scholarly journals Particle-Based Workflow for Modeling Uncertainty of Reactive Transport in 3D Discrete Fracture Networks

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2502 ◽  
Author(s):  
Phuong Thanh Vu ◽  
Chuen-Fa Ni ◽  
Wei-Ci Li ◽  
I-Hsien Lee ◽  
Chi-Ping Lin
Keyword(s):  
Fractured Rocks ◽  
Reactive Transport ◽  
Fracture Network ◽  
Reaction Model ◽  
Transport Processes ◽  
Fracture Networks ◽  
Flow Paths ◽  
Complex Fracture ◽  
Discrete Fracture Networks ◽  
Discrete Fracture

Fractures are major flow paths for solute transport in fractured rocks. Conducting numerical simulations of reactive transport in fractured rocks is a challenging task because of complex fracture connections and the associated nonuniform flows and chemical reactions. The study presents a computational workflow that can approximately simulate flow and reactive transport in complex fractured media. The workflow involves a series of computational processes. Specifically, the workflow employs a simple particle tracking (PT) algorithm to track flow paths in complex 3D discrete fracture networks (DFNs). The PHREEQC chemical reaction model is then used to simulate the reactive transport along particle traces. The study illustrates the developed workflow with three numerical examples, including a case with a simple fracture connection and two cases with a complex fracture network system. Results show that the integration processes in the workflow successfully model the tetrachloroethylene (PCE) and trichloroethylene (TCE) degradation and transport along particle traces in complex DFNs. The statistics of concentration along particle traces enables the estimations of uncertainty induced by the fracture structures in DFNs. The types of source contaminants can lead to slight variations of particle traces and influence the long term reactive transport. The concentration uncertainty can propagate from parent to daughter compounds and accumulate along with the transport processes.

scholarly journals Water Intrusion Characterization in Naturally Fractured Gas Reservoir Based on Spatial DFN Connectivity Analysis

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4235
Author(s):  
Pengyu Chen ◽  
Mauricio Fiallos-Torres ◽  
Yuzhong Xing ◽  
Wei Yu ◽  
Chunqiu Guo ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Water Flow ◽  
Fracture Network ◽  
Connectivity Analysis ◽  
Fracture Networks ◽  
Flow Paths ◽  
Water Intrusion ◽  
Discrete Fracture ◽  
Single Well ◽  
Water Flow Paths

In this study, the non-intrusive embedded discrete fracture model (EDFM) in combination with the Oda method are employed to characterize natural fracture networks fast and accurately, by identifying the dominant water flow paths through spatial connectivity analysis. The purpose of this study is to present a successful field case application in which a novel workflow integrates field data, discrete fracture network (DFN), and production analysis with spatial fracture connectivity analysis to characterize dominant flow paths for water intrusion in a field-scale numerical simulation. Initially, the water intrusion of single-well sector models was history matched. Then, resulting parameters of the single-well models were incorporated into the full field model, and the pressure and water breakthrough of all the producing wells were matched. Finally, forecast results were evaluated. Consequently, one of the findings is that wellbore connectivity to the fracture network has a considerable effect on characterizing the water intrusion in fractured gas reservoirs. Additionally, dominant water flow paths within the fracture network, easily modeled by EDFM as effective fracture zones, aid in understanding and predicting the water intrusion phenomena. Therefore, fracture clustering as shortest paths from the water contacts to the wellbore endorses the results of the numerical simulation. Finally, matching the breakthrough time depends on merging responses from multiple dominant water flow paths within the distributions of the fracture network. The conclusions of this investigation are crucial to field modeling and the decision-making process of well operation by anticipating water intrusion behavior through probable flow paths within the fracture networks.

Discrete-Fracture-Network Generation From Microseismic Data by Use of Moment-Tensor- and Event-Location-Constrained Hough Transforms

SPE Journal ◽  
2016 ◽  
Vol 21 (01) ◽  
pp. 221-232 ◽  
Author(s):  
Xin Yu ◽  
Jim Rutledge ◽  
Scott Leaney ◽  
Shawn Maxwell
Keyword(s):  
Moment Tensor ◽  
Fracture Network ◽  
Fracture Plane ◽  
Fracture Networks ◽  
Complex Fracture ◽  
Location Uncertainty ◽  
Microseismic Data ◽  
Uncertainty Estimates ◽  
Discrete Fracture ◽  
Event Location

Summary Reservoir simulation and prediction of production associated with hydraulic-fracturing require the input of the fracture geometry and the fracture properties such as the porosity and retained permeability. Various methods were suggested and applied for deriving discrete fracture networks (DFNs) from microseismic data as a framework for modeling reservoir performance. Although microseismic data are the best diagnostics for revealing the volume of rock fractured, its incompleteness in representing the deformation induced presents a challenge to calibrate and represent complex fracture networks created and connected during hydraulic-fracture stimulation. We present an automated method to generate DFN models constrained by the microseismic locations and fracture plane orientations derived from moment-tensor analysis. We use a Hough-transform technique to find significant planar features from combinations of the microseismic source locations. We have modified the technique with an equal-probability voting scheme to remove an inherent bias for horizontal planes. The voting mechanism is a general grid search in the space of fracture strike, dip, and location (φ,θ,r, respectively) with grid-cell sizes scaled by uncertainty estimates of φ,θ,r. We constrain fracture orientations with weighting on the basis of the moment-tensor orientations of neighboring events and their associated uncertainties. With two case studies, we demonstrate that our automated technique can reliably extract the complex fracture network on the basis of good matches with the event-cloud trends and the input moment-tensor orientations. We also tested the sensitivity of the technique to event-location uncertainty. With increasing location uncertainty, the details of the fracture network extracted are diminished with events grouping to larger-scale features, but the general shape and orientation of the fracture network obtained are insensitive to the location uncertainty.

Numerical strategies for characterizing fractured rock from heat tracer experiments

2020 ◽  
Author(s):  
Delphine Roubinet ◽  
Zitong Zhou ◽  
Daniel Tartakovsky
Keyword(s):  
Density Function ◽  
Test Data ◽  
Fractured Rocks ◽  
Reactive Transport ◽  
Transport Model ◽  
Fracture Network ◽  
Tracer Test ◽  
Transport Processes ◽  
Distributed Temperature Sensing ◽  
Experimental Conditions

<p>Characterization of fractured rocks is a key challenge for optimizing heat harvesting in geothermal systems. The use of heat as a tracer, facilitated by the development of such advanced techniques as active line source (ALS) borehole heating and the distributed temperature sensing (DTS), shows the great potential for characterizing fractured rocks. However, there is so far a limited number of theoretical and numerical studies on how these tests could be used for estimating both fracture-network and rock-matrix properties.</p><p>We use deep neural networks to describe heat tracer test data and demonstrate how the cumulative density function (CDF) or probability density function (PDF) of the heat tracer test data can be deployed in the inversion mode, i.e., to infer the fracture parameters with. Our approach utilizes the methods of distributions, developed previously to estimate the CDF of solute concentration described by a reactive transport model with uncertain parameters and inputs. The method is applied to analyze several synthetic heat tracer test datasets obtained from a particle-based forward model of transport processes in heterogeneous fractured rocks. The study considers alternative representations of fracture networks with a large range of variation of the fracture network properties, as well as several experimental conditions (e.g., ambient/forced thermal and hydraulic conditions, pulse/continuous changes in temperature). This allows us to characterize the system by combining the information from several thermal tests.</p>

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