Microseismic Marker Segmentation for Reservoir Volume Estimation
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Solution Overview
Problem
Current methods lack an effective way to calculate the stimulated reservoir volume in earth formations, which is crucial for estimating hydrocarbon production rates, as they fail to accurately account for location errors and spatial distributions of microseismic events.
Innovation Solution
A method involving seismic signal processing to represent microseismic events as markers in a spatial distribution, calculating attributes, and summing these attributes within cells to derive a cell attribute density, while accounting for location errors using probability functions and weights, to estimate the stimulated reservoir volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microseismic event locations are used directly to represent stimulated rock volume, then the calculation process is simple, but the accuracy is reduced due to location errors
Solution Approach 1:
The patent segments the continuous space into discrete cells and represents each microseismic event as multiple discrete markers within a probability distribution rather than a single point. This segmentation allows the system to account for location uncertainties by distributing markers across possible locations, thereby improving measurement precision while maintaining computational tractability through discrete summation.
Solution Approach 2:
The patent introduces probability functions and marker distributions as intermediaries between the raw microseismic event locations and the final stimulated reservoir volume calculation. These intermediaries allow the system to incorporate location uncertainties without directly computing complex error propagation, thus improving accuracy while avoiding excessive computational complexity.
2Measurement precision
If location errors are accounted for using probability functions and marker distributions, then the accuracy of volume estimation improves, but the computational complexity increases
Solution Approach 1:
The patent divides the probability distribution of each microseismic event into discrete markers placed in specific cells. This segmentation transforms the continuous probability integration problem into a discrete summation problem, improving location representation accuracy while keeping computational complexity manageable through finite discrete operations.
Solution Approach 2:
The patent uses a finite number of markers to represent the continuous probability distribution, applying partial action rather than exhaustive integration. This approach provides sufficient accuracy for practical applications while avoiding the excessive computational complexity of complete probabilistic integration across all possible locations.
3Measurement precision
If the represented space is divided into many small cells to improve resolution, then the estimation accuracy improves, but the computational burden increases
Solution Approach 1:
The patent segments the represented space into cells and assigns markers to specific cells based on probability distributions. This segmentation allows the system to achieve spatial resolution through selective marker placement rather than requiring extremely fine grid discretization throughout the entire volume, thus improving estimation accuracy while maintaining computational efficiency.
Solution Approach 2:
The patent applies different levels of spatial detail locally through marker distributions concentrated around actual event locations rather than uniformly across the entire represented space. This local quality approach improves resolution where it matters (near events) while avoiding the computational burden of fine discretization throughout the entire volume, thereby balancing accuracy and productivity.
Data Source
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AI summary
A method for estimating a volume of a stimulated reservoir includes receiving a seismic signal from each microseismic event in a plurality of microseismic events in an earth formation by an array of seismic receivers. The method further includes representing each microseismic event by a plurality of markers in the three-dimensional space. A spatial distribution of the markers represents a volume of rock influenced by a microseismic event, wherein the volume and a location of each event are derived from the seismic signal. The method further includes calculating a scalar attribute for each marker in the plurality of markers, dividing the three-dimensional space into a plurality of three-dimensional grid cells, and summing the scalar attributes for all the markers in each grid cell to provide a total scalar attribute for each grid cell.