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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method to minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.

Di, Sheng↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2013

Analysis of Potential Leakage Pathways and Mineralization within Caprocks for Geologic Storage of CO 2

We used a multifaceted approach to investigate the nature of caprocks above, and the interface between, reservoir-quality rocks that might serve as targets for carbon storage. Fieldwork in southeastern Utah examined the regional- to m-scale nature of faults and fractures across the sedimentiological interfaces. We also used microscopic analyses and mechanical modeling to examine the question as to how the contacts between units interact, and how fractures may allow fluids to move from reservoirs to caprock. Regional-­scale analyses using ASTER data enabled us to identify location of alteration, which led to site-­specific studies of deformation and fluid flow. In the Jurassic Carmel Formation, a seal for the Navajo Sandstone, we evaluated mesoscale variability in fracture density and morphology and variability in elastic moduli in the Jurassic Carmel Formation, a proposed seal to the underlying Navajo Sandstone for CO 2 geosequestration. By combining mechano-stratigraphic outcrop observations with elastic moduli derived from wireline log data, we characterize the variability in fracture pattern and morphology with the observed variability in rock strength within this heterolithic top seal. Outcrop inventories of discontinuities show fracture densities decrease as bed thickness increases and fracture propagation morphology across lithologic interfaces vary with changing interface type. Dynamic elastic moduli, calculated from wireline log data, show that Young’s modulus varies by up to 40 GPa across depositional interfaces, and by an average of 3 GPa across the reservoir/seal interface. We expect that the mesoscale changes in rock strength will affect the distributions of localized stress and thereby influence fracture propagation and fluid flow behavior within the seal. These data provide a means to closely tie outcrop observations to those derived from subsurface data and estimates of subsurface rock strength. We also studied damage zones associated normal faults in the Permian Cedar Mesa Sandstone, southeastern Utah. These faults are characterized by a single slip surfaces and damage zones containing deformation bands, veins, and joints. Field observations include crosscutting relationships, permeability increase, rock strength decrease, and ultraviolet light induced mineral fluorescence within the damage zone. These field observations combined with the interpreted paragenetic sequence from petrographic analysis, suggests a deformation history of reactivation and several mineralization events in an otherwise low-permeability fault. All deformation bands and veins fluoresce under ultraviolet light, suggesting connectivity and a shared mineralization history. Pre-­existing deformation features act as loci for younger deformation and mineralization events, this fault and its damage zone illustrate the importance of the fault damage zone to subsurface fluid flow. We model a simplified stress history in order to understand the importance of rock properties and magnitude of tectonic stress on the deformation features within the damage zone. The moderate confining pressures, possible variations in pore pressure, and the porous, fine-­grained nature of the Cedar Mesa Sandstone results in a fault damage zone characterized by enhanced permeability, subsurface fluid flow, and mineralization. Structural setting greatly influences fracture spacing and orientation. Three structural settings were examined and include fault proximity, a fold limb of constant dip, and a setting proximal to the syncline hinge. Fracture spacing and dominant fracture orientation vary at each setting and distinctions between regional and local paleo-stress directions can be made. Joints on the fold limb strike normal to the fold axis/bedding and are interpreted to be sub-parallel to the maximum regional paleo-stress direction as there is no fold related strain. Joints proximal to faults and the syncline hinge may have formed under local stress conditions associated with folding and faulting, and in some cases, are perpendicular to the maximum regional paleo-stress direction. Macroscale analyses of the Iron Wash fault, which cuts most of the stratigraphy of the reservoir- seal analogs, reveals that seal bypass occurs along faults with mixed lithologic composition. While portions of the fault may be effective seals, when modeled as a whole the fault zone cannot support a high CO 2 column. We adapted oil-field methods to calculate column heights and sealing capacities. Examination of the burial and tectonic histories of the rocks, coupled with field- and micro-­scale studies, allow us to examine the potential for seal failure using basic rock mechanics failure analyses. These studies reveal that the sealing lithologies experienced multiple episodes of deformation that resulted in numerous fractures and small faults. In a stress state based on burial history and overburden loads, we show that most of the seals could fail with normal to slightly excess pore pressures.

