Colorado School Of Mines Research Articles

MatDEM-based study of disc cutter force model in open TBM tunnels: Incorporating installation radius and synergistic effects

The prediction of rock cutting force is critical for tunnel boring machine performance and cutterhead design. This paper presents a novel model for rock cutting force prediction based on the Colorado School of Mines (CSM) model, which incorporates the installation position of disc cutters by introducing installation radius and synergistic effect factors. Linear cutting tests in the laboratory and large-scale rotary cutting simulations in MatDEM software were conducted to examine the impact of these factors. Results indicate that the normal and rolling forces increase and stabilize as the installation radius increases. The synergistic effect produces three force modes in a cutting circle, with mode α having the largest cutting force, mode β having a smaller force, and mode γ having the smallest force. The impact of installation radius and synergistic effect varies with rock-cutter parameters. Multiple regression analysis was used to determine the introduced factors. The proposed model was validated with rock strength and operation data from the Irtysh River conveyance project. The results demonstrate that the proposed model outperforms the CSM model in predicting cutting force in field conditions.

Integrated Multiphase-Flow Modeling Technique Yields Accurate Downhole Pressure Predictions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214855, “Integrated Multiphase-Flow Modeling for Downhole Pressure Predictions,” by Abdullah Alkhezzi and Yilin Fan, SPE, Colorado School of Mines. The paper has not been peer reviewed. _ This work presents an integrated multiphase flow model for downhole pressure predictions. The aim of the model is to produce more-accurate downhole pressure predictions under wide flowing conditions while maintaining a simple form. As a component of the integrated model, an improved two-fluid model for segregated flow is proposed. Results of the two-fluid segregated model are compared with five state-of-the-art existing models, while results of the integrated model are compared with three models. Introduction Throughout the life of the well, knowing bottomhole flowing pressure (Pwf) and the pressure profile of the wellbore are of great significance. Unfortunately, deploying downhole pressure gauges to obtain Pwfreadings is often not economical or practical. The common practice is to apply hydraulic models to predict Pwfgiven surface measurements. The prediction of such behavior is simple when dealing with single-phase fluid flow. Unfortunately, this condition is rare in the petroleum industry. The existence of multiple phases introduces multiple complexities hindering the accuracy of downhole pressure predictions. To account for such variations, fluid-property models must be integrated into the calculation procedure. The complexity, coupled with field-data scarcity, results in the deficiency of work that evaluates point models on actual wells. In this work, the authors evaluated the performance of a few widely used multiphase-flow point-based models on actual field data using a marching algorithm and developed a simplified, yet more precise, integrated model. Integrated Model Development Data Set Description. In this study, data points were collected for two main purposes. First, experimental data sets were collected to model and improve the segregated flow model. Second, field data were collected to test the integrated multiphase model. To improve the segregated flow model, 1,478 experimental data points were obtained from various sources in the literature. To evaluate the integrated multiphase flow model, 313 data points from two main sources of data were used (literature and actual field data). In total, four data sets were obtained from the literature and one was obtained from Civitas Resources. Integrated Model Description. The multiphase-flow point model incorporated in the authors’ integrated modeling consists of three main components: critical gas velocity estimation for the onset of liquid loading and hydraulic multiphase flow models before and after the onset of liquid loading. The proposed model characterizes the flow based on whether liquid loading occurs. The onset of liquid loading corresponds with the transition of the flow pattern from segregated to intermittent flow. At points where the superficial gas velocity is estimated to be lower than the critical gas velocity and the flow is considered intermittent or bubbly, the drift-flux model is to be used, which works well in flow patterns where liquid volume is high. Another strength of the model is its ability to capture countercurrent flow. The drift-flux homogeneous-like approach makes the process simple and continuous and simultaneously captures some physics through slippage. For points where the superficial gas velocity is greater than the critical gas velocity, two-fluid modeling is to be used.

