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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗
DOE OSTI2024

A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction–diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., T max ). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model’s potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Observation of spontaneous x-ray magnetic circular dichroism in a chiral antiferromagnet

X-ray magnetic circular dichroism (XMCD) signals are usually absent in antiferromagnets. In this paper, we report the observation of spontaneous XMCD spectra originating from the inverse triangular spin structure, or the polarization of the cluster magnetic octupole, in the chiral antiferromagnet Mn 3 Sn thin film. Finally, the result is consistent with the recent theoretical predictions that the inverse triangular spin structure can give rise to finite XMCD signals in the absence of net magnetization [J. Phys. Soc. Jpn 89, 083703 (2020) and Phys. Rev. Lett. 126, 157402 (2021)].

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗
DOE OSTI2024

Artificial Intelligence for Conjugated Polymers

Conjugated polymers have garnered significant attention due to their diverse applications in electronics, photonics, and energy storage. However, realizing their full potential poses a formidable challenge, as their design has historically relied on iterative adjustments and continuous inspiration from researchers. Traditional methods often struggle to efficiently navigate their vast chemical landscape. In this work, the application of artificial intelligence (AI), specifically machine learning (ML), needs to be discussed in the realm of conjugated polymers. Our paper emphasizes the importance of understanding the structure–property relationships of these polymers and how ML can facilitate property prediction and inverse-design. We delve into various chemical fingerprints, structural descriptors, and ML algorithms, showcasing their utility across a spectrum of applications, including simulations, glass transition temperature determination, photovoltaics, reorganization energy for charge transport, photocatalysts, and sensors. Finally, we give some outlooks in this filed and propose unexplored areas within the field that hold the potential to benefit from ML techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Insertion products in the reaction of carbonyl oxide Criegee intermediates with acids: Chloro(hydroperoxy)methane formation from reaction of CH 2 OO with HCl and DCl

The reactions of carbonyl oxides (“Criegee intermediates”) with organic and inorganic acids have been postulated to proceed predominantly by an insertion mechanism, and the hydroperoxy-substituted ester products from reactions with organic acids have been spectroscopically observed. Here this work characterizes the products from one of the simplest reactions of carbonyl oxides with inorganic acids, that of formaldehyde oxide, CH 2 OO, with hydrogen chloride, which occurs via a 1,2-insertion in the H–Cl bond. Reactions of both HCl and DCl isotopologues yield product signal at the mass of the insertion product chloro(hydroperoxy)methane and a dissociative ionisation peak at the mass of the protonated (or deuteronated) Criegee intermediate. The isotopic composition of the insertion product has been measured for reaction mixtures where both HCl isotopologues are present, and the H/D ratio of the product is consistently higher (by a factor of 1.6 ± 0.3) than that of the reactants. This isotope selectivity in the products has smaller uncertainty than the ratio of measured rate coefficients and suggests a normal (k H > k D ) kinetic isotope effect in the reaction. Theoretical kinetics calculations predict a small normal kinetic isotope effect for the overall reaction (k H / k D = 1.35 at 20 Torr N 2 and k H / k D = 1.2 for 1 atm N 2 ) but predict a substantial inverse kinetic isotope effect (k D > k H ) for the stabilisation fraction, in disagreement with the experimental observation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗
DOE OSTI2025

Predictable third harmonic generation in GaAs metasurfaces through group theory inverse design of meta-atoms

Here we report the group theory-based inverse design of meta-atoms for a dielectric metasurface in GaAs with predictable optical linear response and third harmonic generation (THG). Six sharp Fano resonances have been observed with a corresponding polarization dependence as predicted by group theory and the meta-atom’s symmetry in the D2h point group. THG has been observed for two modes under x-polarization excitation and one mode for y-polarization, in agreement with theoretical symmetry predictions. The polarization-dependent THG aspect ratio was observed to reach as high as 108. Through strategic structural or symmetry-preserving perturbations, it was shown that the THG can be enhanced or reduced by a factor of 7. The highest THG conversion efficiency was estimated to be 3.1×10-7 at the pump intensity of 1.51 MW/cm2. This high THG conversion efficiency indicates that our group theory approach to modal engineering opens a new path towards optical nonlinearity tuning in dielectric metasurfaces.

