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DOE OSTI2020

Probing the surface chemistry for reverse water gas shift reaction on Pt(1 1 1) using ambient pressure X-ray photoelectron spectroscopy

Using ambient pressure XPS (APXPS), we explored carbon dioxide (CO 2 ) adsorption and CO 2 hydrogenation on Pt(1 1 1) single crystal surface to observe the activation of CO 2 and the subsequent reaction mechanism. In pure CO 2 , we observed CO adsorbates and adsorbed oxygen on Pt(1 1 1) derived from CO 2 dissociation at room temperature. The introduction of H 2 (at a pressure ratio of 1:1 (H 2 :CO 2 )) increased the production of CO across all temperatures by facilitating the removal of surface oxygen. As a consequence, the surface could expose sites that could then be utilized for producing CO. Under these conditions, the reverse water–gas shift (RWGS) reaction was observed starting at 300 °C. At higher H 2 partial pressure (10:1 (H 2 :CO 2 )), the RWGS reaction initiated at a lower temperature of 200 °C and continued to enhance the conversion of CO 2 with increasing temperatures. Our results revealed that CO 2 was activated on a clean Pt(1 1 1) surface through the dissociation mechanism to form adsorbed CO and O at room temperature and at elevated temperatures. Introducing H 2 facilitated the RWGS as adsorbed oxygen was consumed continuously to form H 2 O, and adsorbed CO desorbed from the surface at elevated temperatures. This work clearly provides direct experimental evidence for the surface chemistry of CO 2 dissociation and demonstrates how hydrogen impacts the RWGS reaction on a platinum surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Encapsulation of hygroscopic liquids via polymer precipitation in non-aqueous emulsions

Here, encapsulation of ionic liquids (ILs) and phase change materials (PCMs) can overcome limitations associated with bulk materials, e.g., slow mass transfer rates, high viscosities, or susceptibility to external environment. Single step soft-templated encapsulation methods commonly use interfacial polymerization for shell formation, with a multifunctional monomer in the continuous phase and another in the discontinuous phase, and thus do not give pristine core material. We posit that polymer precipitation onto emulsion droplets in non-aqueous emulsions could produce a robust shell without contamination of the core, ideal for the encapsulation of water-sensitive or water-miscible materials. Solutions of commodity polymers were added to the continuous phase of non-aqueous Pickering emulsions stabilized by alkylated graphene oxide (GO) nanosheets such that the change in solubility of the polymer led to formation of robust shells and the production of capsules that could be isolated. We demonstrate that a polymer precipitation approach can produce capsules with pristine core of the IL 1-ethyl-3-methylimidazolium hexafluorophosphate [Emim][PF 6 ] or the salt hydrate PCM magnesium nitrate hexahydrate (MNH) and shell of nanosheets and polystyrene, poly(methyl methacrylate), or polyethylene. The capsules are approximately 80 wt% [Emim][PF 6 ] or >90 wt% MNH, and the core can undergo multiple cycles of solidification and melting without leakage or destruction. This novel, single-step methodology provides a distinct advantage to access capsules with pristine core composition and is amenable to different core and shell, paving the way for tailoring capsule composition for desired applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Improved C 1 shape functions for simplex meshes

Using continuously-differentiable (C 1 ) interpolation methods previously developed for computer visualization and other applications, a new class of shape functions is constructed and applied to electrostatic (ES) Particle-In-Cell (PIC) simulations on simplex (triangle or tetrahedral) meshes. The resulting shape functions are non-negative and partition unity,and lead to a reduction of shot noise and aliasing associated with particle interpolation to and from the mesh. For an energy-conserving implementation as chosen, C 1 interpolation of the potential leads to a continuous (C 0 ) electric field, with associated improved fidelity of particle motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Noise and error analysis and optimization in particle-based kinetic plasma simulations

In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. We find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

A family of independent Variable Eddington Factor methods with efficient preconditioned iterative solvers

