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GeoSeminar: Formation Evaluation and the Petrophysics of Carbon Storage

Join TU’s Geosciences Department to learn more about formation evaluation and the Petrophysics of Carbon Storage. The use of down-hole wireline logging measurements to characterize carbon storage projects has many similarities to the requirements for hydrocarbon-bearing reservoirs. The most important features of any carbon storage project that satisfy operational plus regulatory design include knowledge of storage capacity, injectivity and containment, which is very similar to the major components of a petroleum system that is defined by reservoir volume, hydraulic connectivity and sealing capacity. Wireline logs can provide information on porosity that define storage capacity and rock mechanical properties that define sealing capacity for a proposed reservoir. Well tests and other dynamic measurements are used to determine maximum pressures for CO2 injectivity, though pore-size information from NMR logs provides a rapid evaluation tool for estimating formation permeability. The types of CO2 storage sites depend largely on the state of the CO2 to be stored, whether in supercritical or dissolved in saline waters, which in turn affects wireline responses for any in-situ monitoring strategies. Carbon storage as a mineral precipitate is gaining adherents in the CCUS community, especially when stored in basalt. Basalt storage evaluation depends in accurate porosity measurements in a very low porosity rock along with higher values in altered basalt layers. Much of the storage is located in fractures and in small pores found in altered or weathered basalt. Permeability in basalt is influenced primarily by a fracture network, characterization of which is done through analysis of image logs. Creation of fractures in basalt is a function of rock strength, which is interpreted via acoustic logging methods.

Brief Bio:
James Howard is currently a Research Professor in Geosciences at the University of Tulsa and a technical consultant to a geochemistry group at Columbia University on carbon storage in basalts. He is also a technical advisor to DigiM Solution, a software company that uses AI-powered image processing and analysis tools. Previously he was a Senior Research Fellow at ConocoPhillips’ subsurface laboratory in Bartlesville where he established and directed the Pore-Scale Characterization group. Trained as a clay mineralogist / geochemist, his career meandered from the sedimentology of shales, to logging tool design and interpretation, petrophysics with emphasis on NMR technology, advanced core analysis methods including multi-phase flow experiments at reservoir conditions, production scenarios for natural gas hydrates based on CH4-CO2 exchange, and finally back to measuring dynamic properties in very-low permeability shales. Getting involved with carbon storage issues is a nice way to spend one’s retirement.

Title: Formation Evaluation and the Petrophysics of Carbon Storage
Dr. James Howard, Research Associate, Department of Geosciences, The University of Tulsa
james.jennings.howard@gmail.com
Wednesday April 10, 2024 @ 12pm KEP 3005

GeoSeminar: Drinking Water Treatment in Tulsa

Water is the foundation of civilization and societies. The Tulsa community is invited to join TU’s Department of Geoscience to learn all about Tulsa’s drinking water supply and treatment from Hua Jiang, Ph.D., P.E., Senior Engineer with the City of Tulsa’s Water and Sewer Department.

Water is essential to olivesife and fuels the economy. We, as a human race, have come far from the hunter-and-gatherer age. We don’t typically scoop up the water from a stream to quench our thirst anymore. Ever wonder in modern industrialized countries, where and how we normally get our potable water from? Is our tap water safe to drink? This talk will offer a brief overview of drinking water sources, treatment processes, and safeguards.
The City of Tulsa (City) is a regional water supplier and supplies drinking water to metropolitan Tulsa and surrounding communities. The City owns and operates two large drinking water treatment plants and produces up to 220 million gallons of water per day. The first plant, the Mohawk Water Treatment Plant, was built in 1929 and treated the water from Lake Spavinaw which is about 60 miles away. The lake water is transported to the plant through man-made pipelines, under gravity, for most of the year. The second plant, AB Jewell Water Treatment Plant was built in early 1970. It treats the water from Oologah Lake, which is about 30 miles away. The treated water from both plants is pumped to the City through a network of approximately 2000 miles of water mains.

Brief Bio:
Dr. Jiang is currently a Senior Engineer with the City of Tulsa’s Water and Sewer Department. In his capacity, he provides technical support for Tulsa’s drinking water treatment and supply. He has led some major initiatives and projects during his tenures, such as chloramine conversion and taste & odor control. He obtained his Ph.D. degree in Civil Engineering from the University of Missouri – Rolla in 2006, and earned MS and BS degrees from Nanjing University and Nanjing University of Technology, in Nanjing, China, in 2001 and 1998, respectively. Before he joined the City, he worked for an engineering consulting firm in Kansas City where he worked on some very interesting projects, such as the first advanced wastewater reuse plant in Australia and the world’s largest ozonation project for drinking water purification in Texas.

GeoSeminar: Building subsurface models with AI

TU community join the Department of Geosciences for a GeoSeminar over building subsurface models with AI.

A realistic model that delineates the structure, stratigraphy, and rock properties plays a pivotal role in understanding the Earth’s subsurface, and is essential to natural resource exploration, carbon storage, and civil engineering. Traditionally, building such models requires extensive human interaction with multiple data modalities. For example, to build a structural model, one needs to interpret multiple horizons and faults that define the key structures, which can be time-consuming even for experienced seismic interpreters.
We attempt to automate and accelerate the subsurface model building workflow with artificial intelligence (AI), specifically, with deep learning. We use deep learning models in many key steps of the workflow, including seismic and well log data quality check and conditioning, structural and stratigraphic interpretation, generation of attributes, as well as predicting rock properties. We will see the value of AI in building subsurface models with greatly reduced turn around time, while also discussing some lessons learned along the journey.

