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- W2891470818 abstract "Abstract This paper presents a workflow supported by field examples for modeling the Stimulated Reservoir Volume (SRV) as a Dynamic entity – constrained by validation or calibration against data from the frac treatment, flowback, production and pressure build-ups. The initial reservoir model of static properties is treated as a starting point and evolution of flow and storage are modeled as continuously variable properties using simultaneous modeling of flow and geomechanics – and hence the dynamic qualifier for SRV in the title of this paper. This workflow establishes the interaction between reservoir properties and completion parameters – and allows for program optimization. Emphasis is laid on (i) calibration with multiple types of independently measured field data points and (ii) construction of the simplest models which provide a useful degree of predictability, and we caution against over-parameterization through needless complexity of physical models. We first describe the calibration process – during which a model is perturbed to match various aspects of field data, and then explain the prediction process – where multiple hypothetical completion scenarios can be modeled. The results are then screened against economic metrics (e.g. completions costs vs. projected revenue from improved production) – reducing the number of hit-and-miss experiments in the field. While this paper is comprehensive and self-sufficient in its own coverage, it does focus on the physical modeling and calibration aspects, whereas a companion paper (Min et al. 2018), goes into greater detail about the predictive aspects and data analytics driven completion optimization." @default.
- W2891470818 created "2018-09-27" @default.
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- W2891470818 date "2018-09-24" @default.
- W2891470818 modified "2023-09-23" @default.
- W2891470818 title "Completions and Well Spacing Optimization by Dynamic SRV Modeling for Multi-Stage Hydraulic Fracturing" @default.
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- W2891470818 doi "https://doi.org/10.2118/191571-ms" @default.
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