Watch videos of Presentations from SEG 2017
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning
Seismic interpretation and machine learning by Rocky Roden and Deborah Sacrey, GeoExPro, December 2016


Paradigm, Petrel, Paradise - Neural Networks

Most people associate neural networks, big data and big number crunching as parts of a single paradigm for access to web information. Articulate a query and wait for a list of answers. But in the oil and gas exploration and reservoir replacement business - particularly at this time - we “must” place neural networks and big data tools in the hands of seismic interpreters. 

Seismic interpreters are accustomed to working on interactive workstations, not using web-based queries. We suggest this “must” not because seismic interpreters are a narrow-minded bunch who are unwilling to work with newer web-styled tools. No – there are strong technical reasons which will allow us to dig deeper into our seismic data. The key is not the platform, be it an interactive workstation or the web. The real reason is that we simply do not and cannot understand much about multi-attribute seismic data with conventional seismic interpretation methods – we don’t know the semantics of the words in these data. Instead, we must let neural networks fly through the seismic data unattended to discover whatever properties they might discover. In other words, multi-attribute seismic data ain’t English. We don’t know the language yet. Today, we are starting to use web-style tools to find things in seismic data in just the same way they search through web pages looking for word patterns. 

Someday integration of machine learning and seismic interpretation will evolve into a practice no longer considered a novel but a mundane operation. Then, articulation of queries like, “In this 3D survey, are there any geobodies with statistical properties of Miocene fluvial channel sands?” will be practical. In fact, a large multinational oil company with a world-wide petabyte seismic library might ask a query, “In the eastern Gulf of Mexico, do we have any 3D surveys with geobodies that might be Miocene fluvial channel sands?” 

Queries like this will take lots of number crunching – recognizing and indexing geobodies in 3D surveys and all this will be done long before queries are made – but the web paradigm of today applies directly to the seismic interpretation of tomorrow. In terms of technology crossover, it’s easier than falling off a log. Just time, people and money...We happen to be a little ahead of the pack. 

We have been told that at a presentation at a major oil company a particular slide drew much attention.  On that slide was drawn two curves - curves that must be part of some internal assessment.  One curve was total volume of annual digital seismic data acquired and processed and the second curve was the volume of these data which has been actually inspected. The two curves diverged and they were concerned. They realize that the solution to this kind of problem is addressed by Big Data and therefore, presentations have been made by HP, IBM and now us, Geophysical Insights.  

The beauty of web information access today is that it is non-confrontational. For a well-posed question, there are often many potential answers. These are ranked in decreasing order of importance and suspected relevance.  We’ll be able to do the same with seismic data.

So what we are building in Paradise® today is a workbench for practitioners. The process of neural network analysis is not run once to deliver some magic answer.  It is run many times with each run under the careful eye of an interpreter.  And our Geophysical Insights geoscientists say that the most challenging part of learning to use this new technology is to appreciate the results. The more seasoned the interpreter => the higher the likelihood that the results of machine learning will have some meaning in the eyes of the interpreter. These new tools are part of a new kind of seismic interpretation where fresh eyes bring new insight. Sometimes significant results are not obvious but often the obvious oil & gas prospects have been evaluated. Subtle but significant results are OK too but if and only if the drill proves out a promising prediction.

Dr. Tom Smith received a BS and MS degree in Geology from Iowa State University. His graduate research focused on a shallow refraction investigation of the Manson astrobleme. In 1971, he joined Chevron Geophysical as a processing geophysicist but resigned in 1980 to complete his doctoral studies in 3D modeling and migration at the Seismic Acoustics Lab at the University of Houston. Upon graduation with the Ph.D. in Geophysics in 1981, he started a geophysical consulting practice and taught seminars in seismic interpretation, seismic acquisition and seismic processing. Dr. Smith founded Seismic Micro-Technology in 1984 to develop PC software to support training workshops which subsequently and there led to the development of the KINGDOM software suite for seismic interpretation.
The Society of Exploration Geologists (SEG) recognized Dr. Smith’s work with the SEG Enterprise Award in 2000, and in 2010, the Geophysical Society of Houston (GSH) awarded him an Honorary Membership. Iowa State University has recognized Dr. Smith throughout his career with the Distinguished Alumnus Lecturer Award in 1996, the Citation of Merit for National and International Recognition in 2002, and the highest alumni honor in 2015, the Distinguished Alumni Award.   

 In 2009, Dr. Smith founded Geophysical Insights, where he leads a team of geophysicists and computer scientists in developing advanced technologies for fundamental geophysical problems.  Following over 3 years of development, Geophysical Insights launched the Paradise® multi-attribute analysis software, which uses machine learning and pattern recognition to extract greater information from seismic data.  Dr. Smith has been a member of the SEG since 1967 and is also a member of the SEG, GSH, HGS, EAGE, SIPES, AAPG, GSH, Sigma XI, SSA and AGU. Dr. Smith served as Chairman of the SEG Foundation from 2010 to 2013.  On January 25, 2016, he was recognized the by the Houston Geological Society (HGS) as a geophysicist who has made significant contributions to the field of geology.