About

Milind Deo is the Peter D. and Catherine R. Meldrum Endowed Professor and the Director of the Energy & Geoscience Institute at the University of Utah's Department of Chemical Engineering. His research interests encompass carbon dioxide capture, computer simulation and modeling, energy storage systems, hydrocarbon fuels, natural gas, oil pipeline flow assurance, and related energy technologies. He has received numerous awards including the Distinguished Service Award from the College of Engineering in 2019, the Distinguished Lecturer honor from the Society of Petroleum Engineers, and the Peter D. and Catherine R. Professorship. He is a Fellow of the American Institute of Chemical Engineers and has been recognized for his distinguished service in reservoir characterization and dynamics. His extensive publication record reflects a focus on understanding and optimizing processes related to energy production, subsurface energy resources, and fluid flow in porous media.

Research topics

• Chemistry
• Geology
• Materials science
• Waste management
• Environmental science
• Statistical physics
• Physics
• Engineering
• Composite material
• Chemical physics
• Mechanical engineering
• Organic chemistry

Selected publications

• Performance evaluation of borehole thermal energy storage systems: A sensitivity, stochastic, and optimization approach

  Renewable Energy · 2026-03-27

  article
  PublisherDOI

• Spatiotemporal evolution of temperature extremes across India’s agro-climatic zones (1951–2025)

  2026-04-12

  articleOpen access

  Long-term changes in temperature extremes are a robust signature of anthropogenic climate change, yet their spatial structure across India’s agro-climatic zones (ACZs) remains insufficiently resolved at policy scales. Here, we quantify changes in temperature extremes across 14 mainland ACZs during 1951–2025 using the India Meteorological Department 1°×1° gridded daily dataset. We compute 22 indices, including 11 ETCCDI metrics, 7 India-specific thresholds, and 4 crop-specific growing degree day (GDD) indices, and assess trends using the Mann–Kendall test with trend-free pre-whitening and Sen’s slope estimator. Tropical Nights (TR20; Tmin ≥ 20 °C) exhibit the most spatially coherent increase, with significant trends in 10 of 12 Tier A/B zones, peaking in the Western Dry Region (ACZ-14; +3.182 days decade⁻¹; p < 0.05). Warm-day frequency (TX90p) increases significantly in 9 zones, with strongest trends in the Southern Plateau (+2.962% decade⁻¹), East Coast Plains (+2.940% decade⁻¹), and West Coast Plains and Ghats (+2.917% decade⁻¹). Diurnal temperature range declines in 8 zones, indicating faster warming of minimum than maximum temperatures across Gangetic, arid, and interior plateau regions, while coastal and southern plateau zones show daytime-dominant warming. Seasonal thermal accumulation intensifies, with significant increases in rabi wheat GDD in the Trans-Gangetic Plain (+11.130 GDD decade⁻¹) and Gujarat Plains (+21.625 GDD decade⁻¹). Temporal analysis identifies 2001–2025 as the warmest 25-year period in the 1951–2025 record across most ACZs. These results provide the first comprehensive ACZ-level ETCCDI characterisation of India's 75-year thermal extreme record, revealing spatially heterogeneous warming with pronounced nocturnal intensification across the Gangetic and arid zones and daytime-dominant warming in coastal and southern plateau zones, with direct relevance for agricultural adaptation planning.

  PublisherOA PDFDOI

• Mechanically robust mesoporous solids by dual-mode supramolecular self-assembly

  Scientific Reports · 2026-04-22

  articleOpen access

  Centimeter-scale mesoporous silica monoliths were synthesized by a multi-step fabrication process by combining cooperative and evaporation-induced self-assembly with high-pressure compaction. The fabricated monoliths were highly permeable with a surface area exceeding 270 m2/g from a continuous SBA-16 cubic mesoporous matrix. This new type of mesoporous solids exhibits excellent mechanical stability comparable to sedimentary rocks, despite having a low density of only ~ 1.1 g/cm3.

  PublisherOA PDFDOI

• Slip Flow in Hydrophilic Nanopores of Silica Colloidal Crystals

  Langmuir · 2025-01-14 · 1 citations

  articleCorresponding

  Slip flow, a fluid flow enhanced in comparison to that calculated using continuum equations, has been reported for many nanopores, mostly those with hydrophobic surfaces. We investigated the flow of water, hexane, and methanol through hydrophilic nanopores in silica colloidal crystals. Three silica sphere sizes were used to prepare the crystals: 150 ± 30, 500 ± 40, and 1500 ± 100 nm. The spheres were pressure-packed in a fused silica capillary with an inner diameter of 75 μm. The resulting colloidal crystals had an average pore radius of 18 ± 4, 66 ± 6, and 215 ± 14 nm for the three silica sphere sizes used. The colloidal crystals were demonstrated to possess almost perfect packing. The fluids were flown through the colloidal crystals, and the pressure drop was measured using a pressure transducer. The flow rates varied from 10 to 80 nL/min. Water showed no-slip Hagen-Poiseuille flow with no enhancement for all of the pore sizes. Hexane showed a 20-fold flow enhancement for the smallest pore size, and the enhancement diminished for the medium pore size and was absent for the largest pore size. Methanol also showed a 20-fold flow enhancement for the smallest pores, about a 15-fold enhancement for the medium pores, and no enhancement for the largest pore size. The reduction in flow enhancement was significantly steeper for hexane than for methanol with an increasing pore size. These results demonstrate a significant slip flow in small (15 nm) hydrophilic nanopores for non-wetting fluids, which is size- and fluid-property-dependent. These observations are important for understanding fluid dynamics in liquid chromatography and naturally occurring nanoporous media.