Assessing cutter-rock interaction during TBM tunnelling in granite: Large-scale standing rotary cutting tests and 3D DEM simulations

The widespread utilisation of tunnel boring machines (TBMs) in underground construction engineering requires a detailed investigation of the cutter-rock interaction. In this paper, we conduct a series of large-scale standing rotary cutting tests on granite in conjunction with high-fidelity numerical simulations based on a particle-type discrete element method (DEM) to explore the effects of key cutting parameters on the TBM cutter performance and the distribution of cutter-rock contact stresses. The assessment results of cutter performance obtained from the cutting tests and numerical simulations reveal similar dependencies on the key cutting parameters. More specifically, the normal and rolling forces exhibit a positive correlation with penetration but are slightly influenced by the cutting radius. In contrast, the side force decreases as the cutting radius increases. Additionally, the side force shows a positive relationship with the penetration for smaller cutting radii but tends to become negative as the cutting radius increases. The cutter's relative effectiveness in rock breaking is significantly impacted by the penetration but shows little dependency on the cutting radius. Consequently, an optimal penetration is identified, leading to a low boreability index and specific energy. A combined Hertz-Weibull function is developed to fit the cutter-rock contact stress distribution obtained in DEM simulations, whereby an improved CSM (Colorado School of Mines) model is proposed by replacing the original monotonic cutting force distribution with this combined Hertz-Weibull model. The proposed model outperforms the original CSM model as demonstrated by a comparison of the estimated cutting forces with those from the tests/simulations. The findings from this work that advance our understanding of TBM cutter performance have important implications for improving the efficiency and reliability of TBM tunnelling in granite.

Generation of monthly VIIRS nighttime lights time-series (1992–2013) images using deep learning (cGAN) technique

Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

Two-Fluid and Discrete Element Modeling of a Parallel Plate Fluidized Bed Heat Exchanger for Concentrating Solar Power

Abstract A novel high-temperature particle solar receiver is developed using a light trapping planar cavity configuration. As particles fall through the cavity, the concentrated solar radiation warms the boundaries of the receiver and in turn heats the particles. Particles flow through the system, forming a fluidized bed at the lower section, leaving the system from the bottom at a constant flowrate. Air is introduced to the system as the fluidizing medium to improve particle heat transfer and mixing. A laboratory scale cavity receiver is built by collaborators at the Colorado School of Mines and their data are used for model validation. In this experimental setup, near IR quartz lamp is used to provide flux to the vertical wall of the heat exchanger. The system is modeled using the discrete element method and a continuum two-fluid method. The computational model matches the experimental system size and the particle size distribution is assumed monodisperse. A new continuum conduction model that accounts for the effects of solid concentration is implemented, and the heat flux boundary condition matches the experimental setup. Radiative heat transfer is estimated using a widely used correlation during the post-processing step to determine an overall heat transfer coefficient. The model is validated against testing data and achieves less than 30% discrepancy and a heat transfer coefficient greater than 1000 W/m2 K.

President’s Column: The Importance of Supporting Academia and Students in Navigating the Challenges and Opportunities in the Industry

Editor’s Note: This is a summary of the May episode of the President’s podcast. We encourage you to listen to the episode to hear the full conversation. In this podcast episode, I am joined by Jennifer Miskimins, department head of petroleum engineering at the Colorado School of Mines, to discuss the importance of supporting universities and students through the challenges and opportunities facing the industry. I chose Jennifer to be part of this episode because of her extensive experience in both academia and the industry. This episode aims to delve into the status of petroleum engineering departments and the importance of students in shaping the industry’s future. I emphasize the collaborative effort required from universities, SPE, and its members to attract the next generation to the field. Our conversation revolves around the importance of addressing the challenges faced by petroleum engineering programs and attracting students to the field. I explain that the SPE Board of Directors recognizes the significance of universities, students, and faculty in the industry, including naming them as core constituencies in SPE’s Strategic Plan. Attracting the next generation is one of the top concerns among SPE members. Young professionals and students play a vital role in driving innovation and addressing future challenges, emphasizing the need for a full pipeline of talent to sustain the industry’s progress. I believe that students are essential for shaping the future of energy and fulfilling humanity’s needs, as they aspire to make a meaningful impact in the world. We need the next technical breakthroughs, most of which will come from the students in university today. They want to feel like they’re making a difference in the world. Jennifer adds, “What I frequently hear from students is yes, they want a long-term career but making a difference is very important. And they can do so. So hopefully, we can get more students coming our direction.” Although enrollment in petroleum engineering programs has fluctuated due to industry downturns and external factors like the COVID-19 pandemic, recent data suggests a modest increase in undergraduate enrollment, indicating a potential upswing. There are challenges such as the industry’s public image, perception of job stability, and declining enrollment in STEM education. Addressing these challenges requires reframing the narrative around the energy “transition” to one of “expansion” and highlighting the long-term career opportunities in the industry. We share insights into how universities are incorporating diverse energy-related topics into their curriculum, offering electives on emerging technologies, and engaging with industry professionals to provide students with real-world insights and opportunities. We also emphasize the role of faculty advisors and industry support in sustaining student chapters of organizations like SPE, which play a crucial role in bridging the gap between academia and industry. We further point out the critical role SPE members play in supporting students and universities through mentorship, financial support, and speaking engagements. Active involvement in student chapters helps bridge the gap between academia and industry.

