SRV Uncertainty Quantification via Probabilistic Microseismic Modeling
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Solution Overview
Problem
Current methods fail to accurately quantify the uncertainty in the Stimulated Reservoir Volume (SRV) of unconventional reservoirs like shales and tight sands, leading to inefficiencies in hydraulic fracturing and well placement, due to inadequate integration of static and dynamic datasets and poor data processing.
Innovation Solution
A computer-implemented method that characterizes uncertainty in subsurface regions by combining natural fracture models with dynamic field data to simulate microseismic events, using techniques like Design of Experiments and finite-difference modeling to refine SRV estimates, and incorporating production data for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microseismic measurements are used to estimate SRV, then useful information about fracture size and directionality is obtained, but large uncertainty in the quality of results occurs due to lack of industry-standard data processing
Solution Approach 1:
The patent transforms microseismic event locations from point data into probabilistic spatial distributions by introducing uncertainty parameters. Each microseismic event is represented not as a single location but as a probability density function in three-dimensional space, allowing the system to account for location uncertainties while maintaining quantitative analysis capability
Solution Approach 2:
The patent introduces a probabilistic framework as an intermediary between raw microseismic measurements and SRV estimation. This framework uses probability density functions to bridge the gap between uncertain measurement data and reliable volume calculations, enabling uncertainty propagation through the analysis workflow
2Ease of manufacture
If ad-hoc procedures are used to process microseismic data, then a single SRV value is produced, but the large uncertainty associated with it is completely ignored
Solution Approach 1:
The patent segments the SRV estimation process into distinct probabilistic components: microseismic event location uncertainty, fracture geometry uncertainty, and volume calculation uncertainty. Each component is treated separately with its own probability distribution, allowing comprehensive uncertainty characterization while maintaining systematic workflow
Solution Approach 2:
The patent adds a probabilistic dimension to the traditional deterministic SRV estimation. Instead of producing a single volume value, the system generates a probability distribution of possible SRV values, effectively moving from one-dimensional point estimates to multi-dimensional probabilistic outputs that capture uncertainty
3Reliability
If static and dynamic datasets are integrated to quantify SRV uncertainty, then production potential can be optimized, but the complexity of identifying and integrating appropriate datasets increases
Solution Approach 1:
The patent creates a universal probabilistic framework that can accommodate multiple data types (microseismic, geomechanical, hydraulic fracturing) and multiple analysis objectives (SRV estimation, uncertainty quantification, production prediction). This single framework handles diverse datasets through consistent probability-based methods, reducing the need for separate specialized workflows for each data type
Solution Approach 2:
The patent implements iterative refinement where initial SRV estimates and uncertainty characterizations feed back into the modeling process. The probabilistic framework allows results to be updated as new data becomes available or as models are refined, creating a feedback loop that continuously improves uncertainty quantification without requiring complete workflow redesign
Data Source
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AI summary
A system and method for characterizing uncertainty in a subterranean fracture network by obtaining a natural fracture network, obtaining dynamic data, simulating hydraulic fracturing and microseismic events based on the natural fracture network and the dynamic data, generating a stimulated reservoir volume (SRV), and quantifying the uncertainty in the SRV. It may also include narrowing the uncertainty in the SRV through the use of Design of Experiment methods and characterizing the SRV using static and/or dynamic data.