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Fracture network engineering for hydraulic fracturing

Authors: Will Pettitt; Matt Pierce; Branko Damjanac; Jim Hazzard; Loren Lorig; Charles Fairhurst; Ivan Gil; +4 Authors

Fracture network engineering for hydraulic fracturing

Abstract

Fracture network engineering (FNE) involves the design, analysis, modeling, and monitoring of infield activities aimed at enhancing or minimizing rock mass disturbance. FNE relies specifically on advanced techniques to model fractured rock masses and correlate microseismic (MS) field observations with simulated microseismicity generated from these models. Hydrofracture stimulation is an example where FNE is playing a role, with hydraulic treatments now being widely used to optimize production volumes and extraction rates in petroleum reservoirs, enhanced geothermal systems, and preconditioning operations in caving mines. MS monitoring is now becoming a standard tool for evaluating the geometry and evolution of the fracture network induced during a given treatment, principally by source locating MS hypocenters and visualizing these with respect to the treatment volume and infrastructure. The integrated use of synthetic rock mass (SRM) modeling of the hydrofracturing with enhanced microseismic analysis (EMA) within FNE provides a feedback loop in which SRM is enhanced and constrained by the information provided by the MS data. This improves interpretation via direct observation of the micromechanics within the distinct element models used. Recent developments in both SRM and EMA technologies are described using case studies of the techniques applied to hydrofracture stimulations. We identify and discuss some future developmental challenges these technologies face, including their further integration and validation so as to provide more efficient and robust application of the FNE approach.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Top 10%
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