Fracture Network Properties Research Articles

The goodness-of-fit of models for fracture trace length distribution: Does the power law provide a good fit?

Fracture trace length distributions are often assumed to follow a power law, which implies that the distribution is scale-independent. The present study tests this assumption by evaluating the goodness-of-fit of three statistical models—the power law, piecewise power law, and lognormal distribution—upon a dataset of 57 trace maps that cover a range of fracture modes, host rock types, network scales, and topologies. The goodness-of-fit was assessed through the unbiased Kolmogorov-Smirnov (KS) test, which accounts for the fitting procedure and the degrees of freedom of each model. The results show that the power law provides a poor fit to trace length distributions, being rejected in 24 trace maps at a significance level of 0.05. In contrast, the piecewise power law and lognormal distribution demonstrated better fits across the fracture networks, with the piecewise power law performing the best overall. The poor fit of the power law can be attributed to mechanical and chemical controls on fracture growth, mainly fracture abutment, as well as stress shadowing and cementation, which affect growth rate at different length scales and result in scale-dependent trace length distributions. The consistent poor fit of the power law across various fracture networks suggests that these controls are prevalent in natural systems. While the power law remains a simple and effective model for trace length distribution, it should be recognized that it overlooks such controls that can influence the mechanical and hydraulic properties of fracture networks. Meanwhile, the fit of the piecewise power-law suggests the existence of a characteristic length where a transition in fracture growth occurs.

Multiscale discrete fracture network modelling of shallow-water carbonates: East Agri Valley Basin, Southern Italy

We investigate the multiscale geometrical and dimensional properties of fracture and fault networks crosscutting Lower Jurassic to Cretaceous shallow-water carbonate rocks. The carbonates are exposed along the flanks of the Viggiano Mt., on the eastern side of the High Agri Valley Basin of southern Italy. Furthermore, we employ the results of field and digital structural analyses to derive the input parameters for subsequent Discrete Fracture Network modelling (DFN) of geocellular volumes whose dimension and architecture resemble those of the investigated sites. DFN modelling is carried out for geocellular volumes representing the studied bed packages (5m-side volumes), outcrops (50m-side volumes), and reservoir-scale carbonate cliffs (500m-side volumes). Notably, modelling is obtained considering the minimum mechanical aperture value obtained in the field for porosity computations and theoretical values of fracture hydraulic aperture for permeability computations. This is because the mechanical aperture values and roughness profiles collected in the field along single fractures are unreliable due to weathering and localized karst-related dissolution. Our findings reveal that a scale-dependent geometry characterizes the Cretaceous carbonates over three orders of magnitude. These results support previously published data, and contrast with those obtained for the Lower Jurassic carbonates, which suffer of some bias due to the quality of the exposures. After DFN modelling, we show that the amount of computed fracture porosity is mainly due to the SB and NSB fractures, and that equivalent permeability is greater within faulted rock volumes. In terms of horizontal permeability, which is the most reliable result after DFN modelling, we document near isotropic horizontal permeability ellipses at all scales of observations. At individual outcrops, we note that the permeability ellipses are elongated parallel to the dominant fault sets that denotes how localized strain affects the directionality of fluid flow. Finally, we summarize the original data in a conceptual model illustrating the modalities of meteoric fluid infiltration through the vadose zone, and then subsequent horizontal flow in the phreatic zone present within the fracture carbonates at shallow depths.

