Date(s) - 08/10/2017
8:00 am - 2:30 pm
Rock Bottom Brewery
Categories No Categories
The Latest Developments in Machine Learning and Multi-Attribute Seismic Analysis with selected Case Histories
with Guest Speaker Michael A. Dunn
Sr. Vice President of Business Development at Geophysical Insights.
August 10, 2017
Over the last few years, because of the increase in low cost computer power, individuals and companies have stepped up investigations into the use of machine learning in many areas of E&P. Within the geoscience community, the emphasis has been in the areas of reservoir characterization, seismic data processing and to a lesser extent “bread and butter” interpretation. The benefits of using machine learning (whether supervised or unsupervised) has been demonstrated throughout the literature and yet the technology is still not a standard workflow for most seismic interpreters. This lack of uptake can be attributed to a number of factors including: a lack of “easy to use” software tools, clear and well defined case histories and training. Fortunately, all these factors are being mitigated as the technology matures.
Rather than looking at machine learning as an adjunct to the traditional interpretation methodology, machine learning techniques should be the first step in the interpretation workflow. By using statistical tools such as Principle Component Analysis (PCA) and Self Organizing Maps (SOM) a multi-attribute 3D seismic volume can be “classified”. The PCA reduces a large set of seismic attributes both instantaneous and geometric, to those that are the most meaningful. The output of the PCA serves as the input to the SOM, a form of unsupervised neural network, which when combined with a 2D color map facilitates the identification of clustering within the data volume. When the correct “recipe” is selected the clustered or classified volume allows the interpreter to view and separate geological and geophysical features that are not observable in traditional seismic amplitude volumes. Seismic facies, detailed stratigraphy, direct hydrocarbon indicators, faulting trends, and thin beds are all features that can be enhanced by using a classified volume. To demonstrate these positive results, in various geologic regimes and plays, a major portion of the presentation will be devoted to recent case histories.
Bio – Michael A. Dunn
In January 2017 Michael Dunn joined Geophysical Insights as Sr. Vice President of Business Development. In this role, Mike assumes the leadership responsibility for the company’s global business development. As part of his new role Mike will continue to be involved in Geophysical Insight’s technology and software development.
Mike brings a wealth of experience in the oil and gas industry to the position. He started his career as a geophysicist at Shell Oil Company where he held technical and managerial roles in both operations and research. In the late 1990’s Mike left Shell and founded, with other partners, Geokinetics, a full service geophysical company. Under his leadership in various executive positions Geokinetics grew significantly culminating in a public offering on the American Exchange in 2007. Next, Mike founded Terra Geoscience, a geoscience consulting firm, that was exclusively focused on Mexico where several significant contracts were executed.
In 2009 Mike became Vice President of Technology for Woodside (USA) where he directed the company’s technology strategy to increase valuation of their exploration portfolio and production assets. He continued in this role until 2013 when Woodside consolidating its operations to Australia. In that same year, he joined Halliburton where he served as Sr. Director of Geology, Geophysics and Reservoir Engineering. Under his leadership, the portfolio was significantly augmented and redirected.
Mike has a Bachelor’s degree in Geology from Rutgers University and a Master’s degree in Geophysics from the University of Chicago. He attended Shell executive training programs at MIT’s Sloan School of Business Management and the University of Houston’s Bauer School of Business.