Layered Linear Inversion for Microseismic Event Location
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fracking techniques face challenges in accurately predicting the precise placement and extent of fractures in shale formations due to computationally intensive and unreliable methods for modeling microseismic activity, which affects the effectiveness of the fracking job and tracking of microseismic events.
Innovation Solution
The implementation of layered linear inversion techniques to locate microseismic events by receiving microseismic signals with a detector array, estimating event locations and times for multiple velocity layers, and selecting the most accurate estimates based on calculated arrival time mismatches, using an inversion model that accounts for properties of the subsurface formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally intensive modeling methods are used to predict fracture locations, then measurement precision may improve, but productivity deteriorates due to tedious and time-consuming calculations
Solution Approach 1:
The subsurface formation is divided into multiple velocity layers with different seismic wave propagation speeds. By segmenting the formation into discrete layers, the complex inverse problem becomes computationally tractable while maintaining adequate localization precision. Each layer's velocity characteristics are used to calculate travel times, enabling faster computation compared to continuous medium models.
Solution Approach 2:
The method transforms the continuous formation model into a discrete parameter model by defining specific velocity values for each layer. This parameterization allows the use of efficient linear inversion algorithms that solve for event location and origin time by minimizing travel time residuals, significantly reducing computational burden while preserving essential geological variability.
2Measurement precision
If complex inversion methods are used to locate microseismic events, then measurement precision may improve, but device complexity increases
Solution Approach 1:
The formation is segmented into velocity layers, which simplifies the inversion mathematics by creating a piecewise constant velocity model. This segmentation reduces the complexity of the sensitivity matrix in the inversion process while maintaining sufficient accuracy for event location, making the algorithm more computationally efficient and easier to implement.
Solution Approach 2:
The method replaces complex nonlinear inversion mechanics with a simplified linear inversion approach. By linearizing the travel time equations around an initial guess and using least-squares minimization, the algorithm achieves adequate precision without requiring iterative nonlinear solvers, thereby reducing computational complexity.
3Measurement precision
If traditional modeling methods are used to track microseismic activity, then measurement precision may be maintained, but loss of time increases due to unreliable and tedious calculations
Solution Approach 1:
By changing the mathematical parameters from continuous velocity functions to discrete layer velocities, the method enables rapid computation of event locations. The linear inversion framework with predetermined velocity layers allows real-time or near-real-time processing of microseismic data, dramatically reducing analysis time while maintaining tracking accuracy.
Solution Approach 2:
The velocity layer model is predetermined and established before microseismic events occur. This preliminary characterization of the formation allows subsequent event locations to be computed rapidly using pre-calculated travel time relationships, eliminating the need for time-consuming velocity model construction during event analysis.
Data Source
AI summary
A method for locating a microseismic event in a subsurface formation, in some embodiments, comprises: receiving a microseismic signal at a detector; obtaining a velocity model representative of the subsurface formation, the velocity model comprising multiple velocity layers; estimating, for each of the multiple velocity layers in the subsurface formation, a microseismic event location and a microseismic event origin time; and selecting one of the estimated locations and times using a parameter of the microseismic signal received at the detector.