  PublisherDOI

• Dynamic Reservoir Modeling of the Utah FORGE Enhanced Geothermal Project Using Fast Marching Method

  2025-09-01

  article

  Abstract The Utah FORGE project is the largest Enhanced Geothermal System (EGS) demonstration site for geothermal energy production in low permeability formation with limited subsurface water availability. The FORGE project encompasses hydraulic stimulation followed by fluid circulation test. High-resolution EGS simulations are computationally intensive because they involve non-isothermal flow within hard rocks containing hydraulic and natural fractures. This study involves the development and history-matching of a reservoir model at the Utah FORGE site based on a discrete fracture network model and dynamic data from circulation tests for assessment of long-term performance and sustainability of the geothermal project. We propose a novel Fast-Marching-Method (FMM) based accelerated dynamic reservoir modeling approach enabling orders of magnitude faster simulation and demonstrate its power and efficacy through application at the Utah FORGE site. The reservoir dynamic model for the Utah FORGE site is developed based on a Discrete Fracture Network (DFN) model constructed using well logs and microseismic data. A month-long circulation test results are used as observational data for history matching and model updating. To mitigate the high computational cost from repeated simulations during history matching, we utilize the Fast Marching Method (FMM)-based simulation that transforms 3D fine-scale simulations into equivalent multi-resolution simulations using Diffusive Time of Flight (DTOF) as spatial coordinate. The DTOF represents the propagation time of the ‘pressure front’ and is computed in seconds by solving the Eikonal equation with FMM. Using the DTOF contours, the 3D fine-scale model is converted into a coarse multi-resolution model while preserving the 3D fine-scale near-wellbore region to maintain the hydraulic fracture fidelity and leading to the orders of magnitude acceleration in simulation time. The FMM-based multi-resolution simulation is applied to the Utah FORGE model and compared with 3D fine-scale simulation using a commercial simulator. The proposed approach is shown to speed up the simulation by more than an order of magnitude (10 to 20 times) with minimal loss of accuracy. Using the fast simulation model, a multi-objective genetic algorithm is applied to calibrate the reservoir model using bottomhole pressure and fluid temperature obtained during the circulation test. The calibrated reservoir model is used to predict long-term geothermal performance for 10 years at the Utah FORGE site, providing estimates of production rates, fluid temperatures and thermal power output.

  PublisherDOI

• Effect of Fracture Connectivity between Production and Injection Wells in Enhanced Geothermal Systems (EGS)

  2025-06-08

  article

  ABSTRACT: Fracture connectivity plays a crucial role in governing heat extraction efficiency and long-term sustainability in Enhanced Geothermal Systems (EGS). This study evaluates the impact of different fracture connectivity configurations on thermal performance using a series of numerical simulations. Four groups of fracture networks were analyzed, varying in interconnectivity and surface area to assess their influence on produced water temperature and net heat extraction. The results indicate that increased fracture connectivity enhances heat transfer efficiency, leading to improved thermal retention and sustained energy recovery. Groups with higher fracture surface area exhibited slower thermal decline, mitigating early thermal breakthrough and extending the operational lifespan of the system. Net heat extraction was consistently higher in configurations with greater connectivity, reinforcing the importance of optimizing fracture networks for long-term geothermal energy production. Temperature distribution analysis further confirmed that highly connected fractures facilitate more uniform heat depletion, reducing localized thermal drawdown. These findings highlight the necessity of strategic fracture network design to maximize heat recovery while preventing premature reservoir depletion. Future work should explore adaptive fracture stimulation techniques to optimize connectivity dynamically based on real-time thermal performance. This study provides critical insights into optimizing EGS fracture connectivity for sustainable geothermal energy extraction.

