Date of Award

1990

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

John M. Tyler

Second Advisor

George F. Hart

Abstract

A parallel processing framework for the investigation of three dimensional subsurface geological problems is developed. The Floating Point Systems T-20 hypercube parallel computer system is used in the automatic correlation of petrophysical well logs. Parallel processing issues such as processor management, interprocessor communications management, memory management and algorithm development are discussed. Techniques are discussed to use spontaneous potential petrophysical logs to segregate subsurface strata into sand and shale segments. These segments are then used to generate sand/shale ratio logs for use in an automatic subsurface stratigraphic analysis system on a parallel machine. Phase one prepares petrophysical logs for stratigraphic analysis. The petrophysical logs are filtered and cleaned to remove any as salinity effects and improper tool responses. The spontaneous potential log is used to segregate each log into individual sand and shale beds and use these beds to create sand and shale databases which are used to create sand/shale ratio logs for later analysis. Phase two creates and solves a two dimensional matching probability matrix. The theoretical foundation is developed and several implementation issues are discussed. These issues include: (1) how to select data points, (2) how to compare data values from two petrophysical logs, (3) how to view the data, and (4) how to find the optimal set of strata observing several constraints imposed by the nature of the subsurface. Phase three developments parallel processing techniques to handle a multi-dimensional matching probability matrix. Each petrophysical log is treated as an axis of a multi-dimensional matrix with all points from one log being compared with all points from all other logs. A theoretical foundation is developed followed by implementation details. The data storage space required for this study was in excess of 10$\sp{56}$ bytes.

Pages

466

DOI

10.31390/gradschool_disstheses.4982

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