58 GEOSCIENCES↗
DOE OSTI2022

LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

AC↗
DOE OSTI2020

Bringing Automated Fault Detection and Diagnostics Tools for HVAC&R Into the Mainstream

Heating, ventilation, and air-conditioning (HVAC) systems consume over 5 quads of energy annually, representing 30% of energy consumption in the U.S. commercial buildings. Additionally, commercial refrigeration (R) systems add about 1 quad to commercial buildings energy consumption. Most HVAC systems operate with one or more faults that result in increased energy consumption. Fault detection and diagnostics (FDD) tools have been developed to address this national issue, and many tools are commercially available. FDD tools have the potential to save considerable energy for an existing commercial rooftop unit (RTU) and refrigeration systems. These devices can be used for both retro commissioning and, when faults are addressed, continuous commissioning. However, there appears to be multiple market barriers for this technology. Although there are efforts to develop FDD tool standards, currently there are no standards and methods to define functions, capabilities, accuracy, and reliability of FDD tools in the field. Moreover, most of the commercial FDD tools have not been verified in the field independently. This paper presents a comprehensive approach for bringing HVAC FDD tools into the mainstream. The approach involves demonstrating ten commercially available FDD tools installed at ten different sites, independent testing and evaluation of the FDD tools, communication with various stakeholders, identifying and assessing market barriers, creating a process evaluation methodology, and assisting utility companies in developing incentive programs. Furthermore, the preliminary baseline results from the case study demonstrate how the use of an independent monitoring system (IMS) can be used for ground-truth in evaluating FDD tools in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Bringing Fault Detection and Diagnostics (FDD) Tools into the Mainstream: Retro Commissioning and Continuous Commissioning of HVAC and Refrigeration Systems

HVAC systems in the commercial building sector consume about 3.4 quads of energy annually. Of all the HVAC systems, packaged rooftop air–conditioning units (RTUs) provide cooling and heating for over 60 percent of the commercial building floorspace (about 90 billion ft2) in the U.S. and they are a significant source of energy consumption and peak demand. All HVAC systems suffer from faults that impact thermal comfort and increase energy consumption. There are several commercially available Automated Fault Detection and Diagnostic (AFDD) tools on the market that can detect and diagnose faults, and if those faults are corrected, can save significant national energy. However, there are multiple market barriers for these tools including lack of independent verification of their performance in the field in terms of their technical capabilities, ease of use and installation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. Furthermore, a case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids

Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vulnerable to cyber-physical attacks and faults due to the complex interconnections. As such, it is necessary To design resilient control systems to support the operations of networked microgrids in responses to cyber-physical attacks and faults. This paper introduces a resilient communication scheme for interconnecting multiple microgrids to support critical demand, in which the interconnection decision can be made distributedly by each microgrid controller even in the presence of cyberattacks to some communication links or microgrid controllers. This scheme blends a randomized peer-to-peer communication network for exchanging information among controllers and resilient consensus algorithms for achieving reliable interconnection agreement. The network of 6 microgrids divided from a modified 123-node test distribution feeder is used to demonstrate the effectiveness of the proposed resilient communication scheme.

Vu, Thanh Long↗
DOE OSTI2015

SE Great Basin Play Fairway Analysis Heat and Permeability CRS

Within this submission are multiple .tif images with accompanying metadata of magnetotelluric conductor occurrence, fault critical stress composite risk segment (CRS), permeability CRS, Quaternary mafic extrusions, Quaternary fault density, and Quaternary rhyolite maps. Each of these contributed to a final play fairway analysis (PFA) for the SE Great Basin study area.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Low-overhead transversal fault tolerance for universal quantum computation

Fast, reliable logical operations are essential for realizing useful quantum computers. By redundantly encoding logical qubits into many physical qubits and using syndrome measurements to detect and correct errors, we can achieve low logical error rates. However, for many practical quantum error correction codes such as the surface code, owing to syndrome measurement errors, standard constructions require multiple extraction rounds—of the order of the code distance d—for fault-tolerant computation, particularly considering fault-tolerant state preparation. Here we show that logical operations can be performed fault-tolerantly with only a constant number of extraction rounds for a broad class of quantum error correction codes, including the surface code with magic state inputs and feedforward, to achieve ‘transversal algorithmic fault tolerance’. Through the combination of transversal operations7 and new strategies for correlated decoding, despite only having access to partial syndrome information, we prove that the deviation from the ideal logical measurement distribution can be made exponentially small in the distance, even if the instantaneous quantum state cannot be made close to a logical codeword because of measurement errors. We supplement this proof with circuit-level simulations in a range of relevant settings, demonstrating the fault tolerance and competitive performance of our approach. Furthermore, our work sheds new light on the theory of quantum fault tolerance and has the potential to reduce the space–time cost of practical fault-tolerant quantum computation by over an order of magnitude.

