scholarly journals A new methodology to train fracture network simulation using multiple-point statistics

Solid Earth ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 537-559 ◽  
Author(s):  
Pierre-Olivier Bruna ◽  
Julien Straubhaar ◽  
Rahul Prabhakaran ◽  
Giovanni Bertotti ◽  
Kevin Bisdom ◽  
...  
Keyword(s):  
Fractured Reservoirs ◽  
Fracture Network ◽  
Natural Fracture ◽  
Statistical Characteristics ◽  
Reference Network ◽  
Multiple Point ◽  
Fracture Networks ◽  
Multiple Point Statistics ◽  
Mps Method ◽  
Training Images

Abstract. Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and efficiency at depth. However, even when supplementing fractured reservoir models with outcrop data, gaps will remain in the model and fracture network extrapolation methods are required. In this paper we used fracture networks interpreted from two outcrops from the Apodi area, Brazil, to present a revised and innovative method of fracture network geometry prediction using the multiple-point statistics (MPS) method. The MPS method presented in this article uses a series of small synthetic training images (TIs) representing the geological variability of fracture parameters observed locally in the field. The TIs contain the statistical characteristics of the network (i.e. orientation, spacing, length/height and topology) and allow for the representation of a complex arrangement of fracture networks. These images are flexible, as they can be simply sketched by the user. We proposed to simultaneously use a set of training images in specific elementary zones of the Apodi outcrops in order to best replicate the non-stationarity of the reference network. A sensitivity analysis was conducted to emphasise the influence of the conditioning data, the simulation parameters and the training images used. Fracture density computations were performed on selected realisations and compared to the reference outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is adaptable in terms of training images and probability maps to ensure that the geological complexity in the simulation process is accounted for. It can be used on any type of rock containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and fluid flow efficiency in water, geothermal or hydrocarbon fractured reservoirs.

Modelling large-scale fractured reservoirs efficiently for geothermal energy and groundwater flow

2020 ◽  
Author(s):  
Simon Oldfield ◽  
Mikael Lüthje ◽  
Michael Welch ◽  
Florian Smit
Keyword(s):  
Fluid Flow ◽  
Geothermal Energy ◽  
Large Scale ◽  
Fractured Reservoirs ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Persistent Problem ◽  
Fracture Evolution ◽  
Rapid Generation

<p>Large scale modelling of fractured reservoirs is a persistent problem in representing fluid flow in the subsurface. Considering a geothermal energy prospect beneath the Drenthe Aa area, we demonstrate application of a recently developed approach to efficiently predict fracture network geometry across an area of several square kilometres.</p><p>Using a strain based method to mechanically model fracture nucleation and propagation, we generate a discretely modelled fracture network consisting of individual failure planes, opening parallel and perpendicular to the orientation of maximum and minimum strain. Fracture orientation, length and interactions vary following expected trends, forming a connected fracture network featuring population statistics and size distributions comparable to outcrop examples.</p><p>Modelled fracture networks appear visually similar to natural fracture networks with spatial variation in fracture clustering and the dominance of major and minor fracture trends.</p><p>Using a network topology approach, we demonstrate that the predicted fracture network shares greater geometric similarity with natural networks. Considering fluid flow through the model, we demonstrate that hydraulic conductivity and flow anisotropy are strongly dependent on the geometric connection of fracture sets.</p><p>Modelling fracture evolution mechanically allows improved representation of geometric aspects of fracture networks to which fluid flow is particularly sensitive. This method enables rapid generation of discretely modelled fractures over large areas and extraction of suitable summary statistics for reservoir simulation. Visual similarity of the output models improves our ability to compare between our model and natural analogues to consider model validation.</p>

scholarly journals A new methodology to train fracture network simulation using Multiple Point Statistic

2018 ◽  
Author(s):  
Pierre-Olivier Bruna ◽  
Julien Straubhaar ◽  
Rahul Pranhakaran ◽  
Giovanni Bertotti ◽  
Kevin Bisdom ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Fracture Network ◽  
Natural Fracture ◽  
Multiple Point ◽  
Fracture Aperture ◽  
Strong Impact ◽  
Data Sampling ◽  
Natural Fractures ◽  
Training Images ◽  
Network Geometry