Inertial flow-induced fluid pressurization enhances the reactivation of rate-and-state faults

We reveal that inertial flow can introduce additional pressure perturbations which accelerate the instability of critically stressed faults. We integrate a poroelastic spring-slider model with rate-and-state friction (RSF), a nonlinear flow model characterized by the improved Izbash equation, and a universal visco-inertial permeability model. The effect of inertial flow depends on the fracture network properties, proximity to faults, and injection parameters. Significant inertial flow results in higher pressure magnitudes and pressurization rates, causing notable differences in predictions between linear and nonlinear flow models, as well as aging and slip laws. The impact of inertial flow is particularly pronounced within a specific permeability range and is enhanced by high-rate injections. The fault reactivation is delayed due to fault strengthening with RSF in response to increased pressurization rates. Conversely, the presence of inertial flow enhances fault reactivation and promotes unstable slip behaviors. Furthermore, the results demonstrate that cyclic injection increases the critical stiffness of faults without an adequate release of pore pressure in each cycle. The time interval of the cyclic injection is prolonged as inertial flow generates higher pressure levels. This study highlights the importance of inertial flow in the stability analysis of critically stressed faults.

Influence of structural properties and connectivity of initial fracture network on incipient karst genesis

A karst system is generally difficult to characterize especially regarding its conduits pattern and morphology. Even though speleology allows to map the geometry of cave systems, a large part of karst conduit networks remains unreachable underground. In this study, we use a reactive transport model that simulates dissolution processes in fracture networks to investigate the effect of discrete fracture network (DFN) properties (i.e., anisotropy, connectivity, fracture intersection types) on incipient karst conduit geometry under different boundary conditions (constant hydraulic head versus constant flow rate conditions, directional versus concentrated recharge conditions). We focused on single fracture (with/without cementation) and ladder like fracture networks which are common on limestone. Results show that fracture network properties, play a major role in the final geometry and topology of the incipient karst conduit patterns. Also, the presence of cement on a single fracture favors tortuous and curvilinear wormholes, while the anisotropy of the fracture network favors the formation of anisotropic incipient karst geometries. Finally, the type of hydraulic BC remains a major factor that governs dissolution processes and thus incipient karst conduit geometry.

Fluid flow and solute transport simulations in tight geologic formations: Discrete fracture network and continuous time random walk analyses

Understanding the effect of fracture network heterogeneity on flow and transport in tight formations with low permeability values is of great importance in a wide variety of applications, such as groundwater pollution and storage as well as water production. Although numerous studies have investigated solute transport in fractured media, investigation about scale effects on flow and transport in low-permeability aquifers is limited. More specifically, we are not aware of any study that has addressed how the continuous time random walk (CTRW) model parameters vary with scale and fracture network properties. To this end, we performed a set of numerical simulations in sparse fracture networks of sizes L = 20, 35, and 50 m under two different volumetric fracture counts (p30 = 0.05 and 0.1) using the discrete fracture network approach. The fractures in the networks were elliptical in shape, and their radii followed a truncated power-law distribution with exponents α = 1.5, 2, and 2.5. We simulated fluid flow using the Reynolds equation, and solute transport was simulated using particle tracking. The solute transport behavior was quantified by fitting the solution of the CTRW transport equation to the arrival time distributions. Results showed that as the truncated power law exponent α increased, the permeability of the networks decreased. In all the fracture networks studied, we found non-Fickian solute transport behavior deduced from small values of β(0.69≤β≤1.25), the exponent in the truncated power-law arrival time distribution in the CTRW model. In our fracture networks, the aperture size spanned less than one order of magnitude meaning that the fracture permeability distribution was relatively narrow. Therefore, the observed non-Fickian behavior should be attributed to sparse fracture networks and low connectivity. We found that the effective permeability, keff, at p30 = 0.05 was more scale-dependent compared to that at p30 = 0.1 because the fracture networks with p30 = 0.05 were less connected and, thus, more heterogeneous. Our simulation also disclosed the scale dependence of the CTRW model parameters. We conducted multiple linear regression analysis to link the CTRW model parameters to the network size as well as the geometrical and topological properties of the network. Results of regression analysis showed that all the CTRW model parameters except the average solute velocity were significantly dependent on the system size L (p-value < 0.05). Further investigations using a broader range of fracture network properties, such as broader aperture size distributions, smaller and larger network sizes and volumetric fracture counts are still required.

