Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.
Forward modeling plays a key role in both the creation of predictive models and the study of the surrounding environment through inversion methods. Due to their competitive computational cost and modest algorithmic complexity, finite difference methods (FDM) are commonly used to model the acoustic wave equation. An algorithm has been developed to decrease the computational cost of acoustic-wave forward modeling that can be applied to most finite difference methods. An important feature of the algorithm is the calculation, at each time step, of the pressure in only a moving subdomain which contains the grid points across which waves are passing. The computation is skipped at grid points at which the waves are negligibly small or non-existent. The novelty in this work comes from flexibility of the subdomain and its ability to closely follow the developing wavefield. To demonstrate the efficacy of the algorithm, it is applied to a standard finite difference scheme and validated against 2-D modeling results. The algorithm herein can play an important role in the reduction in computation time of seismic data analysis as the volumes of seismic data increase due to developments in data acquisition technology.
The objective of this paper is to propose an alternative data analysis approach to working with microseismic data. Modern machine learning techniques, such as MWCA (Multiway Component Analysis) and TD (Tucker Decomposition) can give the capability to efficiently work with complex high-dimensional microseismic data structures. Using this method, it was possible to restore hidden information about the signal, compress the data, and get insights about fractures without using conventional time-consuming simulations. Therefore, it is an important addition to the hydraulic fracturing quality assessment. It is a cost-effective technique providing a greater degree of automation in comparison to conventional methods. The approach was tested on synthetic data and relevant real microseismic data provided by a service company. The data was integrated in a 3rd-order tensor form where modes are: seismic events time, receiver locations, and event locations. The tensor was then decomposed into a core tensor and three factor matrices by means of a special form of TD called HOSVD (Higher-Order Singular Value Decomposition). HOSVD is a multidimensional decomposition used to extract low rank approximations of tensors. The MWCA technique was utilized to impose constraints on TD. HOSVD showed potential as a tool for a rapid fractures analysis by observing decomposed tensor structure. Additionally, the technique helped reduce the original model by 73% (supercompression). The proposed workflow is general and highly applicable to various plays. Since the applications of MWCA and TD are still emerging, future enhancements to this methodology are expected. In turn, this will reveal further insights from microseismic data, making it paramount to optimal fracturing and improved field management.
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