Data Science Has Exploded Across the Industry Over the Past 75 Years

In 1957, the Journal of Petroleum Technology published an article titled “Application of Large Computers to Reservoir Engineering Problems.” That was the first reference in JPT to what became known as supercomputers. The high-speed computers used in the 1957 article were capable of performing 60 million operations in about 3½ hours. They were proposed to analyze the thorny problem of multiphase flow. “It presently appears that the large computer will be required to investigate multiphase flow and to predict the flow behavior of oil and gas reservoirs considering two- and three-space dimensions.” Now, 67 years later, supercomputer speeds are measured in trillions of operations per second, and investigating multiphase flow is just one of the many uses. This extreme growth in speed has given rise to artificial intelligence (AI) and machine learning (ML), which has found its way into almost every corner of the petroleum industry. The Society of Petroleum Engineers and JPT has kept up with the digital advances in the industry, holding countless conferences, symposia, and other meetings centered on the digital aspects of the industry and recently creating the Data Science and Engineering Analytics (DSEA) technical discipline. In 2019, SPE launched the Data Science and Digital Engineering in Upstream Oil and Gas online publication. Four leaders in the oil and gas digital space shared their views on the current state of data science in the industry and the future of the discipline. Sushma Bhan, currently on the board of directors for Ikon Science, is SPE’s Technical Director for DSEA. Before joining Ikon, she worked for Shell for 32 years, eventually rising to the role of chief data officer for subsurface and wells. “My journey in the oil and gas industry began in 1988, when I joined Shell’s Production Computing Assisted Operations team as a programmer analyst,” Bhan said. “During my time there, I gained valuable exposure to field production operations and developed a deep understanding of real‑time data.” Jim Crompton, who claims he is mostly retired, is an associate professor of petroleum engineering at the Colorado School of Mines, a faculty fellow at the school’s Payne Institute for Public Policy, and director of Reflections Data Consulting. “My academic career started out in exploration geophysics, so my first introduction to engineering analytics came from processing seismic data at Chevron Geophysical Company in Houston,” Crompton said. “I studied earthquake seismology in graduate school, but, when I graduated, I learned the oil and gas industry paid a lot more than the USGS did, so my career goals changed.” Shahab D. Mohaghegh is a professor at West Virginia University and the president of Intelligent Solutions. “Since I started working for the petroleum industry throughout the world using artificial intelligence in 2000, I was able to work with actual data (field measurements) that has been saved by all the companies,” Mohaghegh said. “Working with actual data using AI provided several technologies that have been incredibly fantastic compared to what we have done in the past.” Pushpesh Sharma, the chair of the DSEA Technical Section, holds a PhD degree in chemical engineering from the University of Houston and is senior product manager for Aspen Technology. “Till a few years ago, the focus was on proving the efficacy of data science/machine learning methods for energy use cases,” Sharma said. “However, in recent years, the concerns are around large-scale deployment, maintenance, and the black‑box nature of ML models. Because of that, I started seeing increased focus on the deployment, explainability, and trust of ML models.”

Introduction to this special section: The future of applied geophysics

This issue's special section was inspired by a series of presentations given in 2023 as part of a forum under the same name at the Bay Area Geophysical Society, the Colorado School of Mines, and two special sessions at IMAGE '23. At the heart of the forum is the existential question: How does applied geophysics remain relevant to the rapidly emergent energy transition and its requirement to secure an unprecedented volume of diverse natural resources while steadfastly minimizing the negative environmental impacts of doing so?

