Abstract

Because surface-based monitoring of hydraulic fracturing is not restricted by borehole geometry or the difficulties in maintaining subsurface equipment, it is becoming an increasingly common part of microseismic monitoring. The ability to determine an accurate velocity model for the monitored area directly affects the accuracy of microseismic event locations. However, velocity model calibration for location with surface instruments is difficult for several reasons: well log measurements are often inaccurate or incomplete, yielding intractable models; origin times of perforation shots are not always accurate; and the non-uniqueness of velocity models obtained by inversion becomes especially problematic when only perforation shots are used. In this paper, we propose a new approach to overcome these limitations. We establish an initial velocity model from well logging data, and then use the root mean square (RMS) error of double-difference arrival times as a proxy measure for the misfit between the well log velocity model and the true velocity structure of the medium. Double-difference RMS errors are reduced by using a very fast simulated annealing for model perturbance, and a sample set of double-difference RMS errors is then selected to determine an empirical threshold. This threshold value is set near the minimum RMS of the selected samples, and an appropriate number of travel times within the threshold range are chosen. The corresponding velocity models are then used to relocate the perforation-shot. We use the velocity model with the smallest relative location errors as the basis for microseismic location. Numerical analysis with exact input velocity models shows that although large differences exist between the calculated and true velocity models, perforation shots can still be located to their actual positions with the proposed technique; the location inaccuracy of the perforation is <2 m. Further tests on field data demonstrate the validity of this technique.

Highlights

  • Hydraulic fracturing of low-permeability reservoirs generates many microseismic events due to pressure increase associated with fluid injection into treatment wells (Warpinski et al 2005)

  • Velocity model calibration for location with surface instruments is difficult for several reasons: well log measurements are often inaccurate or incomplete, yielding intractable models; origin times of perforation shots are not always accurate; and the non-uniqueness of velocity models obtained by inversion becomes especially problematic when only perforation shots are used

  • We present a non-linear inversion method for the calculation of velocity models suitable for locating microseismic events with surface sensor data

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Summary

Introduction

Hydraulic fracturing of low-permeability reservoirs generates many microseismic events due to pressure increase associated with fluid injection into treatment wells (Warpinski et al 2005). Several papers have proposed methods to construct reservoir velocity models for microseismic event location, most of which are based on the following steps: (1) A simple velocity model, using only a few parameters, is constructed from well logging data. Anikiev et al (2014) described a method in which the initial velocity of each layer was simultaneously increased or decreased using the accuracy of perforation shot relocations as an evaluation standard They obtained a relatively accurate velocity model by inversion. Traditional shooting methods were improved by Gao and Xu (1996), who proposed a new type of step-bystep iterative ray tracing algorithm that greatly improved computational efficiency This method can be used with a slightly more complicated velocity model than other techniques. We expand the method to a 3D layered structure for calculating travel times

Ray tracing in a layered medium
Step-by-step iterative ray-tracing method
Very fast simulated annealing with DDrms
The objective function
Velocity perturbation vector
Selecting the optimal velocity model
Sensitivity to the constraints
The sensitivity to picking errors
Findings
Conclusion
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