the traditional method, this modeling method has advantages in seismic wave simulation in the medium with fractures, caves and complicated structures. It can describe such medium in details, and has high accuracy and efficiency.

Imaging and characterizing subsurface natural fractures that are common in the Earth crust has been a long-sought goal in seismology. We present an application of a 3-D passive seismic fracture imaging method applied to Marcellus shale microseismic data for mapping natural fractures. Unlike conventional seismic imaging methods that need source information, the proposed imaging method does not require source information and is flexible enough to apply to any passive seismic data where the source location is unknown or inaccurate. We first test our imaging approach using surface microseismic monitoring array data in 3-D synthetic examples. The finite-aperture fractures are designed by an open-source discrete fracture network software. Compared to conventional source-dependent fracture imaging, the proposed source-independent imaging approach produces superior images of fractures with less ambiguity. These tests also illustrate that the proposed method is less sensitive to the accuracy of background velocity and less affected by the sparse and irregular acquisition geometry which often cause acquisition-footprint issues in convention imaging methods. The final test in the field microseismic data from the Marcellus Shale (Pennsylvania) demonstrates the applicability of the proposed imaging method. Field data results indicate two clusters of east-northeast fractures existed above and below the hydraulic fracturing zone, which corroborates previous work that found two main types of faults in the study area.