Search

The phenomenon of flow-channeling, or the existence of preferential pathways for flow in fracture networks, is well known. Identification of the channels ("backbone") allows for system reduction and computational efficiency in simulation of flow and transport through fracture networks. However, the purpose of machine learning techniques for backbone identification in fractured media is two-pronged system reduction for computational efficiency in simulation of flow and transport as well as physical insight into the phenomenon of flow channeling. The most critical aspect of this problem is the need to have a truly "physics-informed" technique that respects the constraint of connectivity. We present a method that views a network as a union of connected paths with each path comprising a sequence of fractures. Thus, the fundamental unit of selection becomes a sequence of fractures, classified based on attributes that characterize the sequence. In summary, this method represents a parametrization of the sample space that ensures every selected sample sub-network (which is the union of all selected sequences of fractures) always respects the constraint of connectivity, demonstrating that it is a truly physics-informed method. The algorithm has a user-defined parameter which allows control of the backbone size when using the random forest or logistic regression classifier. Even when the backbones are less than 30% in size (leading to computational savings), the backbones still capture the behavior of the breakthrough curve of the full network. Moreover, there is no need to check for path connectedness in the backbones unlike previous methods since the backbones are guaranteed to be connected. [ABSTRACT FROM AUTHOR]

We present a graph-based methodology to reduce the computational cost of obtaining first passage times through sparse fracture networks. We derive graph representations of generic three-dimensional discrete fracture networks (DFNs) using the DFN topology and flow boundary conditions. Subgraphs corresponding to the union of the k shortest paths between the inflow and outflow boundaries are identified and transport on their equivalent subnetworks is compared to transport through the full network. The number of paths included in the subgraphs is based on the scaling behavior of the number of edges in the graph with the number of shortest paths. First passage times through the subnetworks are in good agreement with those obtained in the full network, both for individual realizations and in distribution. Accurate estimates of first passage times are obtained with an order of magnitude reduction of CPU time and mesh size using the proposed method. [ABSTRACT FROM AUTHOR]

Discrete fracture networks (DFN) are often used to model flow and transport in fractured porous media. The accurate resolution of flow and transport behavior on a large DFN involving thousands of fractures is computationally expensive. This makes uncertainty quantification studies of quantities of interest such as travel time through the network computationally intractable, since hundreds to thousands of runs of the DFN model are required to get bounds on the uncertainty of the predictions. Prior works on the subject demonstrated that the complexity of a DFN could be reduced by considering a sub-network of it (often termed a "backbone" sub-network), one whose flow and transport properties were then shown to be similar to that of the full network. The technique is tantamount to partitioning the complete set of fractures of a network into two disjoint sets, one of which is the backbone sub-network while the other its complement. It is in this context that we present a system-reduction technique for DFNs using supervised machine learning via a Random Forest Classifier that selects a backbone sub-network from the full set of fractures. The in-sample errors (in terms of precision and recall scores) of the trained classifier are found to be very accurate indicators of the out-of-sample errors, thus exhibiting that the classifier generalizes well to test data. Moreover, this system-reduction technique yields sub-networks as small as 12% of the full DFN that still recover transport characteristics of the full network such as the peak dosage and tailing behavior for late times. Most importantly, the sub-networks remain connected, and their size can be controlled by a single dimensionless parameter. Furthermore, measures of KL-divergence and KS-statistic for the breakthrough curves of the sub-networks with respect to the full network show physically realistic trends in that the measures decrease monotonically as the size of the sub-networks increase. The computational efficiency gained by this technique depends on the size of the sub-network, but large reductions in computational time can be expected for small sub-networks, yielding as much as 90% computational savings for sub-networks that are as small as 10-12% of the full network. [ABSTRACT FROM AUTHOR]

Shale gas and hydraulic refracturing has revolutionized the US energy sector in terms of prices, consumption, and CO 2 emissions. However, key questions remain including environmental concerns and extraction efficiencies that are leveling off. For the first time, we identify key discoveries, lessons learned, and recommendations from this shale gas revolution through extensive data mining and analysis of 23 years of production from 20,000 wells. Discoveries include identification of a learning-by-doing process where disruptive technology innovation led to a doubling in shale gas extraction, how refracturing with emerging technologies can transform existing wells, and how overall shale gas production is actually dominated by long-term tail production rather than the high-profile initial exponentially-declining production in the first 12 months. We hypothesize that tail production can be manipulated, through better fracturing techniques and alternative working fluids such as CO 2 , to increase shale gas recovery and minimize environmental impacts such as through carbon sequestration. [ABSTRACT FROM AUTHOR]