Abstract. Natural fractures have a strong impact on flow and storage properties of reservoirs. Their distribution in the subsurface is largely unknown mainly due to their sub-seismic scale and to the scarcity of available data sampling them (borehole). Outcrop can be considered as analogues where natural fracture characteristics can be extracted. However, acquiring fracture data on outcrops may produce a large amount of information that needs to be processed and efficiently interpreted to capture the key parameters defining fracture network geometry. Outcrops thus become a natural laboratory where the interpreted fracture network can be tested mechanically (fracture aperture, distribution of strain/stress) and dynamically (fluid flow simulations (Bisdom et al., 2017). The goal of this paper is to propose the multiple point statistics (MPS) method as a new tool to quickly predict the geometry of a fracture network in both surface and subsurface conditions. This sequential simulation method is based on the creation of small and synthetic training images representing fracture distribution parameters observe in the field. These training images represent the complexity of the geological object or processes to be simulated and can be simply designed by the user. In this paper we chose to use multiple training images and a probability map to represent the fracture network geometry and its potential variability in a non-stationary manner. The method was tested on a fracture pavement (2D flat surface) acquired using a drone in the Apodi area in Brazil. Fractures were traced manually on images of the outcrop and constitute the reference on which the fracture network simulations will be based. A sensitivity analysis emphasizing the influence of the conditioning data, the simulation parameters and the used training images was conducted on the obtained simulations. Stress-induced fracture aperture calculations were performed on the best realisations and on the original outcrop fracture interpretation to qualitatively evaluate the accuracy of our simulations. The method proposed here is innovative and adaptable. It can be used on any type of rocks containing natural fractures in any kind of tectonic context. This workflow can also be applied to the subsurface to predict the fracture arrangement and its fluid flow efficiency in water, heat or hydrocarbon reservoirs.

Numerical Simulation of Complex Fracture Network Development by Hydraulic Fracturing in Naturally Fractured Ultratight Formations

2013 ◽  
Author(s):  
Hannes Hofmann ◽  
Tayfun Babadagli ◽  
Günter Zimmermann
Keyword(s):  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Network Development ◽  
Complex Fracture ◽  
Fracture Patterns ◽  
Fracture Systems ◽  
Fracture Development ◽  
Complex Fracture Network ◽  
Treatment Designs

The creation of large complex fracture networks by hydraulic fracturing is imperative for enhanced oil recovery from tight sand or shale reservoirs, tight gas extraction, and Hot-Dry-Rock (HDR) geothermal systems to improve the contact area to the rock matrix. Although conventional fracturing treatments may result in bi-wing fractures, there is evidence by microseismic mapping that fracture networks can develop in many unconventional reservoirs, especially when natural fracture systems are present and the differences between the principle stresses are low. However, not much insight is gained about fracture development as well as fluid and proppant transport in naturally fractured tight formations. In order to clarify the relationship between rock and treatment parameters, and resulting fracture properties, numerical simulations were performed using a commercial Discrete Fracture Network (DFN) simulator. A comprehensive sensitivity analysis is presented to identify typical fracture network patterns resulting from massive water fracturing treatments in different geological conditions. It is shown how the treatment parameters influence the fracture development and what type of fracture patterns may result from different treatment designs. The focus of this study is on complex fracture network development in different natural fracture systems. Additionally, the applicability of the DFN simulator for modeling shale gas stimulation and HDR stimulation is critically discussed. The approach stated above gives an insight into the relationships between rock properties (specifically matrix properties and characteristics of natural fracture systems) and the properties of developed fracture networks. Various simulated scenarios show typical conditions under which different complex fracture patterns can develop and prescribe efficient treatment designs to generate these fracture systems. Hydraulic stimulation is essential for the production of oil, gas, or heat from ultratight formations like shales and basement rocks (mainly granite). If natural fracture systems are present, the fracturing process becomes more complex to simulate. Our simulation results reveal valuable information about main parameters influencing fracture network properties, major factors leading to complex fracture network development, and differences between HDR and shale gas/oil shale stimulations.

A Sensitivity Analysis for Effective Parameters on 2D Fracture-Network Permeability

2009 ◽  
Vol 12 (03) ◽  
pp. 455-469 ◽  
Author(s):  
Alireza Jafari ◽  
Tayfun Babadagli
Keyword(s):  
Sensitivity Analysis ◽  
Experimental Design ◽  
Fractured Reservoirs ◽  
Fracture Network ◽  
Static Model ◽  
Naturally Fractured Reservoirs ◽  
Single Fracture ◽  
Data Sets ◽  
Fracture Networks ◽  
Fractal Characteristics

