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A personal overview of the Calgary 2000 Technical Program

Repsol YPF, The Woodlands, Texas, U.S., and Member of the Editorial Board of TLE

The first 20% of the full text of this article appears below.

More than 7000 delegates visited close to 275 exhibitions and attended roughly 750 papers and posters. The general attitude of the convention was, in my opinion, optimistic. Most of the contractors and vendors are starting to see an increase in activity and with the announced increases in oil companies' exploration and production budgets for 2001 and if the prices of oil and gas continue at high levels, 2001 could be a very active year for the geophysical industry.

What follows is based on my experiences at the convention as well as discussions with colleagues. Given the magnitude of the event, it wouldn't be possible to describe every technical idea presented or every new product exhibited; rather, I summarize the aspects of the Technical Program that I found interesting from my biased point of view as an interpreter and a geophysicist who is trying to keep up with modern technology and understand where the future is headed.

	Reservoir characterization and neural networks

 
There seemed to be an increased focus on advanced approaches in reservoir characterization. There were eight sessions specifically on reservoir characterization and many more related presentations in the Interpretation and Rock Properties and Borehole session topics.

The use of neural networks has increased dramatically in interpreting petrophysical and reservoir parameters. New techniques included utilizing neural networks in advanced methods of prospecting and mapping which hold promise to improve upon our present interpretation methods. One presentation discussed using neural networks and multi-regression analysis of nonlinear attributes, like velocity, coherency, and impedance to define sand trends. In a mid-continent gas field, an artificial neural network trained by poststack seismic attributes was able to classify a seismic volume for lithology, porosity, and thickness within the targeted sands with an acceptable degree of confidence. By use of neural networks, 3-D gamma ray and porosity volumes were generated from . . . [Full Text of this Article]