Quantifying Changes in Hydraulic Fracture Properties Using a Multi-Well Integrated Discrete Fracture Network (DFN) And Reservoir Simulation Model in an Unconventional Wolfcamp Fm., Midland Basin, West Texas

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.

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Methodology To Transfer The Discrete Fracture Network (DFN) Into A Double Porosity Simulation Model

10.3997/2214-4609.2019481011 ◽

2019 ◽

Author(s):

H.J. Marquez Alvarez ◽

R. Chandomi ◽

L.F. Guaiquirian

Keyword(s):

Simulation Model ◽

Fracture Network ◽

Double Porosity ◽

Discrete Fracture Network ◽

Discrete Fracture

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Constructing Dual-Porosity Models from High-Resolution Discrete-Fracture Models Using Deep Neural Networks

10.2118/203901-ms ◽

2021 ◽

Author(s):

Xupeng He ◽

Ryan Santoso ◽

Marwa Alsinan ◽

Hyung Kwak ◽

Hussein Hoteit

Keyword(s):

Simulation Model ◽

Oil Recovery ◽

Reservoir Simulation ◽

Water Saturation ◽

Dual Porosity ◽

Discrete Fracture Model ◽

Discrete Fracture ◽

Dual Porosity Model ◽

Reservoir Simulation Model ◽

Porosity Model

Abstract Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects.

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

10.2523/iptc-18884-ms ◽

2016 ◽

Cited By ~ 3

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

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Dynamic calibration of the Shaikan Jurassic full-field fractured reservoir model through single-well DST and multi-well interference discrete fracture network simulation

Journal of the Geological Society ◽

10.1144/jgs2019-137 ◽

2020 ◽

Vol 177 (6) ◽

pp. 1294-1314

Author(s):

Neil Price ◽

Paul LaPointe ◽

Kevin Parmassar ◽

Chunmei Shi ◽

Phil Diamond ◽

...

Keyword(s):

Dynamic Simulation ◽

Fractured Reservoirs ◽

Fracture Network ◽

Discrete Fracture Network ◽

Full Field ◽

Content Type ◽

Fracture Properties ◽

Field Development ◽

Discrete Fracture ◽

Supplementary Material

The hydraulic behaviour of the fractures in a fractured carbonate reservoir is a function of fracture intensity, aperture, intrinsic permeability, length, height and orientation, all of which influence the scale of connectivity and ultimately storage, productivity and reserves. If a geologically realistic fracture model is not appropriately incorporated into upscaled fracture properties for a dynamic simulation, it may still be possible to match a short production history, but calculations of field-wide fracture pore volumes and forecasts of future reservoir development may be poor and uncertain. To accurately represent the fractures, discrete fracture network (DFN) models were built and used to constrain fracture geometries and their hydraulic properties for use in forecasting, field development options and uncertainty characterization. The workflow illustrated in this paper shows how a DFN may be validated and calibrated through the simulation of transient bottom hole pressures from individual drill stem tests and pressure interference data, followed by upscaling to a full-field dynamic simulation model. This DFN-to-simulation workflow, applicable to most conventional fractured reservoirs, successfully matched reservoir pressure history for the field as a whole and for individual wells without having to locally modify any of the upscaled fracture properties around the wells. Sensitivity analysis identified key fracture drivers having the greatest impact upon the history match, and these were combined to produce history matched Low and High Case models. Production forecasts for the Low, Base and High Cases were used to predict reserves, manage risk and optimize the field development plan.Supplementary material: Supplementary figures are available at https://doi.org/10.6084/m9.figshare.c.5001203Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs

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