Fiber-Optic Strain Measurements Aid Fracture Characterization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3864861, “Geomechanics Modeling of Strain-Based Pressure Estimates: Insights From Distributed Fiber-Optic Strain Measurements,” by Wei Ma and Kan Wu, Texas A&M University, and Ge Jin, Colorado School of Mines. The paper has not been peer reviewed. _ The combination of Rayleigh frequency shift distributed strain sensing (RFS-DSS) and pressure-gauge measurements has been reported recently in field applications. The main objective of the study detailed in the complete paper is to investigate the relationship between strain change and pressure change under various fractured reservoir conditions and provide guidelines for better using this novel strain/pressure relationship to estimate conductive fractures and pressure profiles. Introduction With a spatial resolution of 20 cm and a sensitivity of less than 1 με, RFS-DSS can measure mechanical strain changes along the fiber with higher accuracy and sensitivity than low-frequency distributed acoustic sensing measurements. The field applications of RFS-DSS have improved the understanding of near-well and far-field fracture characteristics and the relationship between stimulation and production in unconventional reservoirs. Although some numerical modeling works have been conducted to study the mechanisms of RFS-DSS data sets, the sensitivity, or influencing factors, of the relationship between strain change and pressure change along the fiber are still unclear. In this work, the authors use a coupled geomechanics and fluid-flow simulator to simulate the strain change and pressure change measured along the producing and monitoring wells during both stable production and shut-in periods. Methodology A 3D multilayer reservoir model with dimensions of 300×400×55.82 m was created using Permian Basin data sets. The reservoir was discretized into 553×129×5 gridblocks. To ensure accurate simulation of field RFS-DSS measurements, the mesh was refined around the fracture and wellbore. The fracture width was set to be the same as the RFS-DSS spatial resolution (0.2 m), and the grid size was set to 5 m except for the refined region. As shown in Fig. 1, the reservoir had 11 perforation clusters along the producing well and the monitoring well was 65 m away from the producing well. A fiber cable was installed on both wells to measure the RFS-DSS data set. The producing well was operated for 240 days before being shut in for 4 days, followed by a 1-day reopening and then continued production for 1 year. The pressure decline was 30–40 psi during the 1-day stable production period. Note that the moment after producing 239 days was taken as the reference time to calculate the strain change during the 1-day production (239–240 days) and the moment after 240 days as the reference time to calculate the strain change during the shut-in period (240–244 days).

Chemical and Biological Engineering at the Colorado School of Mines

Chemical and Biological Engineering at the Colorado School of Mines

Flood Management: Solving Conformance or Sweep Efficiency Problems—Part 4: Solution Performance Analysis

The first three articles in this series have discussed problem identification, problem understanding; solution design, execution, and performance; and benefit methods and techniques that should help in our efforts to improve the solution success rate, but we still have further improvements to make. Part 4 provides an overview of ongoing research and recent technology improvements focused on improving sweep efficiency or conformance solutions taking place in both academia and the service industry within the US. This article does not review efforts taking place inside oil production companies or national oil companies as that work is generally not publicized until completed. Before we start this review, let’s capture some thoughts that were expressed by several department chairs and other leaders in the industry. There is a common feeling that the level of research activity on all conventional oilfield recovery problems has suffered from a change in interest and funding over the past 10 to 15 years. Research on conformance or sweep efficiency problems is a subset of conventional oilfield recovery issues and thus has also suffered from this reduced interest. Instead, a significant rise of unconventional oilfield recovery needs and focus on greenhouse gases (GHG), carbon footprint, and the energy industry transition has shifted the priorities at many universities, governments, and service providers. This shift in efforts has resulted in a significant redistribution of resources that were once focused on conventional oilfield recovery issues. The number and types of conventional oilfield recovery problems has not changed; if anything, they continue to grow, but the addition of unconventional resources and their unique character, along with the push for studies on GHG, carbon footprint, and energy transition without a corresponding increase in funding and manpower has diluted our ability to respond as an industry to these needs. This perspective is offered as a simple observation of conditions that exist in the energy research arena. No judgment is made on the appropriateness of this change or the overall situation. Simply be aware that as an industry we have many challenges to face in the future and some will suffer as others are prioritized. As a result of this and some other reasons, several universities such as Colorado School of Mines, Louisiana State University, Penn State, Stanford University, University of Houston, University of Southern California, The University of Tulsa, and University of Wyoming are not reporting any active or current efforts on conventional asset sweep efficiency or conformance solution research. This does not mean that all interest in these technologies has been lost, only that no current research activities are underway. This is unfortunate, but I believe it is representative of this industry shift. Now, back to the primary focus of this article.