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed - equivalence, rank, equality of intervals, and equality of ratios - depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied.Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators. [ABSTRACT FROM AUTHOR]

In this paper, we introduce pySimFrac , an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. pySimFrac allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, pySimFrac includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present pySimFrac along with integration examples and discuss the ability to extend pySimFrac from a single complex fracture to complex fracture networks. • A python library to develop synthetic complex fractures. • Fractures can be passed to single- and multiphase simulations for analysis. • Examples of fracture generation and analysis is provided in Jupyter notebooks. • The code-base and user-documentation are made available on GitHub and dfnWorks websites, respectively. [ABSTRACT FROM AUTHOR]

We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., the time-frame for which the wells have been producing), and the significant computational expense of high-fidelity modeling. We do this by applying the machine-learning paradigm of transfer learning, where we combine fast, but less accurate reduced-order models with slow, but accurate high-fidelity models. We use the Patzek model (Proc Natl Acad Sci 11:19731–19736, https://doi.org/10.1073/pnas.1313380110, 2013) as the reduced-order model to generate synthetic production data and supplement this data with synthetic production data obtained from high-fidelity discrete fracture network simulations of the site of interest. Our results demonstrate that training with low-fidelity models is not sufficient for accurate forecasting, but transfer learning is able to augment the knowledge and perform well once trained with the small set of results from the high-fidelity model. Such a physics-informed machine-learning (PIML) workflow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. [ABSTRACT FROM AUTHOR]

• Novel fabrication methods are combined to make a core-scale micromodel for EOR. • Long micromodels capture core-scale physics such as the formation of oil banks. • Increasing viscosity of displacing phase in long micromodels increases the amount of oil recovered at breakthrough. Fluid injection experiments in rocks, commonly referred to as corefloods, are widely used to study and understand fluid flow in the subsurface. However, visual inspection of flow in cores requires computed tomography machines which may not be widely accessible. We introduce a novel micromodel that is as long as a typical core (40 cm), has adjustable pore structure, and includes 2.5D pore throats that can be used to conduct fluid displacements analogous to those in cores. Flow can be visualized inexpensively in the micromodel with an optical microscope. We performed standard coreflood tests in our micromodel including a tracer test and a steady state permeability test. We also conducted multiphase displacements by injecting aqueous solutions at varying glycerol concentrations to displace oil from the micromodel and observed the effect of the viscosity ratio on macro-scale recovery efficiency. When the injected aqueous solution was less viscous than the resident oil, it fingered through the oil. Fingering was not observed in the cases where the injected glycerol solution was more viscous than the oil. Moreover, as the viscosity of the injected glycerol solution increased, oil was recovered more rapidly. Additionally, we performed surfactant and glycerol floods in short (2.4 cm) and long (40 cm) micromodels that show long chips capture scale dependent physics, such as oil banking, that small chips do not capture. The novel micromodel shows promise as a screening tool for chemical EOR because it captures phase banks that are desirable in corefloods. [ABSTRACT FROM AUTHOR]

We develop a computational geometry-based upscaling approach to accurately capture the dynamic processes occurring within and between subsurface fracture networks and the surrounding porous matrix named the Upscaled Discrete Fracture Matrix model (UDFM). Fracture attributes (orientations and apertures) are upscaled and combined with matrix attributes (permeability and porosity) into properties of an unstructured spatially variable Delaunay tetrahedral continuum mesh. The resolution of the mesh depends on proximity to the fractures to preserve the geometric and topological integrity of the underlying fracture network as well as increasing the accuracy of gradients in the dynamics between the fractures and matrix. The UDFM Delaunay mesh and its dual Voronoi mesh can be used in existing multiphysics simulators for flow, solute/heat mass transport, and geomechanics, thereby eliminating the need for the additional development of numerical methods to couple processes in fracture/matrix systems. The model is verified by comparing flow, transport, and coupled heat-mass flow simulations on the provided meshes against analytical and numerical benchmarks. We also provide an additional example to demonstrate the applicability of the UDFM to complex heteregenous fractured media. Overall, the UDFM is accurate in all of the cases considered here and presents an attractive alternative to other modeling strategies that require novel numerical methods or multi-dimensional meshes. [ABSTRACT FROM AUTHOR]