Aoueille, Andrew↗
DOE OSTI2025

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis and Observation of Emergent Phenomena in Epitaxial Heusler Compound Heterostructures

The proposal was on the synthesis and observation of emergent phenomena in epitaxial Heusler compound heterostructures. The large range of properties and number of Heusler compounds opens up a wide number of potential compounds that will exhibit emergent phenomena. The similarity, large range of relatively inexpensive, large area, high crystal quality, III-V bulk substrates, lattice parameters and the ability to tune the lattice parameters through ternary or quaternary III-V compound semiconductor epitaxial growth, makes III-V semiconductors an ideal choice for substrates for epitaxial growth of Heusler compounds. A number of half Heusler compounds have been predicted to exhibit band inversion, making them topological and are therefore expected to exhibit spin-momentum locked topological surface states with linear dispersion. Others are predicted to be semimetals with Weyl points and others semiconducting and magnetic. During the course of this grant, emphasis has been on investigating Heusler compounds with emergent phenomena and demonstrating the ability to tune their properties through alloying and strain. We have grown toplogical semimetal (PtLuSb, PtMnBi), Weyl (Co 2 MnAl, Co 2 TiGe), half metal (PtMnSb, Co 2 MnSi, Co 2 MnAl x Si 1-x , Co2FeAl), and semiconducting (CoTiSb, NiTiSn) and tuned their properties through alloying and epitaxial strain. We also investigated the closely related materials of rare-earth monopnictide, some of which have also been predicted to be topological. During the attempts to grow the PtMnBi, it was discovered that Bi, another predicted topological material when ultrathin, could be grown epitaxially on InSb, results for which are also reported here. The main focus for this effort has been on using variable photon energy and spin-dependent angle resolved photoemission (ARPES) to determine bulk band structure and surface states of pristine epitaxial films grown on III-V semiconductor and MgO substrates and correlate results with theory and transport measurements. Theory has been critical to interpretation of experimental results and has been essential in guiding experiments. The research benefited from several strong collaborations between the PIs and the beamline scientists at the Advanced Light Source at Lawrence Berkeley Laboratory, the Stanford Linear Accelerator Center (SLAC) at Stanford and at the Max Lab at Lund University in Sweden. The strong experiment - theory collaboration between the PI’s groups, the Palmstrøm group at UCSB and the Janotti group at the University of Delaware, has been critical for interpreting the experimental ARPES and magnetotransport measurements results and making predictions to guide experiments. Weekly interactive Zoom meetings made this work well. A collaboration between the Palmstrøm group and Dr. Alexei Fedorov at the Advanced Light Source (ALS) resulted in significant modifications to his end chamber to accommodate the vacuum suitcase that was designed and constructed in the Palmstrøm group at UCSB. In collaboration with beamline scientists, Drs. Makoto Hashimoto and Donghui Lu at SLAC, Palmstrøm made modifications to the vacuum suitcase and developed special sample holders that allowed samples to be grown in the Palmstrøm MBE systems at UCSB and transported in the UHV vacuum suitcase to SLAC for ARPES measurements. The development of the vacuum suitcase was essential for this grant as it has allowed variable photon energies to be used to identify surface versus bulk states on samples that could not be capped and decapped using As- or Sb-capping layers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Inverse bremsstrahlung absorption rate for super-Gaussian electron distribution functions including plasma screening