A semi-analytical rate-transient analysis model for fractured horizontal well in tight reservoirs under multiphase flow conditions

Abstract Rate-transient analysis (RTA) has been widely applied to extract reservoir/fracture properties using analytical and semi-analytical methods with simplifying assumptions. However, current RTA models may lead to misdiagnosis of flow regimes and incorrect estimates of reservoir/fracture information when complex fracture network, multiphase flow, and pressure-dependent properties occur in tight reservoirs simultaneously. A semi-analytical model is developed to account for multiphase flow, complex fracture network, and pressure-dependent properties. The technique uses the black oil formulation and butterfly model to determine three nonlinear partial differential equations (PDEs) that describe the flow of oil, gas, and water in the reservoir with complex fracture network. A modified Boltzmann variable considering the heterogeneity of complex fracture network is proposed to convert the fluid flow PDEs to a set of ordinary differential equations (ODEs) that can be solved through the Runge-Kutta method. A new rate-transient analysis workflow is also developed to improve flow regime identification (ID) and the accuracy for tight oil reservoirs with complex fracture network. It is applied to a synthetic case with equivalently modeled complex fracture network and multiphase flow. The estimated fracture properties are in excellent agreement with model inputs.

Dynamic Pressure Analysis of Shale Gas Wells Considering Three-Dimensional Distribution and Properties of the Hydraulic Fracture Network

A major challenge in transient pressure analysis for shale gas wells is their complex transient flow behavior and fracturing parameters. While numerical simulations offer high accuracy, analytical models are attractive for transient pressure analysis due to their high computational efficiency and broad applicability. However, traditional analytical models are often oversimplified, making it difficult to capture the complex seepage system, and three-dimensional fracture characteristics are seldom considered. To address these limitations, this study presents a comprehensive hybrid model that characterizes the transient flow behavior and analyzes the pressure response of a fractured shale gas well with a three-dimensional discrete fracture. To achieve this, the hydraulic fracture is discretized into several panels, and the transient flow equation is numerically solved using the finite difference method. Based on the Langmuir adsorption isotherm and the pseudo-steady diffusion in matrix and Darcy flow in the network of micro-fractures, a reservoir model is established, and the Laplace transformation is adopted to solve the model analytically. The transient responses are obtained by dynamically coupling the flow in the reservoir and the discrete fracture. The precision of the proposed model is validated using the commercial numerical simulator, Eclipse. A series of transient pressure dynamic curves are drawn to make a precise observation of different flow regimes, and the effects of several parameters on transient pressure response are also examined. The results show that the shale gas well testing interpretation curves comprise nine flow stages. The pressure drop of shale gas reservoirs is lower than that of conventional gas reservoirs due to the replenishment of desorbed gas. The artificial fracture flow capacity, fracture length, and height are the main engineering factors affecting the pressure responses of shale gas wells. Maximizing the degree and scope of reconstruction can enhance the gas well production capacity during fracturing construction. The research results also indicate that our model is a reliable semi-analytical model for well test interpretations in real case studies.

Surrogate models of heat transfer in fractured rock and their use in parameter estimation

Fracture distribution plays a significant role in the behavior of subsurface environments, affecting such activities as geothermal production, exploitation and management of groundwater resources, and long-term storage of nuclear waste and carbon dioxide. A key challenge in these and other applications is to estimate the fracture network properties from sparse and noisy observations. We evaluate the utility of cross-borehole thermal experiments for this task, using both physics-based particle-tracking (PBPT) heat-transfer approach and its deep neural network (DNN) surrogates. Synthetic data are provided by the PBPT simulations and used to train and test the DNN surrogates over a full range of the fracture network properties. We propose regionalized and step-by-step training techniques to reduce the computational cost of expensive PBPT forward solves over large ranges of the (to-be-estimated) parameters. Our numerical experiments suggest the feasibility of training a regionalized DNN surrogate over parameter ranges for which the PBPT solves are fast and extrapolating its predictions to parameter ranges with few additional data. We analyze the balance between computational cost and model accuracy, and provide both PBPT and DNN models for applications to others kinds of data.

