John Castagna

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John Castagna
Castagna John DL webimg 180x226px.jpeg
Latest company University of Houston
BSc Geology
MSc Geochemistry
PhD Exploration Geophysics
BSc university Brooklyn College
MSc university Brooklyn College
PhD university University of Texas at Austin

John P. Castagna is a Professor of Geophysics and the Sheriff Chair of the Department of Earth and Atmospheric Science at the University of Houston [1]. He was awarded the 2005 Reginald Fessenden Award, along with Matthew L. Greenberg, for their work in shear-wave velocity estimation in porous rocks. AVO modeling and its successful application to exploration programs are directly related to the timely publication of the article “Shear-wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification, and applications” in Geophysical Prospecting. [2]. The Greenberg and Castagna technique has withstood years of close scrutiny by being compared to both wireline and laboratory measurements. In fact, it is not uncommon to utilize this shear-wave estimation technique even when in-situ S-wave logs are available. Their peers rank this work with the most important contributions to the field.

Biography Citation for the Reginald Fessenden Award 2005

Contributed by Robert W. Siegfried II

When I reported for my first day of work in the oil and gas industry, at ARCO’s research lab in Plano, Texas, I was advised that a grad student would be arriving the following day to work under my supervision for the summer. Feeling somewhat unqualified to provide such supervision, I was very relieved when John arrived the next day and it was clear that direction from me was not among his requirements. John has been forging his own path ever since, much to the benefit of our profession and our industry.

John was introduced to geophysics by Peter Bell, who arranged for the promising kid from Brooklyn to visit the Carnegie Institute of Geophysics a few times during his undergraduate studies at Brooklyn College. Showing remarkable wisdom, John chose to pursue graduate studies at the University of Texas under the tutelage of Milo Backus, where he obtained a thorough grounding in exploration geophysics and signal processing. Perhaps more importantly, John reports that Professor Backus “infected me with his love for geophysics and showed me how to find simplicity in apparent complexity.” Finding simplicity in apparent complexity has been a hallmark of John’s contributions to our field.

That initial summer job at ARCO evolved into a 15-year career. During the early part of John’s ARCO days, the logging and rock physics research activities were all part of a research effort aimed toward finding ways to use laboratory and log data to improve the quantitative interpretation of seismic data. John realized the promise of the ability to predict seismic velocities and worked with Mike Batzle and Ray Eastwood to develop systematic relationships between compressional and shear velocities in clastic silicates. The fact that it might be possible to distill the bewildering array of measurements and models relating velocities to rock composition, structure, and fluid saturation into a few usable relationships represents a significant (and successful) application of the desire to find simplicity in apparent complexity. The fact that such relationships might prove valuable represents an insight that did not seem intuitively obvious at the time.

The work for which John and Matt Greenberg are being recognized with the Reginald Fessenden Award represents an effort to provide a solid experimental and theoretical foundation for the prediction of shear-wave velocities in porous sedimentary rocks. While earlier work may have pointed the way, the Castagna and Greenberg approach provides the basis through which shear velocities can be predicted with sufficient confidence to be used in the modeling and interpretation of AVO results.

After his technical contributions were recognized, John was given the opportunity to try his hand at research management in Plano. He eventually proved too smart for that opportunity, and wangled an assignment in an exploration group in Houston, where he obtained some real experience with the application of the methods that he had been developing. Armed with enough exploration experience to be dangerous, he proceeded to positions at HARC, the University of Oklahoma, and the University of Houston, some of which were held concurrently. He currently serves on the geophysics faculty at the University of Houston. John founded Fusion Geophysical in 2000 to provide a vehicle for the development and commercialization of advanced interpretation technologies. Through his work at Fusion and the University of Houston, John continues to apply the most advanced geophysical thinking to the solution of real-world seismic interpretation problems. I am very pleased to see him recognized through this important award, and extend my heartiest congratulations.

2023 AAPG/SEG Distinguished Lecture

Beyond physics in geophysics

Physics is an essential component of geophysics, but there is much that physics cannot know or address. For example, physics alone cannot ascertain that an inverted low-seismic impedance is indicative of a coal layer in one stratigraphic interval and the same inverted impedance represents an organic shale reservoir in another. Such geological non-uniqueness often results in the false conclusion from physics that such distinctions from seismic data are not possible. Applying physics to determine what is not possible disregards what information outside of physics can achieve. Seismology only understands relationships controlled by the wave equation and is thus incapable of predicting geological correlations between parameters and seismic attributes that are not governed by the physics of wave propagation. Generally, the more geological information that can be properly incorporated into a geophysical prediction, the better.

For the past quarter-century, explorationists have successfully, empirically used seismic multi-attribute regression analysis and neural networks to make predictions that physics alone cannot achieve. More recently, deeper neural networks are being employed to perform a variety of geophysical functions. This machine learning has great promise but can also be readily misapplied and abused when used to directly make predictions, especially given the usual paucity of training data.

An alternative approach is to use machine learning to uncover relationships that may not have been foreseen because they were not readily apparent or addressed using physics alone. Once these relationships are found and understood geologically and geophysically, real — rather than artificial — intelligence can be used to predict rock and fluid properties from seismic data. For example, a simple-minded analysis of reflectivity tells us that changing the impedance contrast across an interface, or for a layer, changes the reflection amplitude of the reflected waveform but not the phase of that event. Thus, physics is readily misapplied to conclude that phase cannot be used to predict rock properties. Yet, multi-attribute analysis case studies tell us that phase is often a powerful attribute for predicting rock properties. Once artificial intelligence tells us that phase is indeed useful in this regard, it allows us to understand why the simple-minded conclusion telling us what NOT to do was wrong. Similarly, multi-attribute analysis tells us that using bandwidth extended seismic data can improve rock properties predictions even though the correlation of such data to synthetic seismograms often decreases with increasing bandwidth. Poorer correlations for bandwidth extended data have been used by those imprisoned by simple physics to conclude that bandwidth extension is not possible.

Understanding when and why bandwidth extension can be useful despite such decreases in correlation falls into the realms of information theory and depositional systems in addition to geophysics. As a final example, seismic inversion theory tells us that the amplitude spectrum of the seismic wavelet must be known in order to model reflection seismic data, and thus must be known to invert that data. This apparently incontrovertible fact is very unfortunate since the temporally and spatially varying seismic wavelet spectrum is very poorly known and limits the accuracy of seismic inversion. However, using machine learning to replace the functionality of seismic inversion and inspecting the neural network weights can provide insight into what information is necessary and most robust to successfully accomplish the same task as traditional model-based seismic inversion. Such guidance can then be incorporated into a physics honoring seismic inversion scheme that works without explicit knowledge of the seismic wavelet. This is readily demonstrated with synthetic seismic data.


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