Summary Fracture-network mapping and estimation of its permeability constitute two major steps in static-model preparation of naturally fractured reservoirs. Although several different analytical methods were proposed in the past for calculating fracture-network permeability (FNP), different approaches are still needed for practical use. We propose a new and practical approach to estimate FNP using statistical and fractal characteristics of fracture networks. We also provide a detailed sensitivity analysis to determine the relative importance of fracture-network parameters on the FNP in comparison to single-fracture conductivity using an experimental-design approach. The FNP is controlled by many different fracture-network parameters such as fracture length, density, orientation, aperture, and single-fracture connectivity. Five different 2D fracture data sets were generated for random and systematic orientations. In each data set, 20 different combinations of fracture density and length for different orientations were tested. For each combination, 10 different realizations were generated. The length was considered as constant and variable. This yielded a total of 1,000 trials. The FNPs were computed through a commercial discrete-fracture-network (DFN) modeling simulator for all cases. Then, we correlated different statistical and fractal characteristics of the networks to the measured FNPs using multivariable-regression analysis. Twelve fractal (sandbox, box counting, and scanline fractal dimensions) and statistical (average length, density, orientation, and connectivity index) parameters were tested against the measured FNP for synthetically generated fracture networks for a wide range of fracture properties. All cases were above the percolation threshold to obtain a percolating network, and the matrix effect was neglected. The correlation obtained through this analysis using four data sets was tested on the fifth one with known permeability for verification. High-quality match was obtained. Finally, we adopted an experimental-design approach to identify the most-critical parameters on the FNP for different fracture-network types. The results are presented as Pareto charts. It is believed that the new method and results presented in this paper will be useful for practitioners in static-model development of naturally fractured reservoirs and will shed light on further studies on modeling and understanding the transmissibility characteristics of fracture networks. It should be emphasized that this study was conducted on 2D fracture networks and could be extended to 3D models. This, however, requires further algorithm development to use 2D fractal characteristics for 3D systems and/or development of fractal measurement techniques for a 3D system. This study will provide a guideline for this type of research.

scholarly journals The Role of In Situ Stress in Organizing Flow Pathways in Natural Fracture Networks at the Percolation Threshold

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Chuanyin Jiang ◽  
Xiaoguang Wang ◽  
Zhixue Sun ◽  
Qinghua Lei
Keyword(s):  
Fluid Flow ◽  
Percolation Threshold ◽  
Fractured Reservoirs ◽  
Fracture Network ◽  
In Situ Stress ◽  
Natural Fracture ◽  
Fracture Aperture ◽  
Dilation Effect ◽  
Stress Dependent

We investigated the effect of in situ stresses on fluid flow in a natural fracture network. The fracture network model is based on an actual critically connected (i.e., close to the percolation threshold) fracture pattern mapped from a field outcrop. We derive stress-dependent fracture aperture fields using a hybrid finite-discrete element method. We analyze the changes of aperture distribution and fluid flow field with variations of in situ stress orientation and magnitude. Our simulations show that an isotropic stress loading tends to reduce fracture apertures and suppress fluid flow, resulting in a decrease of equivalent permeability of the fractured rock. Anisotropic stresses may cause a significant amount of sliding of fracture walls accompanied with shear-induced dilation along some preferentially oriented fractures, resulting in enhanced flow heterogeneity and channelization. When the differential stress is further elevated, fracture propagation becomes prevailing and creates some new flow paths via linking preexisting natural fractures, which attempts to increase the bulk permeability but attenuates the flow channelization. Comparing to the shear-induced dilation effect, it appears that the propagation of new cracks leads to a more prominent permeability enhancement for the natural fracture system. The results have particularly important implications for predicting the hydraulic responses of fractured rocks to in situ stress fields and may provide useful guidance for the strategy design of geofluid production from naturally fractured reservoirs.

scholarly journals Predictive Modelling of Naturally Fractured Reservoirs Using Geomechanics and Flow Simulation

GeoArabia ◽  
2001 ◽  
Vol 6 (1) ◽  
pp. 27-42
Author(s):  
Stephen J. Bourne ◽  
Lex Rijkels ◽  
Ben J. Stephenson ◽  
Emanuel J.M. Willemse
Keyword(s):  
Fractured Reservoirs ◽  
Flow Simulation ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Production Data ◽  
Well Test ◽  
Fracture Networks ◽  
Field Scale ◽  
Fracture Permeability ◽  
Naturally Fractured

ABSTRACT To optimise recovery in naturally fractured reservoirs, the field-scale distribution of fracture properties must be understood and quantified. We present a method to systematically predict the spatial distribution of natural fractures related to faulting and their effect on flow simulations. This approach yields field-scale models for the geometry and permeability of connected fracture networks. These are calibrated by geological, well test and field production data to constrain the distributions of fractures within the inter-well space. First, we calculate the stress distribution at the time of fracturing using the present-day structural reservoir geometry. This calculation is based on a geomechanical model of rock deformation that represents faults as frictionless surfaces within an isotropic homogeneous linear elastic medium. Second, the calculated stress field is used to govern the simulated growth of fracture networks. Finally, the fractures are upscaled dynamically by simulating flow through the discrete fracture network per grid block, enabling field-scale multi-phase reservoir simulation. Uncertainties associated with these predictions are considerably reduced as the model is constrained and validated by seismic, borehole, well test and production data. This approach is able to predict physically and geologically realistic fracture networks. Its successful application to outcrops and reservoirs demonstrates that there is a high degree of predictability in the properties of natural fracture networks. In cases of limited data, field-wide heterogeneity in fracture permeability can be modelled without the need for field-wide well coverage.

