Discrete-Fracture-Network Generation From Microseismic Data by Use of Moment-Tensor- and Event-Location-Constrained Hough Transforms

Abstract

Reservoir simulation and prediction of production associated with hydraulic-fracturing require the input of the fracture geometry and the fracture properties such as the porosity and retained permeability. Various methods were suggested and applied for deriving discrete fracture networks (DFNs) from microseismic data as a framework for modeling reservoir performance. Although microseismic data are the best diagnostics for revealing the volume of rock fractured, its incompleteness in representing the deformation induced presents a challenge to calibrate and represent complex fracture networks created and connected during hydraulic-fracture stimulation. We present an automated method to generate DFN models constrained by the microseismic locations and fracture plane orientations derived from moment-tensor analysis. We use a Hough-transform technique to find significant planar features from combinations of the microseismic source locations. We have modified the technique with an equal-probability voting scheme to remove an inherent bias for horizontal planes. The voting mechanism is a general grid search in the space of fracture strike, dip, and location (u; h; r, respectively) with grid-cell sizes scaled by uncertainty estimates of u; h; r. We constrain fracture orientations with weighting on the basis of the moment-tensor orientations of neighboring events and their associated uncertainties. With two case studies, we demonstrate that our automated technique can reliably extract the complex fracture network on the basis of good matches with the event-cloud trends and the input moment-tensor orientations. We also tested the sensitivity of the technique to event-location uncertainty. With increasing location uncertainty, the details of the fracture network extracted are diminished with events grouping to larger-scale features, but the general shape and orientation of the fracture network obtained are insensitive to the location uncertainty.

A multi-model fitting algorithm for extracting a fracture network from microseismic data

“…DFNs can be used to image and model individual three-dimensional (3D) fractures to evaluate the changes in their geometry and behaviors of associated fluids (Dershowitz and Fidelibus, 1999;Li and Lee, 2008;Hyman et al, 2015). They can be constructed in many ways, such as by linking fracture imaging to seismic activity based on microseismic monitoring data (Fadakar Alghalandis et al, 2013) or the source mechanism (Yu et al, 2016;Yu et al, 2020). They are useful because they can identify and display fracture locations solely based on microseismic data.…”

Multidomain Two-Phase Flow Model to Study the Impacts of Hydraulic Fracturing on Shale Gas Production

“…A geomechanically based methodology is usually applied to the description of the hydraulic fracture 4 in which naturally occurring fractures are first located and hydraulic fracture propagation later simulated. 5,6 For the analysis of gas production, discrete fracture network (DFN) models provide a feasible way to incorporate the hydraulic fracture network into the simulation of shale gas reservoirs. 7,8 In DFN models, the hydraulic fractures are directly modeled and the computing nodes denote either the matrix system or the fracture system.…”

“…To calibrate the effectiveness of HF, the microseismic (MS) event distribution may be used to monitor fracture propagation and interpret the evolution of reactivated natural and artificial hydraulic fracture networks. A geomechanically based methodology is usually applied to the description of the hydraulic fracture in which naturally occurring fractures are first located and hydraulic fracture propagation later simulated. , For the analysis of gas production, discrete fracture network (DFN) models provide a feasible way to incorporate the hydraulic fracture network into the simulation of shale gas reservoirs. , In DFN models, the hydraulic fractures are directly modeled and the computing nodes denote either the matrix system or the fracture system. When applied to reservoir simulations, the DFN approach exhibits three major defects: (1) the DFN is of limited fidelity as the MS signals are always contaminated by noise; (2) DFN approaches are usually highly time-consuming and computationally intensive; and (3) a significant amount of MS field data are required, but are rarely available in most the shale gas fields …”