We present a family of discretizations for the Variable Eddington Factor (VEF) equations that have high-order accuracy on curved meshes and efficient preconditioned iterative solvers. The VEF discretizations are combined with the Discontinuous Galerkin transport discretization from to form effective high-order, linear transport methods. The VEF discretizations are derived by extending the unified analysis of Discontinuous Galerkin methods for elliptic problems presented by Arnold et al. to the VEF equations. This framework is used to define analogs of the interior penalty, second method of Bassi and Rebay, minimal dissipation local Discontinuous Galerkin, and continuous finite element methods. The analysis of subspace correction preconditioners, which use a continuous operator to iteratively precondition the discontinuous discretization, is extended to the case of the non-symmetric VEF system. Numerical results demonstrate that the VEF discretizations have arbitrary-order accuracy on curved meshes, preserve the thick diffusion limit, and are effective on a proxy problem from thermal radiative transfer in both outer transport iterations and inner preconditioned linear solver iterations. We demonstrate that the VEF solution converges to the S N transport solution as the mesh is refined on both problems with smooth and non-smooth behavior in angle. Parallel performance studies show that the interior penalty VEF discretization's linear solve weak scales out to 1024 processors and strong scales well on a single node. Particular attention is paid to the parallel performance of the VEF algorithm when used in combination with a parallel block Jacobi transport sweep.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. Moreover, the construction of our operator admits an efficient implementation which we detail and construct. As an experimental validation of our operator, we consider the task of compressing partial differential equation (PDE) simulation data from fixed meshes. Here, we show that QuadConv can match the performance of standard discrete convolutions on uniform grid data by comparing a QuadConv autoencoder (QCAE) to a standard convolutional autoencoder (CAE). Further, we show that the QCAE can maintain this accuracy even on non-uniform data. In both cases, QuadConv also outperforms alternative unstructured convolution methods such as graph convolution.

Compression↗

DOE OSTI2022

A pilot-process for calcium hydroxide production from iron slag by low-temperature precipitation

The production of Ca(OH) 2 via thermal decomposition of limestone is an energy intensive process resulting in significant CO 2 emissions. However, if produced in a manner that obviates the need for the thermal decomposition of limestone, Ca(OH) 2 could be a “CO 2 -negative” material. Herein, we design and demonstrate the operation of a continuous, low-temperature (< 100 °C), aqueous-phase pilot-process to produce Ca(OH) 2 using calcium extracted from alkaline industrial wastes. The three-step process encompasses unit operations including: (i) calcium leaching from basic oxygen furnace BOF slag, (ii) leachate concentration by reverse osmosis (RO), and (iii) Ca(OH) 2 precipitation by temperature swing. Here this process presents several advantages compared to the traditional route. First, it advances the recycling of waste from the steel industry. Second, it operates significantly below the temperature used in conventional lime production (900 °C) as it bypasses limestone calcination, responsible for ~65% of the CO 2 emissions from the current process. Thus, the new process has a lower CO 2 footprint and enables the use of industrial waste heat. The mass and energy balances were quantified to reveal that increasing the calcium concentration of the feed solution and the precipitation temperature, decrease the energy demands of the RO step, thereby reducing the process’s overall CO 2 footprint. The pilot system operated continuously and achieved a production rate of nearly 1 kg per day of Ca(OH) 2 with a purity greater than 95 wt%. The average particle size of the precipitates depended on the residence time in the precipitation reactor, demonstrating an ability to produce size-controlled particulates. Importantly, the process achieved full water recirculation/reutilization level indicative of a low-consumable water demand. The outcomes offer new insights and understanding relevant to developing and upscaling low-CO 2 processes for cement, lime, and portlandite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Influence of background sources and topographic resolution in the Weather Research and Forecasting Model on xenon plume characteristics at monitoring stations