Zhou, Hengyun [QuEra Computing, Boston, MA (United↗
DOE OSTI2023

Reliability Improvement by Fault-Tolerant Operation of NPC Inverter for Motor Driving

The high-reliability operation of three-level inverters is crucial to prevent equipment damage, process downtime, and economic losses. This article investigates a three-level neutral-point-clamped inverter under all possible combinations of open-circuit and short-circuit faults and proposes a new postfault operation method without adding extra hardware. This method provides comprehensive solution for operations after single and multiple-device failures, increasing the inverter reliability by 24%. This article classified postfault modulations and uncovered previously unknown fault scenarios that can be addressed using the proposed control method. A new postfault modulation based on space vector modulation with virtual vectors is proposed. The feasibility of the proposed control method is verified by simulation and an experiment for one fault scenario in a three-level neutral-point-clamped inverter with a 3.73 kW motor load. Furthermore, this article contributes to improving the reliability of three-level inverters.

42 ENGINEERING↗
DOE OSTI2019

AN APPROACH TO BRINGING AUTOMATED FAULT DETECTION AND DIAGNOSIS (AFDD) TOOLS FOR HVAC&R INTO THE MAINSTREAM

Heating, ventilation and air-conditioning (HVAC) systems can consume over 5 quads of energy annually, representing 30% of energy consumption in the U.S. in commercial buildings. Additionally, commercial refrigeration (R) systems add about 2 quads to commercial buildings energy consumption. Most HVAC systems have one or more faults (low/high refrigerant charge, valve leakage, condenser/evaporator fouling, filter/dryer restriction, economizer faults, controls faults) that result in increased energy consumption. Automated fault detection and diagnosis (AFDD) tools have been developed to address this national issue and many tools are commercially available. AFDD tools have the potential to save considerable energy for existing commercial RTUs, chillers and refrigeration systems. These devices can be used for both retro commissioning, and, when faults are addressed, continuous commissioning as well. However, there appears to be multiple market barriers for this technology. A key market barrier for this technology is the lack of awareness of AFDD products among potential customers. Most HVAC contractors are not familiar with the latest AFDD technologies and HVAC technicians lack skills regarding these technologies. Quantifying potential benefits to building owners is difficult since there are several FDD tools with varying capabilities. For instance, there are several FDD products ranging from handling just economizer faults to those that also handle full-blown refrigerant-side and air-side faults. Methods/algorithms used in FDD vary significantly. Even though there are efforts to develop standards, currently there are no standards/methods to define functions, capabilities, accuracy, and reliability of FDD tools. Moreover, most of the commercial AFDD tools have not been verified in the field independently. This paper presents a comprehensive approach to bringing HVAC AFDD tools into the mainstream. The approach involves demonstrating ten commercially available tools at ten different sites, independent testing and evaluation of the FDD tools, communication with various stakeholders, identifying market barriers, and assisting utility companies in developing incentive programs. This paper presents selection of AFDD tools, ite identification, and field testing and evaluation method.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The Convolutional Multiple Whole Profile (CMWP) Fitting Method, a Global Optimization Procedure for Microstructure Determination

The analysis of line broadening in X-ray and neutron diffraction patterns using profile functions constructed on the basis of well-established physical principles and TEM observations of lattice defects has proven to be a powerful tool for characterizing microstructures in crystalline materials. These principles are applied in the convolutional multiple-whole-profile (CMWP) procedure to determine dislocation densities, crystallite size, stacking fault and twin boundary densities, and intergranular strains. The different lattice defect contributions to line broadening are separated by considering the hkl dependence of strain anisotropy, planar defect broadening and peak shifts, and the defect dependent profile shapes. The Levenberg–Marquardt (LM) peak fitting procedure can be used successfully to determine crystal defect types and densities as long as the diffraction patterns are relatively simple. However, in more complicated cases like hexagonal materials or multiple-phase patterns, using the LM procedure alone may cause uncertainties. Here, we extended the CMWP procedure by including a Monte Carlo statistical method where the LM and a Monte Carlo algorithm were combined in an alternating manner. The updated CMWP procedure eliminated uncertainties and provided global optimized parameters of the microstructure in good correlation with electron microscopy methods.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An Open, Cloud-Based Platform for Whole-Building Fault Detection and Diagnostics