(Invited) Overall Research on Electrode Coating Processes (OREO) - the Role of Imaging and Spectroscopy in Scale-up Fabrication of Low Temperature Water Electrolyzers

With the recent trends in hydrogen technologies, scale-up fabrication of membrane electrode assemblies (MEAs) for the electrochemical systems such as fuel cells and electrolyzers is gaining significant attention. Companies and researchers are focusing on diverse large-scale electrode fabrication processes, such as roll-to-roll and screen-printing. However, optimizing such processes is not trivial, and a number of parameters, including but not limited to catalyst type, solvent, ink mixing, electrode coating and drying, play a role in the quality of the final product. Correlations between the fabrication parameters and resulting electrode microstructure, properties and performance are important to understand, in order to better control the processes. Imaging and spectroscopy techniques play an important role in this understanding, starting from catalysts, inks and all the way to the electrodes. Information about catalyst distribution, composition, and surface chemistry can be correlated to ink characteristics, and finally to electrode structure, component distribution and properties, and their effect on MEA performance can be investigated.In this talk, a collaborative work between four institutions, University of Connecticut, Colorado School of Mines, National Renewable Energy Lab and Fraunhofer ISE, within a project on Overall Research on Electrode Coating Processes (OREO), is discussed, with the focus on low temperature water electrolyzers scale-up fabrication and characterization. The talk will especially focus on the role of advanced microcopy and spectroscopy in the characterization of catalyst powders, inks and electrodes.

Scale Up and Coupling of the MOXIE Solid Oxide Electrolyzer for Both Terrestrial and Space Applications

The Mars Oxygen ISRU Experiment (MOXIE) is a first of its kind demonstration of in-situ resource utilization technology to produce propellant and breathable oxygen from the Mars ambient carbon dioxide. The use of Lunar and Martian resources represents a significant opportunity to reduce the cost of launch from Earth, enabling propellant production for space refueling, and allowing for life support of manned missions to the Lunar and Martian surfaces.Since developing the Solid OXide Electrolysis (SOXE) stacks for the Mars 2020 MOXIE program in 2017, the OxEon team has made significant advancements in scale and capabilities of the SOXE stack technology used in that system. Newer variants have a five-fold larger cell area and a 6.5-fold increase in cells per stack, for a stack scaled 33-times the 0.5% scale of the device in MOXIE. Six of these OxEon mission-scale SOXE stacks will produce 30 tons of propellant oxygen to fuel a Mars Ascent Vehicle (MAV) in the 19-month window between landing an unfueled MAV pre-supply mission and the next launch opportunity for the first crewed Mars Mission, meeting target requirements for a return mission. OxEon has built and demonstrated systems with mission-scale stacks for both Lunar and Martian applications. A system for the production of propellant H2 and O2 from Lunar ice was successfully tested in a cryo-vac chamber at the Colorado School of Mines in 2022. Another mission-scale demonstration system is scheduled to demonstrate production of O2 and methane from Martian H2O and atmospheric CO2 at Jet Propulsion Laboratory in 2022.In addition to SOXE/SOFC systems, the OxEon technology portfolio also consists of a low energy plasma reformer capable of producing sygnas from a variety of hydrocarbons and a modular-scale Fischer-Tropsch reactor for the production of liquid fuels from syngas. These technologies allow OxEon to provide a wide range of terrestrial energy solutions. A DOE Bioenergy Technologies Office (BETO) funded system is currently being fabricated to demonstrate the conversion of both CO2 and CH4 in anaerobic digester gas to liquid transportation fuels. This will be accomplished using an SOEC system to convert steam and CO2 to synthesis gas. OxEon’s plasma reformer will be used to convert methane to synthesis gas, using the plasma to catalyze the reaction of methane with steam and oxygen, supplied in part by the byproduct oxygen from the electrolysis system. The syngas from the plasma reformer and electrolysis systems are then combined to produce liquid fuels in the Fischer-Tropsch (FT) reactor. Figure 1