For many atmospheric monitoring applications, networks of measurement sites—such as the radionuclide stations of the International Monitoring System—can be sparse. With measurement locations potentially hundreds to thousands of kilometers from a release it is important to quantify the effects of physical processes on transport and dispersion of plumes between source and measurement locations. This study addresses the effects of background sources and topography resolution near the release location of radionuclides. We use the Weather Research and Forecasting (WRF) model with inline chemistry to investigate (1) how an additional, time-varying source of 133 Xe, such as an operational medical isotope production facility, contributes to activity concentration measurements at monitoring sites, and (2) how complex topography influences on atmospheric conditions near emission sources impact plume concentrations at varying distances from the source. Two 133 Xe emission sources, including (1) a high flux rate of short duration representing an explosive event, and (2) a variable and continuous background source, are simulated. The continuous background source contributes significantly to total 133 Xe concentrations at several monitoring stations. Further, a WRF simulation at 9 km horizontal resolution is compared with a nested grid simulation, where the innermost domain has a resolution of 1 km. Increased topographic resolution leads to an improved representation of plume responses to local winds, with topographic influences greatest at locations closest to the sources. Differences between the two domain resolutions decrease at greater distances from the sources, as plumes have time to spread and mix and are influenced by synoptic scale circulation patterns that are represented similarly in both simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Recent advances in integrated hydrologic models: Integration of new domains

Over the past several decades, hydrologic models have advanced from independent models of the surface and subsurface to integrated models that can capture the terrestrial hydrologic cycle within one framework. In recent years, these coupled frameworks have seen the inclusion of biogeochemical processes, ecohydrology, sedimentation and erosion, cold region hydrology, anthropogenic activities, and atmospheric processes. This expansion is the result of increased computational, data, and modeling capabilities and capacities, as well as improved understanding of the processes that drive these integrated systems. Here, in this study, we review these recent advances to integrate new processes and systems into existing terrestrial hydrologic models and highlight the significant challenges and opportunities that remain. We identify that with so many models currently available and in development, selecting the most appropriate model is difficult, and we suggest a path for new or novice modelers to find the most appropriate code based on their needs. In addition, data required to parameterize and calibrate these models can often constrain their applicability and usefulness. However, advances in environmental sensors and measurement technology, in addition to data assimilation of non-traditional data (e.g. remote sensing, qualitative data) are providing new ways of addressing this issue. As we expand hydrologic models to integrate more processes and systems, our computational demands also increase. Recent and emerging advances in computational platforms, including cloud and quantum computing, in addition to the use of machine learning to capture some processes, will continue to support the use of increasingly larger and more complex, process-based models. Finally, we highlight that it is critical to develop state-of-the-science models that are accessible to all model users, not just those applied for research and development. We encourage continued development of diverse modeling platforms, considering the user needs, data availability, and computational resources.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Khand, Kul↗

DOE OSTI2023

Computer-controlled finishing via dynamically constraint position-velocity-time scheduler

In a Computer Numerical Controlled (CNC) finishing process, the target material removal from an optical surface is guided by the convolution between the influence function of a machine tool and its dwell time at certain points over the surface. To reduce dynamics stressing and increase machining efficiency, the dwell time must be converted to varying velocities, which are the actual inputs to the machine tool controller. Conventionally, the conversion assumed constant acceleration and relied on linear motion interpolation, which caused discontinuities in velocities. This unsmooth motion affects the material removal distribution, and, thus, the accuracy of the finished surface shape. Many modern CNC machines support the smoother, cubic-polynomial interpolated Position-Velocity-Time (PVT) motion mode; however, the conventional scheduler may fail to provide suitable velocities for the PVT. Here in this study answers this challenge by proposing a novel PVT-based velocity scheduler that achieves smooth motion while considering CNC dynamic limits. Firstly, the principle of the PVT is explained, and the PVT-based velocity scheduler is formulated. Secondly, a quadratic programming is used to optimize the velocities by imposing the CNC dynamic constraints and the C 1 continuities (zeroth and first derivatives are continuous) simultaneously. Thirdly, the smoothness and accuracy of the scheduled velocities are studied on different kinds of tool paths via simulation. Finally, a sub-0.3 nm level surface finishing experiment using ion beam figuring is demonstrated to verify the feasibility of the proposed method. The PVT-based scheduler and simulator code is open-sourced.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fast-rate joining of thermoplastic composites using integrated additive manufacturing and compression molding process

