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At least 127 records · Page 7

Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method

Reductive catalytic fractionation (RCF) is a promising method to extract and depolymerize lignin from biomass, and bench-scale studies have enabled considerable progress in the past decade. RCF experiments are typically conducted in pressurized batch reactors with volumes ranging between 50 and 1000 mL, limiting the throughput of these experiments to one to six reactions per day for an individual researcher. Here, we report a high-throughput RCF (HTP-RCF) method in which batch RCF reactions are conducted in 1 mL wells machined directly into Hastelloy reactor plates. The plate reactors can seal high pressures produced by organic solvents by vertically stacking multiple reactor plates, leading to a compact and modular system capable of performing 240 reactions per experiment. Using this setup, we screened solvent mixtures and catalyst loadings for hydrogen-free RCF using 50 mg poplar and 0.5 mL reaction solvent. The system of 1:1 isopropanol/methanol showed optimal monomer yields and selectivity to 4-propyl substituted monomers, and validation reactions using 75 mL batch reactors produced identical monomer yields. To accommodate the low material loadings, we then developed a workup procedure for parallel filtration, washing, and drying of samples and a 1H nuclear magnetic resonance spectroscopy method to measure the RCF oil yield without performing liquid-liquid extraction. As a demonstration of this experimental pipeline, 50 unique switchgrass samples were screened in RCF reactions in the HTP-RCF system, revealing a wide range of monomer yields (21-36%), S/G ratios (0.41-0.93), and oil yields (40-75%). These results were successfully validated by repeating RCF reactions in 75 mL batch reactors for a subset of samples. We anticipate that this approach can be used to rapidly screen substrates, catalysts, and reaction conditions in high-pressure batch reactions with higher throughput than standard batch reactors.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

M-Star® Software Test and Verification for Impeller Mixing in a Tank

Before a sample of the Slurry Mix Evaporator (SME) can be taken, the SME product sampling procedure requires that the agitator power be stable between 20 and 30 kW for at least one hour. The SME transfer to Melter Feed Tank (MFT) procedure also requires the SME agitator power be stabilized between 20 and 30 kW prior to transfer. These requirements are specified to ensure samples are homogeneous, as discussed in the Waste Form Qualification Report. During SME Batch 804, the agitator power dropped to 19 kW and struggled to achieve and maintain 20 kW. It was later discovered that the cause of the power drop was due to one of the bottom blades of the agitator breaking off. Both the sample and the transfer occurred without the power stabilizing between 20 and 30 kW. Therefore, the quality of SME Batch 804 is indeterminate, and homogeneity was questionable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

M-Star ® Modeling of SME Mixing with Three Impeller Blades

Before a sample of the Slurry Mix Evaporator (SME) can be taken, the SME product sampling procedure requires that the agitator power be stable between 20 and 30 kW for at least one hour. The SME transfer to Melter Feed Tank (MFT) procedure also requires the SME agitator power be stabilized between 20 and 30 kW prior to transfer. These requirements are specified to ensure samples are homogeneous, as discussed in the Waste Form Qualification Report. During SME Batch 804, the agitator power dropped to 19 kW and struggled to achieve and maintain 20 kW. It was later discovered that the cause of the power drop was due to one of the bottom blades of the agitator breaking off. Both the sample and the transfer occurred without the power stabilizing between 20 and 30 kW. Therefore, the quality of SME Batch 804 is indeterminate, and homogeneity was questionable. To evaluate mixing in the SME with a broken agitator containing only three blades (90 degrees apart with space for a missing blade) on the bottom impeller, the Savannah River National Laboratory (SRNL) was requested to perform computer simulations of impeller mixing in the SME using M-Star ® software. The simulations will be used to determine if SME Batch 804 was well mixed and satisfies homogeneity requirements.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗

Optode performance data associated with: Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