Electric-thermal energy storage using solid particles as storage media

Jeffrey Gifford is a PhD Candidate in the Advanced Energy Systems program sponsored by National Renewable Energy Laboratory (NREL) and the Colorado School of Mines. He previously earned his M. and BS in Mechanical Engineering from the University of Colorado – Boulder and Purdue University, respectively, and has worked at NREL as a graduate researcher. His research interests include energy storage systems for economy-wide decarbonization and long-duration, particle-based thermal energy storage systems using a multi-method approach, including computational fluid dynamics, dynamic integrated system modeling, and mixed-integer linear programming for design and dispatch optimization. Janna Martinek is a researcher in the Thermal Energy Systems group at the National Renewable Energy Laboratory (NREL). She received her PhD in Chemical Engineering from the University of Colorado – Boulder prior to joining NREL. Her work includes predictive performance modeling and analysis of concentrating solar power (CSP), solar thermochemical and thermal energy storage (TES) systems, computational fluid dynamics modeling, and analysis of dispatch optimization and grid integration of CSP and TES technologies. Xingchao Wang is a joint appointment researcher to the National Renewable Energy Laboratory (NREL). He is a research faculty of the Mechanical Engineering Department at Colorado School of Mines. Dr. Wang received his PhD in Mechanical Engineering from Lehigh University and BE in Thermal Engineering from Tsinghua University. His expertise lies in heat transfer, thermodynamics, and conventional and renewable energy systems designing and modeling. He is working on energy conversion, storage research, and technology development using renewable energy sources. Zhiwen Ma is a senior engineer in the Thermal Systems R&D Group at National Renewable Energy Laboratory (NREL). He received his PhD in Mechanical Engineering from the Georgia Institute of Technology. Zhiwen is leading the research projects on long-duration energy storage using particle-based thermal energy storage, thermal and electrochemical modeling for hydrogen production, and solar fuel processes. He has expertise in computational modeling and experimental testing, renewable solution and system development, component performance, and cost analysis. He has published over 70 peer-reviewed papers and was awarded 12 patents. Jeffrey Gifford is a PhD Candidate in the Advanced Energy Systems program sponsored by National Renewable Energy Laboratory (NREL) and the Colorado School of Mines. He previously earned his M. and BS in Mechanical Engineering from the University of Colorado – Boulder and Purdue University, respectively, and has worked at NREL as a graduate researcher. His research interests include energy storage systems for economy-wide decarbonization and long-duration, particle-based thermal energy storage systems using a multi-method approach, including computational fluid dynamics, dynamic integrated system modeling, and mixed-integer linear programming for design and dispatch optimization. Janna Martinek is a researcher in the Thermal Energy Systems group at the National Renewable Energy Laboratory (NREL). She received her PhD in Chemical Engineering from the University of Colorado – Boulder prior to joining NREL. Her work includes predictive performance modeling and analysis of concentrating solar power (CSP), solar thermochemical and thermal energy storage (TES) systems, computational fluid dynamics modeling, and analysis of dispatch optimization and grid integration of CSP and TES technologies. Xingchao Wang is a joint appointment researcher to the National Renewable Energy Laboratory (NREL). He is a research faculty of the Mechanical Engineering Department at Colorado School of Mines. Dr. Wang received his PhD in Mechanical Engineering from Lehigh University and BE in Thermal Engineering from Tsinghua University. His expertise lies in heat transfer, thermodynamics, and conventional and renewable energy systems designing and modeling. He is working on energy conversion, storage research, and technology development using renewable energy sources. Zhiwen Ma is a senior engineer in the Thermal Systems R&D Group at National Renewable Energy Laboratory (NREL). He received his PhD in Mechanical Engineering from the Georgia Institute of Technology. Zhiwen is leading the research projects on long-duration energy storage using particle-based thermal energy storage, thermal and electrochemical modeling for hydrogen production, and solar fuel processes. He has expertise in computational modeling and experimental testing, renewable solution and system development, component performance, and cost analysis. He has published over 70 peer-reviewed papers and was awarded 12 patents.

Petroleum++: Role of SPE Members and the Industry in the Energy Transition

As many of you know, SPE recently approved its 2023 Strategic Plan. The theme I chose for my presidency is Petroleum ++. As I explained in previous columns, petroleum refers to our continued work in producing oil and natural gas. The first + refers to our role in mitigating climate change concerns, and the second + is about our role in the energy transition and new forms of energy. The industry is already at different stages in this transition, and SPE is working to provide guidance and education to our members through many avenues. I want to talk about how these industry changes are affecting our current and future members and how SPE is helping. We are seeing many oil- and gas-producing and oilfield service companies adjusting to make their businesses sustainable through industry change. Shell’s strategy is “Powering Progress” which names climate change as an urgent challenge and vows it will contribute to a net-zero world. Saudi Aramco is creating value through sustainable business operations saying sustainability factors have become increasingly critical as it aims to balance profitability, environmental protection, and the growth and prosperity of the communities in which it operates. Baker Hughes is “taking energy forward” to make it safer, cleaner, and more efficient for people and the planet, and the newly rebranded SLB is driving energy innovation for a balanced planet. These are just a few of the higher-profile companies making changes in their organizations. Another important change is academia’s response to the energy transition. Globally, universities are expanding curricula for petroleum engineering students. Many are adding research and courses in carbon capture, utilization, and sequestration (CCUS), hydrogen, geothermal, and mineral processing of coal. Some universities are more closely integrating petroleum engineering, geology, and geophysics courses for beginning students. Penn State, for example, is offering new courses on carbon sequestration, doing new research on hydrogen, and recently opened a new center in mineral processing of coal residuals. The King Fahd University of Petroleum and Minerals (KFUPM) has strongly integrated its petroleum engineering bachelor of science program with the geosciences department (geology and geophysics). A new concentration in energy sustainability will be introduced in the fall of 2023, which will address energy transition and decarbonization, CCUS, and geothermal and hydrogen production and storage. The University of Texas at Austin (UT) and the Bureau of Economic Geology recently launched a geothermal research consortium called HotRock that will integrate isolated bodies of science and engineering knowledge to broaden and accelerate the scale up of “geothermal power anywhere.” UT launched a new multidisciplinary research and education initiative, Energy Emissions Modeling and Data Lab, in partnership with Colorado School of Mines and Colorado State University to address the growing need for accurate, timely, and clear accounting of greenhouse gas emissions across global oil and natural gas supply chains. These are just a few examples of how universities are helping to prepare future workers for a more integrated industry.

