Microseismic velocity model inversion and source location: The use of neighborhood algorithm and master station method

Tan, Yuyang; He, Chu; Mao, Zhonghua

doi:10.1190/geo2017-0308.1

Cited by 21 publications

(19 citation statements)

References 67 publications

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“…It is obvious that the quality of final source image is much improved, and the source energy is better focused. In order to demonstrate the accuracy of our proposed method, we compare our result with that previously obtained by Tan et al (2018). These authors used the neighbourhood algorithm to invert for 1D velocity model and locate the event.…”

Section: D Field Datamentioning

confidence: 87%

Microseismic event estimation and velocity analysis based on a source-focusing function

Song

Alkhalifah

2019

GEOPHYSICS

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Attaining information corresponding to the microseismic source location helps in understanding the reservoir fracturing process. Time-reversal-based migration methods are widely used to obtain a source image directly. Such source-locating methods share a fundamental weakness: The accuracy of the source image depends highly on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used in microseismic data to include an optimization of the source image and velocity model, simultaneously. However, such inversions are vulnerable to cycle-skipping problems, more so when the source location and source origin time are unknown. The computational cost of FWI is also high. To solve these problems, we have developed a source-focusing function as an additional objective to optimize the velocity model and source image. This objective function used to measure the source-image focusing property is defined by the estimated source-location coordinate and the source image. The source image is poorly focused if the velocity is inaccurate, and the objective function reaches a minimum when the velocity is accurate. We use the geometric-mean imaging condition to get a high-resolution source image at each iteration. Then, we use this source image to calculate the estimated source-location coordinate and the gradient of the objective functional with respect to the velocity. The optimized velocity can improve the source-image quality. In the end, the final output velocity and source image will allow us to fit the objective for all these attributes of the model and source image. Using synthetic data generated from the 2D Marmousi and the 3D overthrust models, we highlight the cycle-skipping immune and high-resolution features of our method. The application to field data also indicates that our method can improve the velocity model and source-image quality.

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Section: D Field Datamentioning

confidence: 87%

Microseismic event estimation and velocity analysis based on a source-focusing function

Song

Alkhalifah

2019

GEOPHYSICS

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“…To implement the inversion, we start with homogeneous models V p = 3.3km/s and V s = 1.9km/s as the initial velocities. We have been provided a 1D V p model from the previous study of this dataset (Tan et al, 2018), but we simply choose to start from a lower-accuracy initial model, the homogeneous background velocities, as a test to the robustness of our inversion. The model has 400-by-225 grid points with a 2m spatial interval in both the X and Z directions.…”

Section: Ball-drop Event Inversionmentioning

confidence: 99%

Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set

et al. 2020

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One of the key goals of microseismic processing is the accurate estimation of the source location. Applying full waveform information on passive-source datasets can potentially delineate microseismic sources. The accuracy of both P- and S- wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We propose a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed of source images and source functions, as it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative source representation of its mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We, thus, use a source-independent objective function, based on convolving reference traces with both modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically test our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, the proposed method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy as the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Though the proposed method has higher computational cost than traveltime or migration based methods, the estimated event locations have significantly improved accuracy.

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“…Walda and Gajewski (2017), Xie and Gajewski (2017) utilized differential evolution (DE) to determine common-reflection surface (CRS) attributes. Pei et al (2009), Zhang et al (2014), and Tan et al (2018a) applied SA, DE, and NA to invert microseismic velocity models, respectively. Maity et al (2014), Maity and Salehi (2016) proposed and applied a hybrid ANN based workflow for first arrival picking of microseismic data.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Waveform-based microseismic location using stochastic optimization algorithms: A parameter tuning workflow

Tan

Xie

et al. 2019

Computers & Geosciences

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Algorithm are implemented.A novel parameter tuning workflow of stochastic optimization algorithms for waveform-based location is proposed.Three algorithms are tested and analyzed using both synthetic and field data.The characteristics of two imaging operators are also revealed. Authorship statementL. Li contributed to method development and testing, and drafted the manuscript. J. Tan contributed to manuscript review and funding acquisition. Y. Xie and Y. Tan contributed to algorithm implementation of the study. J. Walda and Z. Zhao revised the manuscript carefully. D. Gajewski advised the study design and revised the manuscript.

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Microseismic velocity model inversion and source location: The use of neighborhood algorithm and master station method

Cited by 21 publications

References 67 publications

Microseismic event estimation and velocity analysis based on a source-focusing function

Microseismic event estimation and velocity analysis based on a source-focusing function

Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set

Waveform-based microseismic location using stochastic optimization algorithms: A parameter tuning workflow

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