This data package is associated with the publication “Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments”, submitted to PlosONE (Kaufman et al. 2023; 10.1101/2023.03.28.534656).We carried out many testing and calibration experiments on a system of small oxygen consumption batch reactors designed for use with water and sediment samples for environmental questions. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. Our primary focus was on practical considerations, such as temperature effects, lighting angle effects, sterilization, and other similar situations that a user may find useful. Most of the tests required comparing “base” calibration curves to “treatment” calibration curves to determine the extent to which the treatment impacted the reported measurements. This data package contains the performance and calibration data collected for that purpose.This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) diffusion test result files; (5) limit of detection test result files; (6) temperature impact files; (7) a main data file that contains test results for all other tests; and (8) an R script that uses Kolmogorov-Smirnov tests to determine whether treatment calibrations are significantly different from their respective base calibrations. All files are .csv, .txt, .Rmd, or .pdf.

54 ENVIRONMENTAL SCIENCES↗

Sludge Batch 11 Assembly: Tank 51

Savannah River Mission Completion (SRMC) Nuclear Safety and Engineering Integration has requested that Savannah River National Laboratory (SRNL) perform Tank 51 characterization analyses in support of Sludge Batch 11 (SB11) assembly. This report provides important characterization of the slurry in Tank 51 after transfers from Tank 22, Tank 35, and Tank 13 (post Tank 15 to Tank 13 transfer) to Tank 51 that demonstrates the sludge concurs with the estimated transfer mass for the SB11 recipe. A total of 3 sets of Tank 51 samples were delivered to SRNL from March 2023 to February 2024. The composite sample was analyzed for the following: density, weight percent solids, chemical composition, radionuclides, and supernate corrosion control analyses. The results of the Tank 51 samples are consistent with and representative of expected sludge projections for Sludge Batch 11.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

SRNL Sludge Batch 10 Qualification SRAT and SME Off-Gas Results

Savannah River National Laboratory (SRNL) completed a small-scale demonstration of the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) utilizing the nitric-glycolic acid (NGA) flowsheet to support Sludge Batch 10 (SB10) qualification. The demonstration utilized a Tank 51 slurry sample washed by SRNL (with added H-canyon material). The purpose of this document is to report the observed off-gas results from the demonstration. With the NGA flowsheet, DWPF has a CPC hydrogen generation limit of 2.4×10 -2 lb/h. The peak observed rate was nearly 90 times less than that limit during SRNL testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Application of Indirect Quantification of 133mXe to Calibration of HPGe Detector

The INL Noble Gas Laboratory provides intercomparison samples for the noble gas analysis laboratories as part of the CTBTO PrepCom IMS. Xe-133m is one of the four relevant radionuclides in nuclear explosion monitoring. Without commercially available Xe-133m calibration standards laboratories must create and improve calibration methods. Improvements in calibration methods at the INL NGL benefit the CTBTO PrepCom through better certified values for Xe-133m intercomparison samples. Calibration of High Purity Germanium detectors for Xe-133m quantification is complicated by the coexistence of Xe-133 in samples under analysis. Xe-133 is typically produced in larger quantities, has higher gamma emission probabilities, and its gammas are detected more efficiently than Xe-133m. Xe-133m activity of samples can be indirectly inferred through the 133:133m activity ratio of a batch of material, and the Xe-133 counts in the assay of a small aliquot of the same material. This indirect quantification method can be leveraged to perform detector calibrations for quantification of Xe-133m. Calibrations can be performed by inferring the Xe-133m to certify the sample, and direct counting to determine detector efficiency. A comparison of method results will be shown.