Here we provide analytic expressions for the effective Coulomb logarithm for inverse bremsstrahlung absorption which predict significant corrections to the Langdon effect and overall absorption rate compared to previous estimates. The calculation of the collisional absorption rate of laser energy in a plasma by the inverse bremsstrahlung mechanism usually makes the approximation of a constant Coulomb logarithm. We dispense with this approximation and instead take into account the velocity dependence of the Coulomb logarithm, leading to a more accurate expression for the absorption rate valid in both classical and quantum conditions. In contrast to previous work, the laser intensity enters into the Coulomb logarithm. In most laser-plasma interactions the electron distribution function is super-Gaussian [Langdon, Phys. Rev. Lett. 44, 575 (1980)], and we find the absorption rate under these conditions is increased by as much as ≈ 30% compared to previous estimates at low density. In many cases of interest the correction to Langdon's predicted reduction in absorption is large; for example at Z = 6 and Te = 400 eV the Langdon prediction for the absorption is in error by a factor of ≈ 2. However, we also account for the additional effect of plasma screening, which predicts a reduction in absorption by a similar amount (up to ≈ 30%). These two effects compete to determine the overall absorption, which may be increased or decreased, depending on the conditions. The corrections can be incorporated into radiation-hydrodynamics simulation codes by replacing the familiar Coulomb logarithm with an analytic expression which depends on the super-Gaussian order “M” and the screening length.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Prediction of neutron production and energy spectrum by the inverse kinematic reaction between an incident 7 Li 3+ beam and a proton target in PHITS

A neutron source using the inverse kinematic reaction between lithium and proton, p( 7 Li, n) 7 Be, achieves forward-directed neutrons, potentially enhancing neutron yield in the forward direction. Despite the advantage, no evaluated-cross-section data for this reaction can be used in Monte Carlo simulation codes, such as PHITS. To solve this problem, this study aims to evaluate the applicability of the user-defined cross-section data, Frag data, for p ( 7 Li, n) 7 Be in PHITS. The simulations reproduced collisions between 7 Li 3+ ions and polypropylene targets. The Frag data was edited based on the JENDL-5 by utilizing the two-body collision kinematics. The neutron yield and angular distribution were investigated in the simulation. As a result, the forward neutron convergence with a reasonable neutron yield and energy spectrum was observed. The expected neutron yield in the forward 1-steradian area is 2.46 × 10 10 n/s when lithium-ion energy and current are 16.45 MeV and 0.1 mA.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Prediction of structure and cation ordering in an ordered normal-inverse double spinel

Abstract Spinels represent an important class of technologically relevant materials, used in diverse applications ranging from dielectrics, sensors and energy materials. While solid solutions combining two “single spinels” have been explored in a number of past studies, no ordered “double” spinels have been reported. Based on our first principles computations, here we predict the existence of such a double spinel compound MgAlGaO 4 , formed by an equimolar mixing of MgAl 2 O 4 normal and MgGa 2 O 4 inverse spinels. After studying the details of its atomic and electronic structure, we use a cluster expansion based effective Hamiltonian approach with Monte Carlo simulations to study the thermodynamic behavior and cation distribution as a function of temperature. Our simulations provide strong evidence for short-ranged cation order in the double spinel structure, even at significantly elevated temperatures. Finally, an attempt was made to synthesize the predicted double spinel compound. Energy Dispersive X-ray Spectrometry and X-ray diffraction Rietveld refinements were performed to characterize the single-phase chemical composition and local configurational environments, which showed a favorable agreement with the theoretical predictions. These findings suggest that a much larger number of compounds can potentially be realized within this chemical space, opening new avenues for the design of spinel-structured materials with tailored functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chiral Teropyrenes: Synthesis, Structure, and Spectroscopic Studies

Abstract We present the inaugural synthesis of a chiral teropyrene achieved through a four‐fold alkyne benzannulation catalyzed by InCl 3 , resulting in good yields. The product underwent thorough characterization using FT‐Raman and FT‐IR spectroscopies, demonstrating a close agreement with calculated spectra. X‐ray crystallographic analysis unveiled a notable twist in the molecule‘s backbone, with an end‐to‐end twist angle of 51°, consistent with computational predictions. Experimentally determined enantiomeric inversion barriers revealed a significant energy barrier of 23 kcal/mol, facilitating the isolation of enantiomers for analysis by circular dichroism (CD) and circularly polarized luminescence (CPL) spectroscopies. These findings mark significant strides in the synthesis and characterization of chiral teropyrenes, offering insights into their structural and spectroscopic properties.