Small commercial buildings in the U.S. waste an estimated 300 Trillion BTU (approximately $6 billion in energy costs) annually due to faults, but lack cost-effective automated fault detection and diagnosis (AFDD) tools. NREL and GE Global Research are partnering to develop hybrid AFDD algorithms tailored to the unique needs of small commercial buildings (</=10,000 sq. ft.). The approach combines physics-based energy modeling tools with state-of-the-art machine learning algorithms to enable diagnostics using modeled input data. NREL, in partnership with Purdue University, has adapted DOE's physics-based modeling software EnergyPlus and OpenStudio to simulate common building faults. Cloud-based implementations of these physics-based models were used to generate data for multiple climate zones. The data were then used to train machine learning algorithms, such as those available in GE's Predix platform, to detect and diagnose building faults. Key features of the system including the automated, cloud-implementation of EnergyPlus, genetic algorithms for automated model feature selection, multiclass detection and diagnosis models, and anomaly detection algorithms will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Numerical Modeling Describing the Effects of Heterogeneous Distributions of Asperities on the Quasi-static Evolution of Frictional Slip

A better understanding of how slip accumulates along faults and its relation to the breakdown of shear stress is beneficial to many engineering disciplines, such as, hydraulic fracture and understanding induced seismicity (among others). Asperities forming along a preexisting fault resist the relative motion of the two sides of the interface and occur due to the interaction of the surface topographies. We employ a finite element model to simulate circular partial slip asperities along a nominally flat frictional interface. Shear behavior of our partial slip asperity model closely matched the theory described by Cattaneo. The asperity model was employed to simulate a small section of an experimental fault formed between two bodies of polymethyl methacrylate, which consisted of multiple asperities whose location and sizes were directly measured using a pressure sensitive film. The quasi-static shear behavior of the interface was modeled for cyclical loading conditions, and the frictional dissipation (hysteresis) was normal stress dependent. We further our understanding by synthetically modeling lognormal size distributions of asperities that were randomly distributed in space. Synthetic distributions conserved the real contact area and aspects of the size distributions from the experimental case, allowing us to compare the constitutive behaviors based solely on spacing effects. Traction-slip behavior of the experimental interface appears to be considerably affected by spatial clustering of asperities that was not present in the randomly spaced, synthetic asperity distributions. Finally, estimates of bulk interfacial shear stiffness were determined from the constitutive traction-slip behavior and were comparable to the theoretical estimates of multi-contact interfaces with non-interacting asperities.

58 GEOSCIENCES↗
DOE OSTI2024

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

58 GEOSCIENCES↗
DOE OSTI2017

Study of hydraulic fracturing processes in shale formations with complex geological settings

Hydraulic fracturing has been applied to extract gas from shale-gas reservoirs. Complicated geological settings, such as spatial variability of the rock mass properties, local heterogeneities, complex in situ stress field, and pre-existing bedding planes and faults, could make hydraulic fracturing a challenging task. In order to effectively and economically recover gas from such reservoirs, it is crucial to explore how hydraulic fracturing performs in such complex geological settings. For this purpose, numerical modelling plays an important role because such conditions cannot be reproduced by laboratory experiments. This study focuses on the analysis of the influence of confining formations and pre-existing bedding planes and faults on the hydraulically-induced propagation of a vertical fracture, which will be called injection fracture, in a shale-gas reservoir. An elastic-brittle model based on material property degradation was implemented in a 2D finite-difference scheme and used for rock elements subjected to tension and shear failure. A base case is considered, in which the ratio SR between the magnitudes of the horizontal and vertical stresses, the permeability k c of the confining formations, the elastic modulus E p and initial permeability k p of the bedding plane and the initial fault permeability k F are fixed at reasonable values. In addition, the influence of multiple bedding planes, is investigated. Changes in pore pressure and permeability due to high pressure injection lasting 2 h were analysed. Results show that in our case during the injection period the fracture reaches the confining formations and if the permeability of those layers is significantly larger than that of the shale, the pore pressure at the extended fracture tip decreases and fracture propagation becomes slower. After shut-in, the pore pressure decreases more and the fracture does not propagate any more. For bedding planes oriented perpendicular to the maximum principal stress direction and with the same elastic properties as the shale formation, results were found not to be influenced by their presence. In such a scenario, the impact of multiple bedding planes on fracture propagation is negligible. On the other hand, a bedding plane softer than the surrounding shale formation leads to a fracture propagation asymmetrical vertically with respect to the centre of the injection fracture with a more limited upward fracture propagation. A pre-existing fault leads to a decrease in fracture propagation because of fault reactivation with shear failure. This results in a smaller increase in injection fracture permeability and a slight higher injection pressure than that observed without the fault. Results of a sensitivity analysis show that fracture propagation is influenced by the stress ratio SR, the permeability k c of the confining formations and the initial permeability k p of the bedding plane more than the other major parameters.

58 GEOSCIENCES↗