133mXe↗

Small Punch Testing of Molybdenum-99 Targets

Northstar Medical Radioisotopes is developing an accelerator-based method to produce 99 Mo, which is a parent isotope of the commonly used 99m Tc medical isotope. The Mo targets being designed for the accelerator will be produced from enriched 100 Mo, also known as aMo . Pressed and sintered powder feedstock is used to fabricate aMo targets, producing 29 mm disk-shaped targets. The targets are subjected to 1–6 days in line of an electron beam with subsequent dissolution of the disk to retain the 99 Mo, which decays to 99m Tc at radio-pharmacies. The press and sinter method is advantageous because the inherent porosity produced by this method enables increased surface area and therefore increased flow of dissolution media, decreasing the dissolution time and reducing the need for a highly acidic media. Although porosity aids in dissolution, it reduces the mechanical strength and ductility. Targets require good mechanical integrity when subjected to the conditions in the accelerator. Therefore, Northstar is seeking methods to rapidly test disk samples after fabrication to assure mechanical performance metrics are achieved. This report details the design and testing of a small punch test (SPT) that accommodates the 29 mm disk. Initial data were used to relate the SPT data to tensile properties, such as the yield strength (YS), ultimate tensile strength (UTS), and total elongation to failure. Although the SPT has been established as a somewhat reliable method for testing metallic materials, few studies have applied the SPT to refractory materials such as Mo. Based on tensile testing performed at Oak Ridge National Laboratory on different Mo samples, correlation between the Mo tensile and SPT properties could be performed, establishing a standard calibration that could be applied to other Mo samples. To test the efficacy of the SPT with Mo, multiple different disk batches were fabricated under different conditions (e.g., pressure, lubricant) with commercially available pure Mo powder. Generally, only a UTS could be well defined because the press and sinter disks failed under brittle fracture, making it difficult to determine the YS and elongation. Compared with disks fabricated with aMo powder, the aMo samples underperformed their pure nat Mo counterparts. This report summarizes the current status of the SPT, but further evaluation is needed before it can be applied as a reliable quality assurance tool.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

First direct 7 Be electron-capture $\mathrm{Q}$-value measurement toward high-precision searches for neutrino physics beyond the Standard Model

Here, we report the first direct measurement of the nuclear electron-capture (EC) decay Q value of 7 Be → 7 Li via high-precision Penning trap mass spectrometry (PTMS). This was performed using the LEBIT Penning trap located at the National Superconducting Cyclotron Laboratory/Facility for Rare Isotope Beams (NSCL/FRIB) using the newly commissioned Batch-Mode Ion-Source (BMIS) to deliver the unstable 7 Be + samples. With a measured value of Q EC = 861.963(23) keV, this result is three times more precise than any previous determination of this quantity. This improved precision and accuracy of the 7 Be EC decay Q value is critical for ongoing experiments that measure the recoiling nucleus in this system as a signature to search for beyond the Standard Model (BSM) neutrino physics using 7 Be-doped superconducting sensors. This experiment has extended LEBIT capabilities, using the first low-energy beam delivered by BMIS at FRIB for PTMS, as well as measuring the lightest-mass isotopes so far with LEBIT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. Here, in this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

97 MATHEMATICS AND COMPUTING↗

aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

High-fidelity complex engineering simulations are often predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous parallel constrained Bayesian optimization method is proposed to efficiently solve the computationally expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantage of this method are three-fold. Firstly, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the proposed method can handle both known and unknown constraints. Third, the proposed method samples several acquisition functions based on their rewards using a modified GP-Hedge scheme. The proposed framework is termed aphBO-2GP-3B, which means asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The numerical performance of the proposed framework aphBO-2GP-3B is comprehensively benchmarked using 16 numerical examples, compared against other 6 parallel Bayesian optimization variants and 1 parallel Monte Carlo as a baseline, and demonstrated using two real-world high-fidelity expensive industrial applications. The first engineering application is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

97 MATHEMATICS AND COMPUTING↗

Influence of porous aluminosilicate grain size materials in experimental and modelling Cs + adsorption kinetics and wastewater column process

This paper focuses on the influence of the grain size of a geopolymer based adsorbent on its Cs + adsorption performances both in batch and fixed-bed process. The geopolymer phase was used as a binder to support NaY zeolite particle in a 20 wt% charged porous composite with 160 m 2 .g –1 of porous surface area. These samples were shaped with three grain sizes (50 /100/500 µm) to remove 80–90 mg/g of Cs + in batch and column operations. After their microstructural and porous characterizations, their efficiency and adsorption characteristics were investigated through adsorption isotherms and kinetic in the two processes. While the grain size has no influence on the maximal extraction capacity of the adsorbent, it strongly affects the sorption kinetic. By coupling experimental data and a modelling approach, the complex sorption mechanism was highlighted, suggesting a new insight of the contaminant sorption kinetic. Then, comparison of batch and column adsorption experiments illustrates the detailed explanation of various process parameters for column study. The results show challenges for fixed-bed column utilization by the choice of the appropriate grain size as a compromise between the material sorption kinetic and hydrodynamic considerations. Furthermore, this is of high importance to more accurately optimize the design of column adsorption to assess the transport of Cs+ in multi-porous tailored grain size materials.