Relationship between box-counting fractal dimension and properties of fracture networks

Due to the capacity to quantify the complexity of a fracture network, fractal dimension (D) is widely applied in analyzing fracture network-related issues, such as connectivity and permeability. While the relationship between D and individual properties of a fracture network has been extensively studied, D is influenced by a combination of multiple attributes of the fracture network. Therefore, this work utilizes multivariate analysis to establish an equation for predicting D, taking into account various properties of the fracture network, namely fracture length, number, and orientation. Monte Carlo simulation is employed to generate a substantial number of fracture network models. Subsequently, relationships between the fractal dimension (D) and various properties are derived for three types of fracture networks with (1) invariant fracture length and random orientation, (2) exponential fracture length and random orientation, and (3) exponential fracture length and von-Mises orientation. The initial analysis focuses on the simplest relationship, wherein the fundamental formula of fractional expression is determined. Then the second and third relationships are obtained through replacing the fixed parameter in the first relationship with the distribution parameters of fracture properties. Correlation analyses between the predicted D and the actual values reveal a remarkably high correlation (>0.99). To validate the established relationships, a fracture network obtained from geological outcrops is utilized. The results demonstrate the validity of the derived relationships. The utilization of these equations enhances the efficiency, practicality, and convenience of estimating fractal dimensions from fracture properties. As a result, the analysis of fracture network-related issues becomes more feasible and accessible.

Characterizing fractured reservoirs by integrating outcrop analogue studies with flow simulations

This research focuses on how ‘static’ properties of fracture networks can be studied by considering ‘dynamic’ flow simulation, while static properties such as clustering, connectivity, variation in aperture and, of course, anisotropy of fracture networks can be quantified using different geostatistical/data analysis techniques. The flow responses through such networks can be simulated to check if flow simulation can be used as a tool for evaluating its geometry. In order to achieve this, outcrop analogues of fractured reservoirs are converted into permeability structured grids implementing the fracture continuum (FC) concept. These FC models are flow simulated in a streamline simulator, TRACE3D. Results of the first experiment show that rather than the ‘fractal dimension’, the ‘lacunarity parameter’, which quantifies scale-dependent clustering of fractures, is a unique identifier of network geometry and acts as a proxy for fracture connectivity and an indicator of flow behaviour. The FC model further accommodates variability in fracture apertures and, thus, in a second experiment a set of models with a hierarchical aperture distribution was built and tested for their time-of-flight (TOF) and recovery curves, which showed that smaller fractures with narrow apertures do not significantly contribute to flow. In a third experiment considering anisotropy, it was observed that tightly clustered fractures along preferential directions can be identified from anisotropy in flow patterns. The results from these three experiments show that flow patterns in fracture networks can indicate the overall scale-dependent clustering, the anisotropy that arises from such clustering and that narrower fractures do not significantly alter the overall flow behaviour. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

The use of stochastic geomechanical properties of potential failure plane and fracture networks for realistic modelling of rock mass behaviour: A synthetic rock mass modelling (SRM) study

The use of stochastic geomechanical properties of potential failure plane and fracture networks for realistic modelling of rock mass behaviour: A synthetic rock mass modelling (SRM) study

A fractal model for estimating the permeability of tortuous fracture networks with correlated fracture length and aperture

Many fractures are present in the crust and dominate fluid flow and mass transport. This study proposes a fractal model of permeability for fractured rock masses that includes fractal properties of both fracture networks and fracture surface tortuosity. Using this model, a mathematical expression is derived based on the traditional parallel-plate cubic law and fractal theory. This expression functions as the equivalent permeability of the tortuous fracture network in terms of the maximum fracture length lmax, the fractal dimension of the length distribution Df, porosity ϕ, fracture orientation θ, and the proportionality coefficient between fracture length and aperture β. The fractal scaling law of the fracture length distribution and fractal permeability model is verified by comparison with published studies and fluid dynamic computation, respectively. The results indicate that the deviation of permeability values predicted by the models that do or do not consider the fracture surface tortuosity are as large as three orders of magnitude, which emphasizes that the role of tortuosity should be considered to avoid the overestimation of permeability due to the smooth fracture surface assumption. Further analyses show that the permeability increases with increasing fractal dimension Df, proportionality coefficient β, maximum fracture length lmax, and effective porosity ϕ but decreases with increasing tortuosity dimension Dtf and orientation θ. The fractal dimension of the fracture length distribution Df has the most significant influence on the permeability of the fracture network, followed by Dtf, β, lmax, θ, and ϕ, sequentially.

