Increasing confidence in estimating stimulated reservoir volume by integrating RTA and microseismic analysis
Peyman Moradi, Katie Jeziorski and Doug Angus
Journal name: First Break
Issue: Vol 37, No 10, October 2019 pp. 65 - 73
Info: Article, PDF ( 6.53Mb )
Rate Transient Analysis (RTA) and microseismic monitoring are gaining momentum in modelling Stimulated Reservoir Volume (SRV) in Multi-Frac Horizontal Wells (MFHWs) in unconventional reservoirs. From a behavioural perspective, RTA uses history matching and production data analysis to estimate fracture volume and productivity, and microseismic analysis maps fracking- induced micro-earthquakes to calibrate the fracture network from a spatiotemporal point of view. Defining the concepts of normalized rate, material balance time and pseudo-time, dynamic drainage volume, together with convolution, deconvolution and analytical models, make RTA a powerful and computationally efficient tool for modelling MFHWs (Blasingame et al., 1991; Agarwal et al., 1998, Mattar and Anderson, 2003). Poe (2005) proposed a rate-transient analysis method for evaluating the performance of wells with limited pressure data using the superposition theory and dimensionless parameters. Soliman and Adams (2010) estimated fracture properties by applying Flow Regime Identification (FRI) plots to early production data, followed by using analytical models derived for each distinct flow regime. Kuchuk et al. (2016) calibrated reservoir models by history-matching the transient flow rate and pressure measurements. Brown (2009), Stalgorova and Mattar (2012a, 2012b), Deng et al. (2015), and Yuan et al. (2015) divided the reservoir into a series of linear flow regions and derived analytical pressure transient models from the pressure diffusion equation, not only to confirm the validity of the identification, characterization and diagnostic analyses but also to provide production forecasts and carry out optimization studies. Clarkson et al. (2015) successfully applied RTA analytical and semi-analytical modelling techniques to a gas condensate MFHW in a Western Canadian Basin, highlighting the fact that building a predictive understanding of drainage volume dynamics is best started with physics-based analytical models rather than multi-phase numerical simulations. This is particularly important in unconventional reservoirs where the complex, small-scale physics and rock-fluid interactions significantly hinder gathering enough measurements to support the numerically added complexities.