  PublisherDOI

• Nanoparticle Deposition Techniques for Silica Nanoparticles: Synthesis, Electrophoretic Deposition, and Optimization- A review

  ArXiv.org · 2025-03-28

  preprintOpen access

  Silica nanoparticles have emerged as key building blocks for advanced applications in electronics, catalysis, energy storage, biomedicine, and environmental science. In this review, we focus on recent developments in both the synthesis and deposition of these nanoparticles, emphasizing the widely used Stöber method and the versatile technique of electrophoretic deposition (EPD). The Stöber method is celebrated for its simplicity and reliability, offering precise control over particle size, morphology, and surface properties to produce uniform, monodisperse silica nanoparticles that meet high-quality standards for advanced applications. EPD, on the other hand, is a cost-effective, room-temperature process that enables uniform coatings on substrates with complex geometries. When compared to traditional techniques such as chemical vapor deposition, atomic layer deposition, and spin coating, EPD stands out due to its scalability, enhanced material compatibility, and ease of processing. Moreover, Future research should integrate AI-driven optimization with active learning to enhance electrophoretic deposition (EPD) of silica nanoparticles, leveraging predictive modeling and real-time adjustments for improved film quality and process efficiency. This approach promises to accelerate material discovery and enable scalable nanofabrication of advanced functional films.

  PublisherOA PDFDOI

• Comparative Analysis and Ranking of 10 US Onshore Tight Oil Plays Based on Estimated Ultimate Recovery (EUR)

  2025-01-01 · 1 citations

  articleSenior author
  PublisherDOI

• Soil organic carbon (SOC) prediction using super learner algorithm based on the remote sensing variables

  Environmental Challenges · 2025-04-21 · 1 citations

  articleOpen accessSenior authorCorresponding

  • Developed a Super Learner (SL) SOC prediction model for various US locations. • Vegetation indices were more critical than topological variables. • Tested linear regression (LR) and random forest (RF) as the meta learners. • LR-based SL showed higher accuracy, but it was impacted by the list of base learners. • RF-based SL was better when the optimal set of base learners were unknown for LR. The absorption of carbon into the soil and its accurate monitoring is crucial for crop production rates and for mitigating global warming through increased carbon sequestration. Soil organic carbon (SOC) predictions using machine learning techniques have been actively researched because of their ability to handle non-linear relationships and predict accurately with limited prior assumptions about underlying mechanisms. However, the selection of appropriate machine learning methods remains a subject of debate, since each study area has unique data patterns, leading to various prediction performance across different algorithm types. To address these challenges, superlearner algorithm was employed to predict SOC with data from four U.S. states: Arkansas, Idaho, Nebraska, and Utah. Remote sensing variables derived from Sentinel-2 and ALOS PALSAR were used as predictors, with feature selection applied. Results indicated that the linear regression-based superlearner achieved higher accuracy (nRMSE: 7.6 %, R²: 0.804) compared to the random forest-based model (nRMSE: 8.3 %, R²: 0.768), likely due to its ability to better capture the specific data patterns through careful base learner selection and hyperparameter optimization. In contrast, the random forest-based model demonstrated low variance in accuracy across different base learner combinations. Both models were used to predict SOC at new locations in Salt Lake City, Utah, with the linear regression-based model showing more accurate prediction results (nRMSE: 52.9 %, RMSE: 0.48 % OC). This study of the selection of ML algorithms facilitates more reliable monitoring of SOC in various environmental circumstances, supporting establishment of strategies for addressing climate change and for agricultural production by quantifying SOC accurately.

  PublisherDOI

• EFRC-MUSE: Multi-Scale Fluid-Solid Interactions in Architected and Natural Materials

  2024-11-11

  reportOpen access1st authorCorresponding

  Phase interactions and fluid properties in geological and other environments are critical in applications ranging from hydrogen production and geologic storage and recovery, carbon dioxide storage and sequestration, and the sustainable use of water resources. The four goals of EFRC-MUSE: Multi-Scale Fluid-Solid Interactions in Architected and Natural Materials were based on the priority directions articulated in the Basic Research Needs documents, and the scientific needs in nanoscience. 1. Develop a fundamental understanding of confinement and surface interactions in mesoscale media with nanometer-sized pores on the phase behavior, thermodynamic and multiphase flow properties of multicomponent fluid mixtures. 2. Examine the impact of mineralogy and material heterogeneity on mechanical properties to better understand chemo-mechanical interactions in material failure. 3. Determine in-operando cross-scale structural and nanostructural material properties with fluids in confinement and flow under realistic condi

  PublisherOA PDFDOI

Frequent coauthors

• Palash Panja

  University of Utah

  48 shared

• F.V. Hanson

  28 shared

• Raúl Velasco

  18 shared

• Manas A. Pathak

  Galgotias University

  16 shared

• John McLennan

  University of Utah

  16 shared

• Kyeongseok Oh

  Yonsei University

  15 shared

• M. Royhan Gani

  Western Kentucky University

  13 shared

• Hai Huang

  Xidian University

  12 shared

Awards & honors

• Distinguished Service Award, College of Engineering, 2019
• Distinguished Lecturer, Society of Petroleum Engineers
• Peter D. and Catherine R. Professorship, University of Utah…
• Fellow, American Institute of Chemical Engineers 07/2012
• Distinguished Service in the Area of Reservoir Characterizat…