Effects of discontinuities on the rock block geometry of dimension stone quarries: a case study

ABSTRACT The geometry of rock blocks, including shape and size, is a crucial factor affecting the quarry’s profitability. The block’s dimension is limited by the fracture network properties, especially discontinuity’s orientation, and spacing. Volumetric joint count (Jv), and weighted joint density (WJd) are two well-known indexes to determine the average block’s volume. In this investigation the discontinuity properties of eight quarries are determined through the line and aerial mapping surveys. The investigation was performed along 2258 m scanline in 63 slope benches. Different indexes were used to compare the obtainable rock block’s geometry. An empirical index was proposed considering different aspects of discontinuity network to increase the accuracy of measurements. The index was introduced based on the number of joints in each joint set, average spacing of joints in each joint set, and the total number of joints recorded during the investigation. The joint mean spacing index allows us to estimate the average volume of blocks and evaluate the quarry’s profitability. By increasing the ratio of the joint mean spacing index, the obtainable rock block’s volume and mine profitability increase.

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many different p′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-related p′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560 p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in the p′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the p′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection of p′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence the p′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour.

Applying Reservoir Simulation and Artificial Intelligence Algorithms to Optimize Fracture Characterization and CO2 Enhanced Oil Recovery in Unconventional Reservoirs: A Case Study in the Wolfcamp Formation

Reservoir simulation for unconventional reservoirs requires proper history matching (HM) to quantify the uncertainties of fracture properties and proper modeling methods to address complex fracture geometry. An integrated method, namely embedded discrete fracture model–artificial intelligence–automatic HM (EDFM–AI–AHM), was used to automatically generate HM solutions for a multistage hydraulic fracturing well in the Wolfcamp Formation. Thirteen scenarios with different combinations of matrix and fracture parameters as variables or fixed inputs were designed to generate 1300 reservoir simulations via EDFM–AI–AHM, from which 358 HM solutions were retained to reproduce production history and quantify the uncertainties of matrix and hydraulic fracture properties. The best HM solution was used for production forecasting and carbon dioxide (CO2)-enhanced oil recovery (EOR) strategy optimization. The results of the production forecast for primary recovery indicated that the drainage area for oil production was difficult to extend further into the low-permeability reservoir matrix. However, CO2 EOR simulations showed that increasing the gas injection rate during the injection cycle promoted incremental oil production from the reservoir matrix, regardless of minimum miscibility pressure. A gas injection rate of 25 million standard cubic feet per day (MMscfd) resulted in a 14% incremental oil production improvement compared to the baseline scenario with no EOR. This paper demonstrates the utility of coupling reservoir simulation with artificial intelligence algorithms to generate ensembles of simulation cases that provide insights into the relationships between fracture network properties and production.

Deriving Three‐Dimensional Properties of Fracture Networks From Two‐Dimensional Observations in Rocks Approaching Failure Under Triaxial Compression: Implications for Fluid Flow