Structural and topological information play a key role in modeling flow and transport through fractured rock in the subsurface. Discrete fracture network (DFN) computational suites such as DFNWORKS (Hyman et al. Comput. Geosci. 84, 10-19 2015) are designed to simulate flow and transport in such porous media. Flow and transport calculations reveal that a small backbone of fractures exists, where most flow and transport occurs. Restricting the flowing fracture network to this backbone provides a significant reduction in the network’s effective size. However, the particle-tracking simulations needed to determine this reduction are computationally intensive. Such methods may be impractical for large systems or for robust uncertainty quantification of fracture networks, where thousands of forward simulations are needed to bound system behavior. In this paper, we develop an alternative network reduction approach to characterizing transport in DFNs, by combining graph theoretical and machine learning methods. We consider a graph representation where nodes signify fractures and edges denote their intersections. Using random forest and support vector machines, we rapidly identify a subnetwork that captures the flow patterns of the full DFN, based primarily on node centrality features in the graph. Our supervised learning techniques train on particle-tracking backbone paths found by DFNWORKS, but run in negligible time compared to those simulations. We find that our predictions can reduce the network to approximately 20% of its original size, while still generating breakthrough curves consistent with those of the original network. [ABSTRACT FROM AUTHOR]

One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. We compare DFN and ECM in terms of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km 3 ) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. We identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions. [ABSTRACT FROM AUTHOR]

We present the near-Maximal Algorithm for Poisson-disk Sampling (nMAPS) to generate point distributions for variable resolution Delaunay triangular and tetrahedral meshes in two and three-dimensions, respectively. nMAPS consists of two principal stages. In the first stage, an initial point distribution is produced using a cell-based rejection algorithm. In the second stage, holes in the sample are detected using an efficient background grid and filled in to obtain a near-maximal covering. Extensive testing shows that nMAPS generates a variable resolution mesh in linear run time with the number of accepted points. We demonstrate nMAPS capabilities by meshing three-dimensional discrete fracture networks (DFN) and the surrounding volume. The discretized boundaries of the fractures, which are represented as planar polygons, are used as the seed of 2D-nMAPS to produce a conforming Delaunay triangulation. The combined mesh of the DFN is used as the seed for 3D-nMAPS, which produces conforming Delaunay tetrahedra surrounding the network. Under a set of conditions that naturally arise in maximal Poisson-disk samples and are satisfied by nMAPS, the two-dimensional Delaunay triangulations are guaranteed to only have well-behaved triangular faces. While nMAPS does not provide triangulation quality bounds in more than two dimensions, we found that low-quality tetrahedra in 3D are infrequent, can be readily detected and removed, and a high quality balanced mesh is produced. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing of shale formations in the United States has led to a domestic energy boom. Currently, water is the only fracturing fluid regularly used in commercial shale oil and gas production. Industry and researchers are interested in non-aqueous working fluids due to their potential to increase production, reduce water requirements, and to minimize environmental impacts. Using a combination of new experimental and modeling data at multiple scales, we analyze the benefits and drawbacks of using CO 2 as a working fluid for shale gas production. We theorize and outline potential advantages of CO 2 including enhanced fracturing and fracture propagation, reduction of flow-blocking mechanisms, increased desorption of methane adsorbed in organic-rich parts of the shale, and a reduction or elimination of the deep re-injection of flow-back water that has been linked to induced seismicity and other environmental concerns. We also examine likely disadvantages including costs and safety issues associated with handling large volumes of supercritical CO 2 . The advantages could have a significant impact over time leading to substantially increased gas production. In addition, if CO 2 proves to be an effective fracturing fluid, then shale gas formations could become a major utilization option for carbon sequestration. [ABSTRACT FROM AUTHOR]

Gas slippage occurs when the mean free path of the gas molecules is in the order of the characteristic pore size of a porous medium. This phenomenon leads to Klinkenberg's effect where the measured permeability of a gas (apparent permeability) is higher than that of the liquid (intrinsic permeability). A generalized lattice Boltzmann model is proposed for flow through porous media that includes Klinkenberg's effect, which is based on the model of Guo et al. [Phys. Rev. E 65, 046308 (2002)]. The second-order Beskok and Karniadakis-Civan's correlation [A. Beskok and G. Karniadakis, Microscale Thermophys. Eng. 3, 43 (1999) and F. Civan, Transp. Porous Med. 82, 375 (2010)] is adopted to calculate the apparent permeability based on intrinsic permeability and the Knudsen number. Fluid flow between two parallel plates filled with porous media is simulated to validate the model. Simulations performed in a heterogeneous porous medium with components of different porosity and permeability indicate that Klinkenberg's effect plays a significant role on fluid flow in low-permeability porous media, and it is more pronounced as the Knudsen number increases. Fluid flow in a shale matrix with and without fractures is also studied, and it is found that the fractures greatly enhance the fluid flow and Klinkenberg's effect leads to higher global permeability of the shale matrix. [ABSTRACT FROM AUTHOR]