The integration of additive manufacturing (AM) technology with compression molding (CM), has emerged as a high-performance composite manufacturing technology in recent years. In the AM-CM process, quickly deposited AM preform on a mold undergoes a rapid compaction cycle to fabricate structurally robust composite parts due to highly controlled fiber alignment (from AM) and reduced porosity (from CM). Currently, AMCM-based part size is limited by the size of the mold volume, thus posing a challenge to manufacture scalable parts. Here, this work focuses on joining techniques developed to enable fast rate joining of short fiber-reinforced thermoplastic composite parts using the AM-CM process. Acrylonitrile butadiene styrene resin reinforced with 20 wt% short carbon fiber composites was printed onto a flat mold and pressed under a hydraulic press. Fabricated panels were joined by the (a) mechanical impression at the joining interface and (b) over-molded continuous carbon fiber (CCF) sheet. Tensile tests were performed to characterize the joining strength of both mechanical impression-based joints and CCF over molded joints. Among the mechanical impression-based joints, a U-shape channel allowed the fibers to flow between two joint parts, and 280 % increased mechanical properties were observed. The continuous carbon fiber-based over molded joint CCF showed 350 % increase in tensile strength compared to the butt joints.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Single-shot switching in Tb/Co-multilayer based nanoscale magnetic tunnel junctions

Magnetic tunnel junctions (MTJs) are elementary units of magnetic memory devices. For high-speed and low-power data storage and processing applications, fast reversal of the magnetization by an ultrashort laser pulse is extremely important. Here we demonstrate single-shot switching of Tb/Co-multilayer based nanoscale MTJs by combining the optical writing and the electrical read-out methods. A 90-fs-long laser pulse switches the magnetization of the storage layer (SL). The change in the tunneling magnetoresistance (TMR) between the SL and a reference layer (RL) is probed electrically across the oxide barrier. Single-shot switching is demonstrated by varying the cell diameter from 300 nm to 20 nm. The anisotropy, magnetostatic coupling, and switching probability exhibit cell-size dependence. By suitable association of laser fluence and magnetic field, successive commutation between high-resistance and low-resistance states is achieved. The nature of the magnetization reversal of both SL and RL in a continuous film is probed with a depth-resolved magneto-optical Kerr effect (MOKE) magnetometry. The ultrafast dynamics in the continuous full-MTJ stack is investigated with the time-resolved pump–probe technique. Our experimental findings provide strong support for the growing interest in ultrafast spintronic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A fourth-order phase-field fracture model: Formulation and numerical solution using a continuous/discontinuous Galerkin method

Modeling crack initiation and propagation in brittle materials is of great importance to be able to predict sudden loss of load-carrying capacity and prevent catastrophic failure under severe dynamic loading conditions. Second-order phase-field fracture models have gained wide adoption given their ability to capture the formation of complex fracture patterns, e.g. via crack merging and branching, and their suitability for implementation within the context of the conventional finite element method. Higher-order phase-field models have also been proposed to increase the regularity of the exact solution and thus increase the spatial convergence rate of its numerical approximation. However, they require special numerical techniques to enforce the necessary continuity of the phase field solution. In this paper, we derive a fourth-order phase-field model of fracture in two independent ways; namely, from Hamilton’s principle and from a higher-order micromechanics-based approach. The latter approach is novel, and provides a physical interpretation of the higher-order terms in the model. In addition, we propose a continuous/discontinuous Galerkin (C/DG) method for use in computing the approximate phase-field solution. This method employs Lagrange polynomial shape functions to guarantee -continuity of the solution at inter-element boundaries, and enforces the required regularity with the aid of additional variational and interior penalty terms in the weak form. Finally, the phase-field equation is coupled with the momentum balance equation to model dynamic fracture problems in hyper-elastic materials. Two benchmark problems are presented to compare the numerical behavior of the C/DG method with mixed finite element methods.