AbstractApproximating the three‐dimensional structure of a fault network at depth in the subsurface is key for robust estimates of fluid flow. However, only observations of two‐dimensional outcrops are often available. To shed light on the relationship between two‐ and three‐dimensional measurements of fracture networks, we examine data from a unique set of 11 X‐ray synchrotron triaxial compression experiments that reveal the evolving three‐dimensional fracture network throughout loading. Using machine learning, we derive relationships between the two‐ and three‐dimensional measurements of three properties that control fluid flow: the porosity, and volume and tortuosity of the largest fracture at a particular differential stress step. The models predict the porosity and volume of the largest fracture with R2 scores of &gt;0.99, but predict the tortuosity with maximum R2 scores of 0.68. To test the assumption that different rock types may require different equations between the two‐ and three‐dimensional properties, we develop models for both individual rock types (granite, monzonite, marble, sandstone) and all of the experiments. Models developed using all of the experiments perform better than models developed for individual rock types, suggesting fundamental similarities between fracture networks in rocks often analyzed separately. Models developed with several parallel two‐dimensional observations perform similarly to models developed with several perpendicular two‐dimensional observations. When the models are developed with statistics of the two‐dimensional observations, the models primarily depend on the mean and median when they predict the porosity, and minimum when they predict the volume and tortuosity.

The evolving representative elementary volume size in crystalline and granular rocks under triaxial compression approaching macroscopic failure

SUMMARY Determining the size of the representative elementary volume (REV) for properties of fracture networks, such as porosity and permeability, is critical to robust upscaling of properties measured in the laboratory to crustal systems. Although fractured and damaged rock may have higher porosity and permeability than more intact rock, and thus exert a dominant influence on fluid flow, mechanical stability and seismic properties, many of the analyses that have constrained the REV size in geological materials have used intact rock. The REV size is expected to evolve as fracture networks propagate and coalesce, particularly when fracture development becomes correlated and the growth of one fracture influences the growth of another fracture. As fractures propagate and open with increasing differential stress, the REV size may increase to accommodate the larger fractures. The REV size may also increase as a consequence of the increasing heterogeneity of the fracture network, as many smaller fractures coalesce into fewer and longer fractures, and some smaller fractures stop growing. To quantify the evolving heterogeneity of fracture networks, we track the REV size of the porosity throughout eleven triaxial compression experiments under confining stresses of 5–35 MPa. Acquiring X-ray tomography scans after each increase of differential stress provides the evolving 3-D fracture network in four rock types: Carrara marble, Westerly granite, quartz monzonite and Fontainebleau sandstone. In contrast to expectations, the REV size does not systematically increase toward macroscopic failure in all of the experiments. Only one experiment on sandstone experiences a systematic increase in REV size because this rock contains significant porosity preceding loading, and it subsequently develops a localized fracture network that spans the core. The REV size may not systematically increase in most of the experiments because the highly heterogeneous porosity distributions cause the REV to become larger than the core. Consistent with this idea, when the rock does not have a REV, the fractures tend to be longer, thicker, more volumetric, and closer together than when the rock hosts a REV. Our estimates of the REV for the porosity of the sandstone are similar to previous work: about two to four times the mean grain diameter, or 0.5–1 mm. This agreement with previous work and the &amp;lt;15 per cent change in the REV size in two of the sandstone experiments suggests that when a system composed of sandstone does not host a localized, system-spanning fracture network, estimates of the REV derived from intact sandstone may be similar to estimates derived from damaged sandstone. Using the existing REV estimates derived from intact sandstone to simulations with more damaged crust, such as the damage zone adjacent to large crustal faults, will allow numerical models to robustly simulate increasingly complex crustal systems.

Understanding hydraulic fracture mechanisms: From the laboratory to numerical modelling