Building Community Consensus for Scientific Metadata with YAMZ

ABSTRACTThis paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps.

A system dynamics model for assessing the impact of fiscal regimes on mining projects

Mining-dependent economies heavily rely on the minerals sector to extract revenue for socio-economic development. The exploitation of mineral resources in these countries is made possible through local and foreign direct investment with little or no government participation. To extract revenue from the minerals sector different countries design mining fiscal regimes that integrate tax and non-tax elements. The designed regimes must maintain a balance between extracting revenue from the mining sector and leaving mining companies in business. However, achieving this is no easy task. The structures of mining fiscal regimes heavily depend on the models that are used in the design process. The current models (e.g., IMF FARI, and Colorado School of Mines (CSM) models) for evaluating mining fiscal regimes are static because they cannot assess variables as they evolve over the life of the mine project. Therefore, a system dynamics model has been developed to capture the dynamism of mining fiscal regimes by analysing technical and economic variables over time. To this end, the proposed model can be viewed as an extension rather than a replacement of the IMF FARI and CSM models. To illustrate its practical application, the model was applied to a case study to evaluate Zambia's copper taxation reforms based on a hypothetical mine project. Results show that the 2023 regime generates a lower financial and technical impact on a stylized hypothetical mining project when compared to the 2022 regime. A synopsis of results shows that over the 10 years production period, the 2023 regime generates lower profiles of average effective tax rate, effective tax rate, marginal effective tax rate, and Net Present Value (NPV) of government revenue when compared to the 2022 regime. These results imply that the 2023 regime is more attractive in capturing global mobile capital than the 2022 regime. Additionally, the 2023 regime is more attractive than the 2022 regime in terms of marginal flows of investment. However, the 2022 regime has a higher revenue-raising potential for the government than the former. In terms of sterilised reserves the 2022 regime generates a higher profile of values than the 2023 fiscal system (i.e., between 1-11 months and 36-120 months). However, this is with the exception of the 12-35 months where the 2023 regime has higher reserve sterilised values than the 2022 fiscal system.

US Petroleum Engineering Graduation Rates Keep Falling, but Oil Execs Are Not Complaining Yet

