Integrated Characterization of Hydraulically Fractured Shale-Gas Reservoirs—Production History Matching

Abstract

Advancements in horizontal-well drilling and multistage hydraulic fracturing have enabled economically viable gas production from tight formations. Reservoir-simulation models play an important role in the production forecasting and field-development planning. To enhance their predictive capabilities and to capture the uncertainties in model parameters, one should calibrate stochastic reservoir models to both geologic and flow observations.In this paper, a novel approach to characterization and history matching of hydrocarbon production from a hydraulic-fractured shale is presented. This new methodology includes generating multiple discrete-fracture-network (DFN) models, upscaling the models for numerical multiphase-flow simulation, and updating the DFN-model parameters with dynamic-flow responses. First, measurements from hydraulic-fracture treatment, petrophysical interpretation, and in-situ stress data are used to estimate the initial probability distribution of hydraulic-fracture and induced-microfracture parameters, and multiple initial DFN models are generated. Next, the DFN models are upscaled into an equivalent continuum dual-porosity model with analytical techniques. The upscaled models are subjected to the flow simulation, and their production performances are compared with the actual responses. Finally, an assisted-history-matching algorithm is implemented to assess the uncertainties of the DFN-model parameters. Hydraulicfracture parameters including half-length and transmissivity are updated, and the length, transmissivity, intensity, and spatial distribution of the induced fractures are also estimated.The proposed methodology is applied to facilitate characterization of fracture parameters of a multifractured shale-gas well in the Horn River basin. Fracture parameters and stimulated reservoir volume (SRV) derived from the updated DFN models are in agreement with estimates from microseismic interpretation and rate-transient analysis. The key advantage of this integrated assisted-history-matching approach is that uncertainties in fracture parameters are represented by the multiple equally probable DFN models and their upscaled flow-simulation models, which honor the hard data and match the dynamic production history. This work highlights the significance of uncertainties in SRV and hydraulic-fracture parameters. It also provides insight into the value of microseismic data when integrated into a rigorous production-history-matching work flow.

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

“…Other approaches are: artificial neural networks (Al-Anazi and Babadagli, 2009), Markov Chain Monte Carlo (Ginting et al, 2011), probability perturbation method (Suzuki et al, 2007), recovery curve method (Ghaedi et al, 2015), Discrete Fracture Network flow simulator (Lange, 2009), Ensemble Kalman Filter (Lu and Zhang, 2015;Nejadi et al, 2015), and Kernel principal component analysis (Paico, 2008).…”

Fast Assisted History Matching of Fractured Vertical Well in Coalbed Methane Reservoirs Using the Bayesian Adaptive Direct Searching Algorithm

“…The AHM method in the petroleum engineering field was first proposed for conventional sandstone reservoirs to deduce the formation properties that are difficult to measure directly from field-scale historical production data [17]. During the past two decades, AHM has been extended to solve various inverse problems such as (i) interpretation of relative permeability from laboratory core flooding data, (ii) estimation of permeability and skin factor of multilayered reservoir from injection-falloff test and (iii) characterization of hydraulic fractures in shale reservoirs from seismic and/or production data [18][19][20]. These previous studies indicated that the AHM is capable of deriving reasonable results with the proper experimental design and the implementation of robust algorithms.…”