36 MATERIALS SCIENCE↗

Carbon Fiber Reinforced Plastic with Substantially Improved Through-plane Thermal Conductivity

Thermal conductivity of carbon fiber support structures that also serve as cooling substrates is of increasing importance for high energy physics detectors. One of the shortcomings of current carbon fiber laminates is that they exhibit excellent thermal conductivity of order several hundred W/(m*K) along the fiber direction, whereas the through plane thermal conductivity perpendicular to the fibers is orders of magnitude lower, and at about 1 W/(m*K) more in the range of thermal insulators. This proposal aims to significantly improve the through plane thermal conductivity, by at least a factor five, changing CFRP from being a thermal insulator to a thermal conductor in the through plane direction. Between June 2021 and December 2022, a total of 36 carbon fiber laminate test samples and two carbon loaded epoxy test samples were manufactured and analyzed for thermal performance. Unfortunately, the promising results obtained with a similar sample manufactured in 2017 were not reproduced. We believe this to be mainly due the excessive age of the carbon fiber prepreg material used for this SBIR - the same material batch, produced in 2015, was used in 2017 and in 2021/2022, i.e. the material was seven years old when the final samples were produced - , and to a lesser degree to the difference in pressure attainable with the 2017 and 2021/2022 laminate curing setups. While disappointing, the results are not completely discouraging, since for the samples with the highest compression the through-plane thermal conductivities plotted as a function of per-ply thickness extrapolate well to the data point from the 2017 sample.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

ETF Grout Expansion: Effects of Different Slags and Struvite Precipitation Protocols (Stirring Time)

The ETF ammonia tolerant grout waste form was developed by the Vitreous State Laboratory (VSL) to stabilize ammonium and thereby prevent emission of ammonia vapor during solidification of an ammonium-rich, concentrated sodium sulfate aqueous waste stream generated at the Hanford Effluent Treatment Facility (ETF). VSL personnel did not observe expansion in samples prepared during the waste form development work. However, Savannah River National Laboratory (SRNL) personnel detected expansion, in a few ETF grout samples, up to ~ 20 percent vertical expansion, while preforming work scope to evaluate the effect of ETF brine compositional ranges on the precipitation and solidification processes. The initial work requests can be found in Washington River Protection Services (WRPS) Statement of Work (SOW, Requisitions #: 339922 Revisions 0 and 1, March 11, 2021, and November 2021, respectively. As a result of the waste form expansion observed by SRNL, additional evaluation was requested by WRPS SOW Requisitions #: 356730 Revision 0 and Revision 1, February 1, 2022, and July 11, 2022, respectively. The goal of these requests was to determine whether differences in the slags and / or in sample preparation protocols used by VSL and SRNL were the cause of the expansion observed in the SRNL samples. G. Chen, WRPS, arranged a materials exchange between VSL and SRNL and coordinated the VSL protocol(s) for grouting and curing 1L batches of Base Case ETF base case simulant. Materials were exchanged between VSL and SRNL. Both laboratories performed the VSL struvite precipitation-solidification protocol and cured samples for at least 28 days. Differences in VSL and SRNL slag compositions, mineralogy and particle size results in themselves or combined were determined to not be the cause of the expansion because all samples expanded regardless of whether the VSL or SRNL slags were used. The fundamental cause of the observed expansion could not be attributed to minor variations in the following: 1) chemistry of the slag and other reagents, 2) container wall thickness, 3) ambient bench top curing conditions at VSL and SRNL, 4) pH adjustments to 7 ± a few tenths of unit, nor 5) stirring time between the precipitation and solidification process steps. Additional observations include: grout samples prepared with VSL slag expanded less than those prepared with the SRNL slag and, based on a very limited sample set, some samples stirred for longer times expanded less than those stirred for 20 minutes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