The job market is looking up for petroleum engineers graduating this spring. With oil prices up, companies are hiring, which means most of the shrinking number of graduates have already lined up jobs, according to Lloyd Heinze, a professor at Texas Tech University, who does an annual survey of petroleum engineering (PE) programs. At Texas Tech, he said more than 90% of the students already have jobs upon graduation, with most of them going to work for operators. Demand is also strong for graduates at Colorado School of Mines, Penn State University, and Texas A&M University, where most of the seniors have jobs arranged in advance of graduation. Tom Blasingame, a petroleum engineering professor at Texas A&M, and 2021 SPE president, said the career fair held last fall “was very well attended, and I believe our placement percentage will be very high in 2023.” He said 80% of students who graduated from A&M in December had jobs lined up, and he expected they would all have jobs within 2–3 months of graduation. Jennifer Miskimins, a professor who is head of the petroleum engineering department at Mines, agreed that the hiring rate is at that level, with some students getting job offers after completing summer internships. Sanjay Srinivasan, department head at the Energy and Mineral Engineering Department at Penn State, said hiring demand “looks much better this year than last year. However, we are concerned that many of the oil and gas companies that come on recruitment visits seem more interested in talking to graduates from other departments like mechanical and chemical engineering.” Others are seeing the same trend. “The supermajors will take the engineers and train/mold them into whatever they need,” Miskimins said. Blasingame added that independent operators, however, focus on petroleum engineers because they “don’t have extensive training programs like supermajors, so they hire the people who can ‘work on day 1.’ ” Also, Srinivasan said companies are increasingly selective. “I feel that the companies have lean recruitment targets and so they basically shop for the best-fit graduates from multiple schools,” he said. It is a striking turnaround from a few years ago when record numbers of students flooded a weak job market, looking for any oil industry job they could find or looking outside the industry to companies that valued their math and science skills. Still, given the high price of oil, hiring demand that is equal to a shrinking supply feels odd to Heinze and the others. Based on their experience, when oil prices hit $100/bbl, the demand for PE grads and enrollment go up. Something Heinze finds “spooky” this time around is that oil companies are not complaining about the small size of the graduating classes. It feels odd to Heinze because in the past when prices were high and activity picked up, college administrators heard complaints from oil companies such as “How many (seniors) do you have?” and “We need more.” “We are seeing the same, very good placement. However, I have had companies tell me that they aren’t making their hiring goals,” Miskimins said. She is not hearing complaints about the graduation rate “but definitely concerns.” To address the problem, Mines has hired a direct recruiter as part of an effort to attract more earth sciences students, Miskimins said.

Memorial

The geophysical community has lost one of its brightest lights with the passing of Norman Bleistein, university emeritus professor at the Colorado School of Mines (CSM).

Study Explores Potential of Carbon Leakage in Denver-Julesburg Basin Wells

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210256, “Wellbore-Integrity Impact on Carbon Leakage To Ensure Safe Geological Sequestration,” by Yanrui Ning, SPE, and Ali Tura, Colorado School of Mines. The paper has not been peer reviewed. _ In the complete paper, poorly understood leakage potential is researched to quantify the carbon-leakage rate along one legacy well when carbon is injected into a storage formation in the Denver-Julesburg (DJ) Basin. The authors’ results indicate that, for secure carbon sequestration, a good geological storage site should be kept away from wells that have a surface casing depth of 476 ft or shallower and that were drilled before 1994. The effective well permeability highly affects leakage rate, suggesting the importance of estimating the most-representative value or range for this parameter. Introduction To estimate risks associated with geological carbon storage, the National Risk Assessment Partnership (NRAP) Open-Integrated Assessment Model (IAM) tool used in this study presents a modeling approach that can couple efficient reservoir and wellbore components with low computational cost. Two wellbore model components, the open-wellbore model and the multisegmented-wellbore model, were studied to estimate the leakage rate during and after carbon-injection operations. The effective permeability of the disturbed zone outside the well casing is an important parameter in leakage models. However, estimating a reasonable range for the effective well permeability of the disturbed zone remains a challenge. For this study, a more than 1-sq-mile section in the Wattenberg Field of the DJ Basin was used. The goal was to quantify the leakage rate when carbon dioxide (CO2) escapes through a well 1,100 ft from the CO2 injector. First, a reservoir simulation model was built to reflect the pressure and saturation changes during the 30-year injection period and the 100-year postinjection period. This model, based on an ongoing field study, included the CO2 injector, the legacy well that is the potential pathway of CO2 leakage, and the carbon storage formation. Study Area Carbon Storage Potential in Colorado. The top-ranked CO2 emission facilities in Colorado are above the DJ Basin. According to carbon-emission data, 13 facilities in Colorado, including five coal plants, emitted more than 0.7 million tons of CO2 in 2020. All the coal plants are scheduled to be retired by 2034. All eight CO2 emission facilities are in the DJ Basin, with half of those in the Wattenberg Field. This makes the DJ Basin, especially the Wattenberg Field, a good target for carbon storage. The stacked formations of oil and gas reservoirs and saline aquifers in the DJ Basin are potential CO2-storage formations. The current drilling activities with multistage fracture stimulation of horizontal wells target the oil- and gas-producing zones of Niobrara through Greenhorn. These oil and gas producers penetrate the shallower Pierre sandstone formation. This study quantifies the carbon-leakage risk when the Pierre sandstone is the carbon-storage formation and when one nearby Niobrara legacy well penetrates the storage formation and may behave as a potential leakage pathway.