A unified Bayesian framework for relative microseismic location

Poliannikov, Oleg V.; Prange, Michael; Malcolm, Alison; Djikpesse, Hugues

doi:10.1093/gji/ggt119

Cited by 26 publications

(15 citation statements)

References 14 publications

(7 reference statements)

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“…Microseismic event locations and origin times are not observed directly. Instead, they are estimated with some uncertainty from recorded microseismic data (Michaud et al, 2004;Bennett et al, 2005;Huang et al, 2006;Poliannikov et al, 2013Poliannikov et al, , 2014.…”

Section: Event Location Uncertaintymentioning

confidence: 99%

Bayesian inversion of pressure diffusivity from microseismicity

et al. 2015

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We have considered the problem of using microseismic data to characterize the flow of injected fluid during hydraulic fracturing. We have developed a simple probabilistic physical model that directly ties the fluid pressure in the subsurface during the injection to observations of induced microseismicity. This tractable model includes key physical parameters that affect fluid pressure, rock failure, and seismic wave propagation. It is also amenable to a rigorous uncertainty quantification analysis of the forward model and the inversion. We have used this probabilistic rock failure model to invert for fluid pressure during injection from synthetically generated microseismicity and to quantify the uncertainty of this inversion. The results of our analysis can be used to assess the effectiveness of microseismic monitoring in a given experiment and even to suggest ways to improve the quality and value of monitoring.

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Section: Event Location Uncertaintymentioning

confidence: 99%

Bayesian inversion of pressure diffusivity from microseismicity

et al. 2015

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“…Poliannikov et al, (2013Poliannikov et al, ( , 2014) discuss a method for simultaneously locating and estimating origin times of all recorded events, s, and quantifying the associated uncertainty, from noisy time picks, T, and an uncertain velocity model. The output of their algorithm is the posterior distribution, f(s | T), of all locations and origin times of microseismic events given noisy arrival time picks.…”

Section: Probabilistic Inversionmentioning

confidence: 99%

The Effect of Velocity Uncertainty and Attenuation on Flow Estimates From Microseismicity

Poliannikov¹,

Fehler²,

Malcolm³

2015

Unconventional Resources Technology Conference

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We study the effects of the velocity uncertainty and attenuation in the subsurface on the fluid pressure diffusivity estimated from observed microseismicity. We use a probabilistic physical model that directly ties fluid pressure in the subsurface during the injection to direct-arrival times of induced microseismic events observed at the monitoring receiver array to track the propagation of uncertainty in the forward model and inversion. We estimate the fluid pressure due to the injection, and quantify the uncertainty of this estimate, from synthetically modeled noisy travel times. The errors in the estimated travel times are primarily due to a combination of an uncertain velocity model and attenuation. Our examples demonstrate that the robust inversion of fluid flow parameters from microseismic data is possible if all effects contributing to uncertainty are properly accounted for.

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“…It follows a similar analysis of location uncertainty for seismic events (Poliannikov et al, 2013(Poliannikov et al, , 2014. Given surface seismic reflection data, we construct a joint posterior estimator of the locations of chosen horizons that is a multidimensional probability distribution of the locations of discrete points on the horizons.…”

Section: Introductionmentioning

confidence: 99%

The effect of velocity uncertainty on migrated reflectors: Improvements from relative-depth imaging

Poliannikov

Malcolm

2016

GEOPHYSICS

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We have studied the problem of uncertainty quantification for migrated images. A traditional migrated image contains deterministic reconstructions of subsurface structures. However, input parameters used in migration, such as reflection data and a velocity model, are inherently uncertain. This uncertainty is carried through to the migrated images. We have used Bayesian analysis to quantify the uncertainty of the migrated structures by constructing a joint statistical distribution of the location of these structures. From this distribution, we could deduce the uncertainty in any quantity derived from these structures. We have developed the proposed framework using a simple model with velocity uncertainty in the overburden, and we estimated the absolute positions of the horizons and the relative depth of one horizon with respect to another. By quantifying the difference in the corresponding uncertainties, we found that, in this case, the relative depths of the structures could be estimated much better than their absolute depths. This analysis justifies redatuming below an uncertain overburden for the purposes of the uncertainty reduction.

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A unified Bayesian framework for relative microseismic location

Cited by 26 publications

References 14 publications

Bayesian inversion of pressure diffusivity from microseismicity

Bayesian inversion of pressure diffusivity from microseismicity

The Effect of Velocity Uncertainty and Attenuation on Flow Estimates From Microseismicity

The effect of velocity uncertainty on migrated reflectors: Improvements from relative-depth imaging

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