Brief Bio:
Tao Zhao is the data science manager for interpretation at SLB. Tao joined SLB in 2019 as a senior data scientist, developing deep learning applications for seismic processing and imaging. From 2017 to 2019, Tao was a research geophysicist at Geophysical Insights. Tao has PhD and MS degrees in geophysics from the University of Oklahoma and the University of Tulsa, and BE degree in exploration geophysics from China University of Petroleum (East China). Tao received the J. Clarence Karcher Award from the Society of Exploration Geophysicists (SEG) in 2023, and the best paper award from the 2024 SEG-AAPG IMAGE annual meeting.

GeoSeminar

TU students and faculty join Geosciences for a talk at GeoSeminar with Abatan, Ph.D., to learn more about machine learning algorithms.

 

The title: Data Science Application for Geosciences: Insights from Marcellus Shale Oil & Gas Asset Development and Allegheny Sandstones Paleo-fluvial Channel Analogue, NE United States.

Abstract:
Machine learning algorithms have become increasingly popular in geoscience applications, K-means clustering has emerged as a powerful tool for characterizing subsurface rock types using well log data. Geological exploration often involves the acquisition of vast amounts of well log data from drilling operations, which can be challenging to interpret manually. K-means clustering offers a data-driven approach to group similar rock types based on various log measurements, thereby simplifying the classification process. By partitioning the data into distinct clusters, geologists can gain valuable insights into subsurface lithology and better understand the geological composition of the Earth’s crust. This method not only streamlines the identification of rock types but also aids in making informed decisions for resource exploration and extraction, contributing to more efficient and sustainable practices in the field of geoscience.
In addition to rock type characterization, machine learning techniques such as Support Vector Machine (SVM) regression have found applications in predicting paleo-channel depth using cross-set thickness data. Paleo-channel depth prediction is crucial for understanding ancient river systems, which can provide insights into past environmental conditions and depositional processes. SVM regression, with its ability to model complex relationships in data, is well-suited for this task. By training on cross-set thickness data and associated geological attributes, SVM regression models can accurately estimate the depth of paleo-channels, shedding light on the history of river systems and aiding in geological reconstructions.

Brief Bio:
I work with BP America as a data scientist in the Innovation and Engineering (I&E) Group. I completed my Ph.D. in Sedimentology and Stratigraphy at West Virginia University in January 2020. My research focused on process-based analysis of fluvial stratigraphic record. I got my masters degree in Geosciences from the University of Tulsa in August 2013, where I focused on facies analysis of paleo-fluvial channels. I currently use statistics and machine learning to optimize production on BP assets in the Gulf of Mexico.

GeoSeminar

TU students and faculty join Geoscience in hearing from Dr. Steve Roche at the GeoSeminar Title: ‘Monitoring Seismicity in Guatemala – An SEG Geoscientists Without Borders (GWB) Project’

Read more here:

Approximately 1,502,000 people live within the Zacapa, Chiquimula, El Progresso, Jalapa and Izabal Departments in Eastern Guatemala. Residential and commercial structures are rarely constructed with respect to earthquake code, representing a significant potential for loss of life and damage. Additionally, approximately 5,000,000 people live in the urban area of Guatemala City, capital of Guatemala. A magnitude 7.6 earthquake in 1976 in Guatemala resulted in ~23,000 fatalities. Establishing a seismicity monitoring array will provide real data to characterize the seismicity risk in this region, compile a quantitative earthquake event catalogue, and allow the development of an “earthquake early warning system” (EEWS) in the case of a high magnitude earthquake.
The foundation of this project will be the collaboration between project participants to increase community resilience in the event of a catastrophic earthquake. We combine the strengths, and seek to strengthen, the first responder (regional fire fighters and EMT groups), the Guatemalan Institution (INSIVUMEH) responsible for monitoring earthquake hazards, The University of Tulsa, Universidad San Carlos de Guatemala City, and USA Universities. Societal goals include community education in the geosciences, greater awareness of geohazards and ideally a reduction in disaster response time.

The above partnerships all work together towards the project goals of…
• Installing/upgrading a seismic monitoring array in Guatemala
• Collaborating with regional firefighting/EMT infrastructure (reduce disaster response time)
• Collaborating with Guatemala INSIVUMEH (increase resolution of existing array)
• Ascertaining the seismicity characteristics along the Polochic/Motagua fault system
• Providing a research network for developing an earthquake early warning (EEWS) system
• Engaging with Guatemalan Universities and High School STEM students for geoscience education, community awareness, disaster preparedness and community resilience

From May 2022 to October 2023, we deployed twenty-one 3C RaspberryShake seismometers in Guatemala, fully integrated into the INSIVUMEH earthquake monitoring network. Global, regional and local events have been observed, including one local earthquake swarm with over 600 events. In that instance, two RaspberryShake sensors were redeployed on short notice to provide small-offset observations of the swarm.

Brief Bio:

Dr. Steven L. Roche received his B.Sc. in Geophysics from the University of California, Riverside (1978) and PhD Geophysics from the Colorado School of Mines (1997). Research interests include time-lapse multicomponent seismology with applications to subsurface porous rock systems. Steve’s career in seismic imaging, hydrocarbon exploration and induced seismicity spanned 45 years. In 2017, he joined the Geoscience faculty at The University of Tulsa, teaching graduate and undergraduate courses in petroleum seismology and near-surface geophysics. Steve is currently a Research Professor at TU, Adjunct Professor at OSU and geophysical consultant for hydrocarbon energy and CCUS clients.