scholarly journals Clustering, Connectivity and Flow Responses of Deterministic Fractal-Fracture Networks

2020 ◽  
Vol 54 ◽  
pp. 149-156
Author(s):  
Ajay K. Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Spatial Clustering ◽  
Flow Behavior ◽  
Flow Simulation ◽  
Fractal Dimensions ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Fracture Models ◽  
Fractal Fracture

Abstract. It is well known that fracture networks display self-similarity in many cases and the connectivity and flow behavior of such networks are influenced by their respective fractal dimensions. In the past, the concept of lacunarity, a parameter that quantifies spatial clustering, has been implemented by one of the authors in order to demonstrate that a set of seven nested natural fracture maps belonging to a single fractal system, but of different visual appearances, have different clustering attributes. Any scale-dependency in the clustering of fractures will also likely have significant implications for flow processes that depend on fracture connectivity. It is therefore important to address the question as to whether the fractal dimension alone serves as a reasonable proxy for the connectivity of a fractal-fracture network and hence, its flow response or, if it is the lacunarity, a measure of scale-dependent clustering, that may be used instead. The present study attempts to address this issue by exploring possible relationships between the fractal dimension, lacunarity and connectivity of fractal-fracture networks. It also endeavors to study the relationship between lacunarity and fluid flow in such fractal-fracture networks. A set of deterministic fractal-fracture models generated at different iterations and, that have the same theoretical fractal dimension are used for this purpose. The results indicate that such deterministic synthetic fractal-fracture networks with the same theoretical fractal dimension have differences in their connectivity and that the latter is fairly correlated with lacunarity. Additionally, the flow simulation results imply that lacunarity influences flow patterns in fracture networks. Therefore, it may be concluded that at least in synthetic fractal-fracture networks, rather than fractal dimension, it is the lacunarity or scale-dependent clustering attribute that controls the connectivity and hence the flow behavior.

Clustering, Connectivity and Flow in Naturally Fractured Reservoir Analogs

2021 ◽  
Author(s):  
Ajay K. Sahu ◽  
Ankur Roy
Keyword(s):  
Fractal Dimension ◽  
Oil And Gas ◽  
Flow Behavior ◽  
Oil And Gas Industry ◽  
Fracture Network ◽  
Natural Fracture ◽  
Fractured Reservoir ◽  
Fracture Networks ◽  
Gas Industry ◽  
Network Geometry

Abstract A previous study by the authors on synthetic fractal-fracture networks showed that lacunarity, a parameter that quantifies scale-dependent clustering in patterns, can be used as a proxy for connectivity and also, is an indicator of fluid flow in such model networks. In this research, we apply the concepts thus developed to the study of fractured reservoir analogs and seek solutions to more practical problems faced by modelers in the oil and gas industry. A set of seven nested fracture networks from the Devonian Sandstone of Hornelen Basin, Norway that have the same fractal-dimension but are mapped at different scales and resolutions is considered. We compare these seven natural fracture maps in terms of their lacunarity and connectivity values to test whether the former is a reasonable indicator of the latter. Additionally, these maps are also flow simulated by implementing a fracture continuum model and using a streamline simulator, TRACE3D. The values of lacunarity, connectivity and fluid recovery thus obtained are pairwise correlated with one another to look for possible relationships. The results indicate that while fracture maps that have the same fractal dimension show almost similar connectivity values, there exist subtle differences such that both the connectivity and clustering values change systematically with the scale at which the fracture networks are mapped. It is further noted that there appears to be a very good correlation between clustering, connectivity, and fluid recovery values for these fracture networks that belong to the same fractal system. The overall results indicate that while the fractal dimension is an important parameter for characterizing a specific type of fracture network geometry, it is the lacunarity or scale-dependent clustering attribute that controls connectivity in fracture maps and hence the flow properties. This research may prove helpful in quickly evaluating connectivity of fracture networks based on the lacunarity parameter. This parameter can therefore, be used for calibrating Discrete Fracture Network (DFN) models with respect to connectivity of reservoir analogs and can possibly replace the fractal dimension which is more commonly used in software that model DFNs. Additionally, while lacunarity has been mostly used for understanding network geometry in terms of clustering, we, for the first time, show how this may be directly used for understanding the potential flow behavior of fracture networks.

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