Chemistry↗
DOE OSTI2026

Latent Twins

Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex systems. From inverse problems to numerical partial differential equations (PDEs), dynamical systems, and model reduction, these advances have pushed the boundaries of what can be simulated. Yet they have often progressed in parallel, with representation learning and algorithmic solution methods evolving largely as separate pipelines. With Latent Twins, we propose a unifying mathematical framework that creates a hidden surrogate in latent space for the underlying equations. Whereas digital twins mirror physical systems in the digital world, Latent Twins mirror mathematical systems in a learned latent space governed by operators. Through this lens, classical modeling, inversion, model reduction, and operator approximation all emerge as special cases of a single principle. We establish the fundamental approximation properties of Latent Twins for both ordinary differential equations (ODEs) and PDEs and demonstrate the framework across three representative settings: (i) canonical ODEs, capturing diverse dynamical regimes; (ii) a PDE benchmark using the shallow-water equations, contrasting Latent Twin simulations with deep operator network and forecasts with a four-dimensional variational method baseline; and (iii) a challenging real-data geopotential reanalysis dataset, reconstructing and forecasting from sparse, noisy observations. Latent Twins provide a compact, interpretable surrogate for solution operators that evaluate across arbitrary time gaps in a single-shot, while remaining compatible with scientific pipelines such as assimilation, control, and uncertainty quantification. Looking forward, this framework offers scalable, theory-grounded surrogates that bridge data-driven representation learning and classical scientific modeling across disciplines.

Latent Twins↗
DOE OSTI2023

Final Report DE-FE0031785 Mohsen Ahmadian, Ph.D. Demonstration of Proof of Concept of a Multiphysics Approach for Real-Time Remote Monitoring of Dynamic Changes in Pressure and Salinity in Hydraulically Fractured Networks

Hydraulic fracturing has evolved into a multistep process with varying flow rates, carrier fluids (e.g., gel or slickwater), proppant loadings, and proppant grain sizes. As a result, primary recovery from a hydraulically fractured tight-oil reservoir is often a tiny fraction of the original oil in place, ranging between 5 and 10%. As stated in the FOA1990, “part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir volume in contact with the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volumes, and optimize recovery over the entire producing life span of a well”. We first need to enhance the current fracture diagnostic techniques to improve a well-completion design. However, detecting and delineating a subsurface hydraulic fracture is extremely difficult because the induced fracture network is only fractionally propped, and these propped fractures are generally very thin. Microseismic and tiltmeter monitoring techniques can provide information on the fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. On the other hand, electromagnetic (EM) imaging has shown the capability to monitor proppant distribution throughout the fracture area, especially in the presence of Electrically Active Proppants (EAPs). A previous EM survey of hydraulic fracturing at the Devine Fracture Pilot Site (DFPS) and subsequent EM code developments demonstrated this survey as a robust technique to remotely interrogate the extent of the EAP-filled hydraulic fracture during its propagation. The objectives of the project were threefold: (1) to capitalize on the material properties of an EAP to demonstrate remote monitoring of relative changes in pressure, pressure, and flow that are commonly encountered during production from a hydraulically fractured reservoir; (2) to evaluate EM imaging tools, to achieve Objective 1 in near real-time; and (3) to develop a multi-physics joint inversion approach to precisely predict flow patterns and physiochemical changes within an EAP-filled fracture network. This research project was built upon our previous work at the Devine Test Site managed by the Bureau of Economic Geology (BEG) at The University of Texas at Austin (UT-Austin). It also leveraged a significant investment from the Advanced Energy Consortium (AEC) to address the DOE's interest in subsurface flow, containment, and characterization by multiple signals. This three-year and three-month project succeeded in demonstrating the feasibility of a real-time dynamic fluid flow mapping technique at Technology Readiness Level 5 (TRL5) by utilizing a commercially available surface-based Controlled-Source Electromagnetic (CSEM) method (Objectives 1, 2). We demonstrated that injections into an EAP-filled fracture could be successfully coupled with real-time electric field measurements on the surface, leading to remote monitoring of dynamic changes within the EAP-filled fracture. Furthermore, the observed electric field in our study is influenced by bottomhole pressure, flow rate, and salinity, which is demonstrated by comparing these parameters with the electrical field potentials. EM simulations solely based on assumptions of fracture conductivity changes during injection did not reproduce the whole measured electric field magnitudes. Preliminary estimates showed that including Streaming Potential (SP) in our geophysical model is likely needed to reduce the simulation misfit.

02 PETROLEUM↗