A quantitative assessment of how faults control the migration of geofluids is critical in many areas of geosciences. We integrated geological fieldwork, quantitative analysis of the fractures distribution and numerical modeling to build a geometrical representation of a fault zone and to characterize its hydraulic properties. Our target is a fault located in the Majella Mountain (Italy). We collected 21 scan lines across the fault profile in order to characterize its architecture. The numerical modeling of the fracture network of the damage zones and their hydraulic parameters was performed using both commercial (Move®) and open source software (dfnWorks and PFLOTRAN). Move® was used to build a representative model of the fault zone using fracture spacing as a proxy, and to model the hydraulic parameters of the different fault domains. dfnWorks and PFLOTRAN were employed to infer the hydraulic parameters of the damage zones of the fault and then upscale these properties to an equivalent continuum domain, suitable for fluid flow simulations through the whole fault zone. Our findings show how even in a relatively small area it is possible to describe changes in terms of hydraulic properties of a fault zone and to build models capable to represent these variations. • We integrate geological fieldwork, quantitative analysis of fractures and numerical modeling to infer the hydraulic properties of a fault zone. • Our results stress the value of detailed field investigations in the hydraulic characterization of fault zones. • The use of open source software like dfnWorks and PFLOTRAN allows for a wide control of the parameters used to build a numerical model of the fault. • The proposed workflow has potential applications in several fields, especially when is crucial to assess the impact of faults on fluid migration. [ABSTRACT FROM AUTHOR]

The aim of this study was to apply and evaluate a recently developed prioritization model which uses the synoptic approach to geographically prioritize watersheds in which Best Management Practices (BMPs) can be implemented to reduce water quality problems resulting from erosion and sedimentation. The model uses a benefit-cost framework to rank candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most water quality improvement per conservation dollar invested. The model was developed to prioritize BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS Conservation Effects Assessment Project (CEAP). We applied the model to HUC-8 watersheds within the southeastern Coastal Plain ecoregion (USA) because not only is it an important agricultural area but also because it contains a well-studied medium-sized CEAP watershed which is thought to be representative of the ecoregion. The results showed that the three HUC-8 watersheds with the highest rankings (most water quality improvement expected per conservation dollar invested) were located in the southern Alabama, northern Florida, and eastern Virginia. Within these watersheds, measures of community attitudes toward conservation practices were highly ranked, and these indicators seemed to push the watersheds to the top of the rankings above other similar watersheds. The results, visualized as maps, can be used to screen and reduce the number of watersheds that need further assessment by managers and decision-makers within the study area. We anticipate that this model will allow agencies like USDA-NRCS to geographically prioritize BMP implementation efforts.

Migratory animals provide unique spectacles of cultural, ecological, and economic importance. However, the process of migration is a source of risk for migratory species as human actions increasingly destroy and fragment habitat, create obstacles to migration, and increase mortality along the migration corridor. As a result, many migratory species are declining in numbers. In the United States, the Endangered Species Act provides some protection against extinction for such species, but no protection until numbers are severely reduced, and no guarantee of recovery to population levels associated with cultural, ecological, or economic significance. Although groups of species receive some protection from statutes such as the Migratory Bird Treaty Act and Marine Mammal Protection Act, there is no coordinated system for conservation of migratory species. In addition, information needed to protect migratory species is often lacking, limiting options for land and wildlife managers who seek to support these species. In this Article, we outline the existing scientific, legal, and management information and approaches to migratory species. Our objective is to assess present capacity to protect the species and the phenomenon of migration, and we argue that al three disciplines are necessary for effective conservation. We find significant capacity to support conservation in all three disciplines, but no organization around conservation of migration within any discipline or among the three disciplines. Areas of synergy exist among the disciplines but not as a result of any attempt for coordination. As a result, significant gaps in information and capacity exist that must be addressed if effective conservation of migratory species is to be undertaken. We suggest that all three disciplines cooperate to identify the most pressing research needs, so that these can become targets for relevant funding sources. We identify areas of current risk to migratory species that represent gaps in current legal protections: protective legislation that provides no guidelines for desirable population sizes or best management practices for migratory species, taxonomic groups, particularly those including long-distance migrants, for which no agency has oversight, and gaps in policies to address impacts of fragmentation and obstacles such as power lines and wind turbines that curtail migration or cause mortality. Finally, we suggest that state-level programs provide either a foundation to augment with, or a model on which to build, conservation efforts targeting migratory species. Problems will arise due to lack of funds, difficulties in securing a landscape that will support abundant migrations, lack of adequate standards and best management practices, and an insufficient culture of collaboration among the three main relevant disciplines. However, we view these problems as entirely soluble and see evidence of support in society at large for conservation of migratory species.