The development of fracture networks associated with hydraulic fracturing operations are extremely complex multiphysics processes and there is still no accepted methodology for mapping or realistic recreating such fracture networks. This is an issue especially for modeling purposes, as, ideally, an accurate numerical representation, and subsequent numerical model, should be able to honor the trajectory, type, connectivity, and geometric properties of the complex fracture network generated. This research proposes a novel framework capable of conducting fluid flow numerical simulations based on mapped fracture networks induced during hydraulic fracturing laboratory experiments where a shale sample, under true triaxial reservoir stress conditions, is subjected to fluid injection to mimic a single stage open-hole in-situ hydraulic fracture operation. The resulting post-test fracture network of the shale sample is filled with fluorescent dyed epoxy and subsequently imaged. The images are segmented, and individual fractures are classified based on their geometrical characteristics, as parted bedding planes, opened natural fractures, and newly generated hydraulic fractures. The digital fracture network is numerically represented for fluid flow simulation using a dual-porosity model within the finite volume method. In the numerical reconstruction, fractures are implicitly represented in a set of cells with virtual fracture aperture. The properties of each grid cell are assigned based on fracture classification, and flow between grid cells is explicitly assigned based on the connectivity of the grid cells. Findings show faster fluid drainage parallel to bedding planes (horizontal) than in the vertical direction, indicating strong fluid flow anisotropy. Cited as: Abdelaziz, A., Ha, J., Li, M., Magsipoc, E., Sun, L., Grasselli, G. Understanding hydraulic fracture mechanisms: From the laboratory to numerical modelling. Advances in Geo-Energy Research, 2023, 7(1): 66-68. https://doi.org/10.46690/ager.2023.01.07

Improving heat extraction performance of enhanced geothermal systems: Insights from critical fracture network parameter and multi-objective optimization method

• An integrated workflow is proposed for improving thermal recovery of hot dry rocks. • Network connectivity is a key fracture parameter in dominating thermal efficiency. • Network connectivity governs thermo-hydro-mechanical processes of fractured rocks. • Fractures with connectivity around 15–35 are better for production optimization. The generation of a highly permeable fracture network in Enhanced Geothermal Systems (EGSs) is a prerequisite to develop such geothermal reservoirs efficiently. However, it lacks a quantitative geometrical metric to evaluate what kind of fracture network is preferable for geothermal production. To address this problem, we use our previously developed high-fidelity Thermo-Hydro-Mechanical (THM) coupling model to investigate the joint influence of fracture geometry and thermal stress on the THM behaviors of EGSs. Results show that the geometrical connectivity of fracture networks plays a dominant role in determining the THM processes and the thermal performance of EGSs, in which the network connectivity integrates multiple properties of fracture networks including the fracture length, intensity, location and orientation. Satisfactory thermal performance tends to be achieved when the connectivity of fracture systems ranges from 15 to 35. We focus on such fracture networks and design an appropriate production scenario for optimizing multiple objectives (i.e. maximum EGS service life, heat extraction rate, and global thermal power). The overall heat extraction performance of EGSs under the optimum scenario gets improved by 77.7% compared with the base case. This research provides an effective workflow and feasible methods for evaluating and optimizing energy exploitation efficiency of EGSs.

Artificial neural network prediction models for Montney shale gas production profile based on reservoir and fracture network parameters

The prediction of shale gas production is necessary to evaluate the project's economical feasibility. Some studies suggested prediction models for predicting shale gas production. However, the model-based planar fracture assumption may not apply to a naturally fractured shale gas reservoir which induces a complex fracture network. This paper proposes three ANN architectures for predicting the peak production and Arps's hyperbolic decline parameters (Di and b) of a shale gas well in the Montney formation with an existing natural fracture system. A production profile can be reconstructed using the Arps' hyperbolic decline model and the predicted parameters. The ANN architectures were developed based on 370 simulation data of the reservoir, hydraulic fracture design parameters, and the fracture network properties, including fracture spacing and fracture conductivity, which remarkably affect shale gas production. The testing results, using another set of 92 simulation data, confirmed the high correlation between the input and objective functions with R2 > 0.86. Moreover, good agreement was observed between the measured and predicted cumulative gas production at one-, five-, ten-, fifteen-, and twenty-years of production with R2 > 0.94, and percentage errors were lower than 15.6%. This suggests that the shale gas production can be predicted efficiently and reliably using the Arps' hyperbolic model and the predicted parameters. The estimated production profiles can be used to continuously update the field development plans and calculate the project's NPV. Furthermore, the proposed method is applicable for predicting the production of newly produced reservoirs with limited production history.