Hydraulic Fracture Optimization Based on Discrete Fracture Network Model in Shale Reservoirs

2016 ◽  
Author(s):  
Lidong Mi ◽  
Hanqiao Jiang ◽  
Yuhe Wang ◽  
Yanli Pei ◽  
Junjian Li ◽  
...  
Keyword(s):  
Network Model ◽  
Hydraulic Fracture ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Discrete Fracture ◽  
Shale Reservoirs

Development of Discrete Fracture Network Model Simulator, GeoFlow, for Evaluation of Three Dimensional Channeling Flow

2009 ◽  
Author(s):  
Takuya Ishibashi ◽  
Noriaki Watanabe ◽  
Nobuo Hirano ◽  
Atsushi Okamoto ◽  
Noriyoshi Tsuchiya
Keyword(s):  
Network Model ◽  
Three Dimensional ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Discrete Fracture ◽  
Channeling Flow

Discrete fracture network model of the vapor zone leakages at the Copahue geothermal field

2020 ◽  
Vol 140 ◽  
pp. 104155 ◽  
Author(s):  
H. Barcelona ◽  
R. Maffucci ◽  
D. Yagupsky ◽  
M. Senger ◽  
S. Bigi
Keyword(s):  
Network Model ◽  
Fracture Network ◽  
Geothermal Field ◽  
Discrete Fracture Network ◽  
Discrete Fracture

A fractal analysis of radon migration in discrete fracture network model

Chemosphere ◽  
2021 ◽  
Vol 266 ◽  
pp. 129010
Author(s):  
Shengyang Feng ◽  
Yurong Wu ◽  
Yong Liu ◽  
Xiangyang Li ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Network Model ◽  
Fractal Analysis ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Discrete Fracture

Microseismicity-constrained discrete fracture network models for stimulated reservoir simulation

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. B37-B47 ◽  
Author(s):  
Sherilyn Williams-Stroud ◽  
Chet Ozgen ◽  
Randall L. Billingsley
Keyword(s):  
Hydraulic Fracture ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Three Dimensions ◽  
Fracture Flow ◽  
Signal To Noise ◽  
Discrete Fracture ◽  
Microseismic Events ◽  
The Impact ◽  
Dfn Model

The effectiveness of hydraulic fracture stimulation in low-permeability reservoirs was evaluated by mapping microseismic events related to rock fracturing. The geometry of stage by stage event point sets were used to infer fracture orientation, particularly in the case where events line up along an azimuth, or have a planar distribution in three dimensions. Locations of microseismic events may have a higher degree of uncertainty when there is a low signal-to-noise ratio (either due to low magnitude or to propagation effects). Low signal-to-noise events are not as accurately located in the reservoir, or may fall below the detectability limit, so that the extent of fracture stimulated reservoir may be underestimated. In the Bakken Formation of the Williston Basin, we combined geologic analysis with process-based and stochastic fracture modeling to build multiple possible discrete fracture network (DFN) model realizations. We then integrated the geologic model with production data and numerical simulation to evaluate the impact on estimated ultimate recovery (EUR). We tested assumptions used to create the DFN model to determine their impact on dynamic calibration of the simulation model, and their impact on predictions of EUR. Comparison of simulation results, using fracture flow properties generated from two different calibrated DFN scenarios, showed a 16% difference in amount of oil ultimately produced from the well. The amount of produced water was strongly impacted by the geometry of the DFN model. The character of the DFN significantly impacts the relative amounts of fluids produced. Monitoring water cut with production can validate the appropriate DFN scenario, and provide critical information for the optimal method for well production. The results indicated that simulation of enhanced permeability using induced microseismicity to constrain a fracture flow property model is an effective way to evaluate the performance of reservoirs stimulated by hydraulic fracture treatments.

Construction of 3D Discrete Fracture Network Model using Structural and Hydrogeological Data

2020 ◽  
Author(s):  
Mohammadreza Jalali ◽  
Zhen Fang ◽  
Pooya Hamdi
Keyword(s):  
Rock Mass ◽  
Network Model ◽  
Fracture Network ◽  
Crystalline Rock ◽  
Discrete Fracture Network ◽  
Seismic Survey ◽  
Discrete Fracture ◽  
Dfn Model ◽  
Hydrogeological Data

<p>The presence of fractures and discontinuities in the intact rock affects the hydraulic, thermal, chemical and mechanical behavior of the underground structures. Various techniques have been developed to provide information on the spatial distribution of these complex features. LIDAR, for instance, could provide a 2D fracture network model of the outcrop, Geophysical borehole logs such as OPTV and ATV can be used to investigate 1D geometrical data (i.e. dip and dip direction, aperture) of the intersected fractures, and seismic survey can mainly offer a large structure distribution of the deep structures. The ability to combine all the existing data collected from various resources and different scales to construct a 3D discrete fracture network (DFN) model of the rock mass allows to adequately represent the physical behavior of the interested subsurface structure.</p><p>In this study, an effort on the construction of such a 3D DFN model is carried out via combination of various structural and hydrogeological data collected in fractured crystalline rock. During the pre-characterization phase of the In-situ Stimulation and Circulation (ISC) experiment [Amann et al., 2018] at the Grimsel Test Site (GTS) in central Switzerland, a comprehensive characterization campaign was carried out to better understand the hydromechanical characteristics of the existing structures. The collected multiscale and multidisciplinary data such as OPTV, ATV, hydraulic packer testing and solute tracer tests [Jalali et al., 2018; Krietsch et al., 2018] are combined, analyzed and interpreted to form a combined stochastic and deterministic DFN model using the FracMan software [Golder Associates, 2017]. For further validation of the model, the results from in-situ hydraulic tests are used to compare the simulated and measured hydraulic responses, allowing to evaluate whether the simulated model could reasonably represent the characteristics of the fracture network in the ISC experiment.</p><p> </p><p><strong>References</strong></p><ul><li>Amann, F., Gischig, V., Evans, K., Doetsch, J., Jalali, M., Valley, B., Krietsch, H., Dutler, N., Villiger, L., Brixel, B., Klepikova, M., Kittilä, A., Madonna, C., Wiemer, S., Saar, M.O., Loew, S., Driesner, T., Maurer, H., Giardini, D., 2018. The seismo-hydromechanical behavior during deep geothermal reservoir stimulations: open questions tackled in a decameter-scale in situ stimulation experiment. Solid Earth 9, 115–137.</li> <li>Golder Associates, 2017. FracMan User Documentation.  Golder Associates Inc, Redmond WA.</li> <li>Krietsch, H., Doetsch, J., Dutler, N., Jalali, M., Gischig, V., Loew, S., Amann, F., 2018. Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments. Scientific Data 5, 180269.</li> <li>Jalali, M., Klepikova, M., Doetsch, J., Krietsch, H., Brixel, B., Dutler, N., Gischig, V., Amann, F., 2018. A Multi-Scale Approach to Identify and Characterize the Preferential Flow Paths of a Fractured Crystalline Rock. Presented at the 2<sup>nd</sup> International Discrete Fracture Network Engineering Conference, American Rock Mechanics Association.</li> </ul>

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