Uncertainties in passive seismic monitoring

Eisner, Leo; Duncan, P. Martin; Heigl, Werner M.; Keller, William R.

doi:10.1190/1.3148403

Cited by 180 publications

(101 citation statements)

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“…Hence, using the downhole tools is a better option in measuring the vertical height growth of a hydraulic fracture. Also, the fracture height comparison supports the previous statement by Eisner et al (2009) indicating that the surface geophone arrays do not provide a robust of depth estimation in comparison to the downhole geophone arrays. After reviewing the overlapping results for both wells, it is fair to conclude that the discrepancies between the mapped microseismic events and the simulated fracture models, particularly for fracture height for both methodologies, demonstrate the difficulty to obtain sufficient information to obtain completely matched results.…”

Section: Downhole Methodology Vs Surface Methodologysupporting

confidence: 77%

A Comparison of Hydraulic-Fracture Modeling With Downhole and Surface Microseismic Data in a Stacked Fluvial Pay System

Mohammad

Miskimins

2012

SPE Production &Amp; Operations

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The work in this project concentrates on comparing the results of hydraulic fracture mapping and modeling using two microseismic monitoring techniques: downhole and surface arrays. There were three objectives achieved in this project in order to obtain the resulting comparisons. The first objective was to develop detailed posttreatment models of the hydraulic fracturing treatments in the subject well, Well D1, which was monitored with downhole microseismic data. The second objective was to develop detailed post-treatment models of the hydraulic fracturing treatments in the subject well, Well S1, which was monitored with surface microseismic data. The final objective of this project was to determine the match characteristics of the downhole and surface microseismic data to hydraulic fracture models developed for both Wells D1 and S1. Comparisons of the match characteristics of the multiple inputs were then developed.In this study, GOHFER™, a fully three-dimensional fracture simulator was used to build hydraulic fracture stimulation models that were integrated with microseismic events detected during actual hydraulic fracture treatments on Wells D1 and S1.GOHFER™ uses data from actual hydraulic fracturing treatments (pressure, slurry rate, and proppant concentration) and log-derived data to supplement reservoir and mechanical iv Input data for this project were obtained from five wells in the Greater Natural Buttes, Uinta basin, Utah. Two wells, Wells D1 and S1, were stimulated with hydraulic fracturing treatments where each well was monitored by geophone arrays that were placed at different locations. Downhole geophone receivers were employed in an observation well, Well D2, to monitor microseismic activities in the treatment well, Well D1. For the other treated well, Well S1, geophone receivers were placed on the ground surface surrounding the treatment well to monitor microseismic events in the subsurface.Multiple log data were provided for the treated wells including the sonic log from which rock mechanical properties were calculated.Ten fracture models were built using GOHFER™ for five stages each from Wells D1 and S1. Pressure matching procedures were performed to achieve the final simulated fracture geometries. The integration process took place by utilizing data from two sources: microseismic mapping during actual hydraulic fracturing treatment; and fracture profiles from simulated fracture models. Results from the integration process show good agreement for most stages in the downhole-monitored well, whereas comparisons of surface microseismic mapping measurements with the simulated fracture geometries yield questionable results especially regarding microseismic event locations with respect to depth.

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Section: Downhole Methodology Vs Surface Methodologysupporting

confidence: 77%

A Comparison of Hydraulic-Fracture Modeling With Downhole and Surface Microseismic Data in a Stacked Fluvial Pay System

Mohammad

Miskimins

2012

SPE Production &Amp; Operations

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“…The location part builds on our past work (Poliannikov et al, 2011a(Poliannikov et al, , 2011b(Poliannikov et al, , 2012(Poliannikov et al, , 2014(Poliannikov et al, , 2016Poliannikov and Malcolm, 2016), which has focused primarily on the location problem and highlights how different location algorithms perform under different assumptions on the uncertainties in both arrival times and velocity. That work shares some similarities with that of Eisner et al (2009). A recent overview of MTI methods can be found in Cesca et al (2013) or Gu (2016).…”

Section: Introductionmentioning

confidence: 75%

What moved where?: The impact of velocity uncertainty on microseismic location and moment-tensor inversion

2017

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With the rise of unconventional resources, microseismic monitoring is becoming increasingly important because of its cost-effectiveness. This has led to significant research activity on how best to locate events and characterize their moment tensors. Locations tell us where fracturing is occurring, allow the tracking of fluid movement, and fracture propagation. Moment tensors help to determine the type of failure occurring, which is beneficial in planning and interpreting the results of hydraulic-fracturing jobs and in monitoring production. The rising number of methods to determine parameters raises important questions about how uncertainties in the input parameters are translated into uncertainties in the final locations and moment tensors. We present a framework for assessing these uncertainties and use it to demonstrate how velocity uncertainty -as well as uncertainties in arrival times and amplitudes -translates into uncertainties on the recovered quantities of location and moment-tensor parameters.

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“…For a surface array with an aperture twice the depth of interest, this point spread function will be elongated in the vertical direction by roughly a factor of three compared to the horizontal response. This elongated shape is primarily an expression of the trade-off between time of the event (i.e., origin time) and depth of the event when origin time of the event is unknown (Eisner et al, 2009). The actual size of this response is determined by the frequency of the received signal, the velocity of the overburden, as well as the array configuration.…”

Section: Imaging Methodsmentioning

confidence: 99%