Engineering topics

Myshakin, Evgeniy [NETL Site Support Contractor, National Energy Technology Laboratory]

Publications and source records attributed to Myshakin, Evgeniy [NETL Site Support Contractor, National Energy Technology Laboratory].

8 records

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Microwave-Assisted Activation of Mo/HZSM5 in Methane Dehydroaromatization

Natural gas flaring is a significant challenge for oil and gas producers. One viable option to mitigate flaring is the conversion of natural gas into valuable chemicals. Applying microwave energy to heterogenous catalyst materials offers advantages in terms of higher rates and product selectivities in comparison to traditional thermal systems which can be designed for on-demand chemical production in modular reactor systems. Methane dehydroaromatization (MDHA) produces transportable, liquid aromatic products directly from natural gas, which is ideal for remote production but is hindered by low thermodynamic yields and rapid deactivation. The stability and performance of the Mo/ZSM5 catalyst is often determined by the state and nature of Mo in the catalyst, which in turn is dictated by the temperature and gaseous environment during catalyst activation. In this study, the Mo/HZSM5 catalyst was activated under three different heating modes (thermal, microwave E-field, and microwave H-field) and four different gas environments (CO, He, CH4+H2, and H2). All the activated catalysts were characterized using different tools and performance evaluated in MDHA.

methane dehydroaromatization↗

DOE OSTI2025

Density Functional Theory and Experimental Studies of Mo/ZSM-5 Utilizing Metal Promoters for Enhancing Methane Dehydroaromatization

The presentation reports enhanced Mo catalysts performance prepared with metal promoters. DFT and Raman studies are utilized to interpret the activity of select cations in the MDA reaction. Binding energies of promoters and their preference site location to internal surface of ZSM-5 are discussed in context of improved aromatic yield.

DFT calculations↗

DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the presentation prepared for the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗

DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the conference paper accompanying an oral presentation at the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗

DOE OSTI2025

The Effect of Metal Promoters in an Mo-Supported HZSM-5 Catalyst for Microwave-Assisted Methane Dehydroaromatization to Aromatics

Microwave (MW)-assisted methane dehydroaromatization (MDHA) using an Mo-supported HZSM-5 catalyst (Mo/HZ5) can convert methane into value-added aromatic products in modular microwave reactor systems, enabling producers to generate revenue from an otherwise wasted resource. Modifying the local environments of active Mo species with metal promoters potentially regulates the reaction/deactivation pathways and improves the heating properties of the Mo/HZ5 under microwaves. Herein, metal promoters (M), including monovalent K+ and bivalent Co2+ and Ni2+, were incorporated to form M-Mo/HZ5 and their MDHA performance was investigated.

metal promoters↗

DOE OSTI2025

Exploring Catalyst Compositions for Microwave-Assisted Methane Dehydroaromatization

The flaring of natural gas in U.S. shale regions remains a challenge for producers. One alternative to flaring is converting the wasted gas into valuable chemicals. Microwave-based processes offer a promising solution, potentially enabling the development of compact, modular systems for on-site production of chemicals, such as aromatics, from natural gas. This is due to the advantages of microwave heating, including efficient heating of compact volumes, accelerated reaction rates, and electrification of heating. However, developing effective catalysts for microwave-based processes is challenging, as conventional materials often require modification to be effectively heated by microwaves. This study provides an overview of a catalyst development project focused on a molybdenum-supported zeolite catalyst, optimized for the direct conversion of methane into aromatics under microwave irradiation. It details the synthesis, characterization, and the effects of promoters, as well as computational efforts undertaken to understand and enhance the catalyst's performance.

flare gas↗

DOE OSTI2025

Fracture Network Quantification for Enhanced Reservoir Characterization in CO2 Storage Sites

Presentation slides on “Fracture Network Quantification for Enhanced Reservoir Characterization in CO2 Storage Sites” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. Accurate reservoir characterization is fundamental for the safe and efficient operation of carbon capture, utilization, and storage (CCUS) projects. This process is not only critical during initial site selection but also plays a key role in continuously updating reservoir geomodels throughout injection operations to effectively manage subsurface storage sites. Our primary objective is to develop a comprehensive understanding of fracture networks within CO2 injection reservoirs, which are critical for predicting the behavior of injected fluids, ensuring storage integrity post-injection, and mitigating risks such as leakage or induced seismicity.

artifical intelligence / machine learning (AI/ML)↗

DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