ML-based Micro-CT SOFC Microstructure Models (from Kent 2026 Microstructural Augmentation paper)

Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')

3D microstructure↗

Pore connectivity influences mass transport in natural rocks: Pore structure, gas diffusion and batch sorption studies

For this work, six rocks (one granodiorite, one limestone, two chalks, one mudstone, and one dolostone) with different extents of heterogeneity at six different particle sizes (from 75 to 8000 μm) were studied to describe the effects of pore connectivity on mass transport. The methods applied were (i) porosity measurement of granular rocks, (ii) analyses of gas-phase diffusive transport in a bed of packed particles, along with a solid quartz method at these six particle sizes being developed to identify the contribution of intraparticle diffusion, and (iii) batch sorption tests of multiple ions (anions and cations) with subsequent analyses of inductively coupled plasma-mass spectrometry. Granular porosity measurement results reveal that with decreasing particle sizes, the effective porosities for the “heterogenous” group of rocks (Grimsel granodiorite and Edwards limestone) increase, whereas the porosities of another “homogeneous” group (two Israel chalk samples, Japan mudstone, and Wyoming dolostone) remain constant. Gas diffusion results show that the intraparticle gas diffusion coefficient among these two sample groups, varying in the magnitude of 10 -8 to 10 -6 m 2 /s, are not directly correlated to the porosity differences. Moreover, the batch sorption work displays a different affinity of rocks for various tracers. For Grimsel granodiorite, Japan mudstone, and Wyoming dolostone, the adsorption capacity of Sm 3+ and Eu 3+ increases as the particle size decreases. In general, this integrated research of grain size distribution, granular rock porosity, intraparticle diffusivity, and ionic sorption capacity gives insights into the pore connectivity effect on both physical and chemical transport behaviors for different lithologies and/or different particle sizes.

58 GEOSCIENCES↗

Effects of Interactions Between Produced Formation Fluid and Rock Matrix on Pore Structure of Caney Shale, Southern Oklahoma

ABSTRACT: Rock-fluid interactions change properties of shales during exploitation. To investigate effects of rock-fluid interactions on pore structure of shales matrix after hydraulic fracturing, powder samples from two late Mississippian Caney Shale cores in the Ardmore Basin, southern Oklahoma, were used to react with formation produced fluid from the field in the batch reactor analysis. X-ray diffraction for mineralogy and Low-pressure nitrogen adsorption isotherms for pore structure were measured for original, after-7days, and after-30days samples. Results show that the samples consist mainly of quartz, followed by clay minerals, carbonates, and feldspar. The pore sizes of micropore (<2 nm) and mesopore (2-50 nm) increase 14%-233% due to dissolution of pyrite, feldspar, and carbonates after 7 days. Due to the transformation from smectite to illite and the increase of pore size, the specific surface area (SSA) decreases after 7-days interactions. After 30-days interactions, the micropore volume slightly increases and the mesopore and macropore volume decreases. Due to the decrease of pore size, the SSA of 30-days reacted samples increases correspondingly and is lower (for the clay-rich sample) or higher (for the calcareous sample) than that of the unreacted samples. Findings improve our understanding of dynamic alteration of shale properties during production. 1. INTRODUCTION Energy demand will continuously grow owing to the increasing global population as well as energy consumption (EIA, 2023). On the other hand, shale gas and oil reshaped the energy market in the United States, enabling the United States to become a net-export of natural gas country in 2017 (EIA, 2023). However, shale reservoirs are challenging tight formations that are still poorly understood in the extraction and production of hydrocarbons (Ross and Bustin, 2009; Curtis et al., 2012; Xiong et al., 2015, 2021a; Li Y. et al., 2016; Gong et al., 2019a; Benge et al., 2021; Awejori et al., 2022; Huang et al., 2022). One of the most challenging topics is the rock-fluid interactions post hydraulic fracturing and its subsequent impacts on the pore structures of fractured formation matrix.

Xiong, Fengyang↗