Correlation of Stimulated Rock Volume from Microseismic Pointsets to Production Data - A Horn River Case Study

Abstract

Hydraulic fracture monitoring from microseismic allows operators to optimize completions through a clear understanding and correlation of the reservoir response to stimulation. Furthermore it helps operators to improve production and avoid out of zone growth by identifying patterns of fluid movement, fracture growth and connectivity. These critical insights allow refinements to the treatment plan, and provide useful insights for long-term improvements regarding well spacing, well design, and completion design. Shale reservoirs with very low permeability in the nano darcy range require a large fracture network to increase well performance. In these reservoirs, unless natural pre-existing fractures and faults have been reactivated and hydraulically opened to create a complex and well-connected network, pore pressure changes do not permeate far from the fractures. As a result, the microseismic pointset roughly corresponds to the size of the real fracture network which offers a means to estimate the stimulated rock volume (SRV). Although the producing fracture network could be smaller than the total SRV by a substantial percentage, it is expected that the effective network and the total SRV show a positive correlation. However, SRV is not the only indicator of well productivity. In a given SRV, the quality of the reservoir and parameters such as fracture conductivity and fracture spacing will affect production and can have a major impact on recovery calculations. In this study, stimulated rock volumes obtained from microseismic pointsets are correlated with actual field production. The correlations are used to illustrate how this concept can optimize treatment design, well spacing, and stage spacing through correlation of the reservoir response to hydraulic fracturing and production data. The correlation between production and SRV for each well shows that larger SRVs result in higher well production regardless of the percentage of the SRV that contains proppant filled fractures. The direct relationship of the microseismic pointsets and production can be used to predict a new well's potential productivity immediately upon completion of the stimulation job. This suggests that a key completion strategy is to create a large and effective SRV to provide maximum recovery and well performance monitored microseismically to provide production prediction.

Performance-based fractal fracture model for complex fracture network simulation

“…Jones et al (2013) and Chen et al (2016) showed that the complexity and connectivity of the fracture network have great influences on production, implying that the bi-wing fracture model is not fit for the simulation of a complex fracture network. Otherwise, it is found that the critical zone of the stimulated volume is always smaller than the area obtained by the microseismic monitoring (Friedrich and Milliken 2013;Rahimi Zeynal et al 2014). These problems motivate the development of a fracture model that is multileveled and easily adjusted.…”

“…According to the reservoir simulation, the well performance should be higher if the overall stimulated reservoir area is regarded as being filled with a high conductivity fracture network, so the real contribution of the networks is overestimated. Rahimi Zeynal et al (2014) proposed that the contributing stimulated volume is relatively smaller than that of the microseismic monitored through rate-transient analysis. The effective proppant volume (EPV) is different from the SRV (Friedrich and Milliken 2013).…”

A Validation Assessment of Microseismic Monitoring

“…A similar evaluation in horizontal wells is much more difficult because of the complexities of the completion and stimulation. Numerous studies of hydraulic fractures in horizontal wells have resulted in very complex distribution of microseismicity in shale reservoirs (e.g., Maxwell et al 2002;Daniels et al 2007;Waters et al 2009a;Ciezobka and Salehi 2013;Suliman et al 2013;Zeynal et al 2014;Virues et al 2015, etc. ), but none of these had ancillary data that actually validated the generation of a complex network.…”

“…Considerable effort has been expended to construct DFNs from microseismic data (e.g., Mayerhofer et al 2006;Jacot et al 2010;Eisner et al 2010;Cipolla et al 2011a;Neuhaus et al 2012Neuhaus et al , 2013Zeynal et al 2014;Cipolla and Wallace 2014;Yu et al 2015, etc. ) and use them for engineering purposes-either modeling the stimulation, production, or both.…”

Multidomain Two-Phase Flow Model to Study the Impacts of Hydraulic Fracturing on Shale Gas Production

“…Clearly, the shale gas production behavior strongly correlates with the SRV. 12,14 The associated models are proposed based on the concept of the SRV. The analytical models for single gas flow were first proposed based on linear flow assumption.…”