Thesis (Ph. D.)--University of Chicago, 1922. / "Private edition, distributed by the University of Chicago libraries, Chicago, Illinois." "Reprinted from Missouri Bureau of geology and mines, vol. XVII, second series, chap. IV." Bilbliography, by E.B. Branson": p. 269-275. Also available on the Internet.

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Thesis (M.S.)--University of Wisconsin, 1957. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 23-24).

Digitized by Kansas State University Libraries

Digitized by Kansas State University Libraries

Call number: LD2668 .T4 1951 D37 / Master of Science

Master of Science / Department of Geology / Matt Totten / The Mississippian-aged St. Louis Limestone has been a major producer of oil, and natural gas for years in Kearny County, Kansas. Since 1966 two major fields in the County, the Lakin, and Lakin South fields, have produced over 4,405,800 bbls of oil. The St. Louis can be subdivided into six different depositional facies, all with varying lithologies and porosities. Only one of these facies is productive, and the challenge of exploration in this area is the prediction of the productive facies distribution. A previous study by Martin (2015) used a neural network model using well log data, calibrated with established facies distributed within a cored well, to predict the presence of these facies in adjacent wells without core. It was assumed that the model’s prediction accuracy would be strongest near the cored wells, with increasing inaccuracy as you move further from the cored wells used for the neural network model. The aim of this study was to investigate the accuracy of the neural network model predictions. Additionally, is the greater accuracy closest to the cored wells used to calibrate the model, with a corresponding decrease in predictive accuracy as you move further away? Most importantly, how well did the model predict the primary producing unit (porous ooid grainstone) within the St. Louis Limestone? The results showed that the neural network was not completely reliable in predicting total facies distribution. This can be attributed to many different inefficiencies in the data, including different resolutions between cuttings data and well logs, limited well cuttings available, and missing cuttings from the wells that were observed. Relating the neural network predictions to actual well productivity validates the neural network’s ability to predict the producing facies. There are also instances of the productive facies being present when not predicted. This is likely a function of different facies thickness in these wells from the cored wells used to calibrate the model, rather than distance from the cored well.

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