42 ENGINEERING↗

DOE OSTI2021

Convergence of micro-geochemistry and micro-geomechanics towards understanding proppant shale rock interaction: A Caney shale case study in southern Oklahoma, USA

As a direct outcome of economic development coupled with an increase in population, global energy demand will continue to rise in the coming decades. Although renewable energy sources are increasingly investigated for optimal production, the immediate needs require focus on energy sources that are currently available and reliable, with a minimal environmental impact; the efficient exploration and production of unconventional hydrocarbon resources is bridging the energy needs and energy aspirations, during the current energy transition period. The main challenges are related to the accurate quantification of the critical rock properties that influence production, their heterogeneity and the multiscale driven physico-chemical nature of rock–fluid interactions. A key feature of shale reservoirs is their low permeability due to dominating nanoporosity of the clay-rich matrix. As a means of producing these reservoirs in a cost-effective manner, a prerequisite is creation of hydraulic fracture networks capable of the highest level of continued conductivity. Fracturing fluid chemical design, formation brine geochemical composition, and rock mineralogy all contribute to swelling-induced conductivity damage. The Caney Shale is an organic-rich, often calcareous mudrock. Many studies have examined the impact that clay has on different kinds of shale productivity but there is currently no data reported on the Caney Shale in relation to horizontal drilling; all reported data on the Caney Shale is on vertical wells which are shallow, compared to an emerging play that is at double the depth. Here, in this work we develop geochemical–geomechanical integration of rock properties at micro-and nanoscales that can provide insights into the potential proppant embedment and its mitigation. The novel methodology amalgamates the following: computed X-ray tomography, scanning electron microscopy, energy dispersive spectroscopy, micro-indentation, and Raman spectroscopy techniques. Here, our results show that due to the multiscale heterogeneity in the Caney Shale, these geochemical and structural properties translate into a variation in mechanical properties that will impact interaction between the proppant and the host shale rock.

03 NATURAL GAS↗

DOE OSTI2022

Long-term permeability evolution of shale seal rocks with argon and scCO 2

In this work, we conducted a suite of experiments to evaluate the long-term permeability evolution of shale rocks under constant effective stress, before, during, and after interaction with supercritical carbon dioxide (scCO 2 ). To do so, we measured the time-dependent evolution of argon permeabilities for the pre- and post-reacted samples (i.e., before and after long-term interaction with scCO 2 ). In addition, we obtained permeability evolution during long-term interaction with scCO 2 . The samples showed either relatively constant permeabilities or a moderate decrease during pre reaction long-term argon tests. The permeability evolution during long-term CO 2 tests showed continuous increase, continuous decrease, or cycles of increase/decrease in permeability. The long term response of the samples to CO 2 included phenomena such as 20 (i) salt precipitation, (ii) swelling-induced cracks, and (iii) carbonate dissolution. While it is obvious that salt precipitation and swelling-induced cracks decrease and increase the permeability, respectively, the sample response to carbonate dissolution proved to be more complex and may increase or decrease the permeability. The permeability evolution during post-reaction long-term argon injection is also affected by the contribution of each of these three phenomena, during long-term interaction with CO 2 . We observe increase, decrease, and constant permeability evolution during post-reaction argon tests. Our experiments reveal that the initial permeability of the samples plays a significant role on the long-term permeability response of shales in the presence argon and scCO 2 fluids. This study shows, when shales are hydraulically fractured with CO 2 , their initial permeability has a more significant role than their permeability evolution over time.

58 GEOSCIENCES↗