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Lithology identification technology using BP neural network based on XRF

Wang Qingshan, Zhang Xiongjie, Tang Bin, Ma Yingjie, Xing Jisheng, Liu Longfeng

Acta Geophysica

2021

Vol. 69, no. 6

2231--2240

The element content obtained by X-ray fluorescence (XRF) mud-logging is mainly used to determine mineral content and identify lithology. This work has been developed to identify dolomite, granitic gneiss, granite, limestone, trachyte, and rhyolite from two wells in Nei Mongol of China using back propagation neural network (BPNN) model based on the element content of drill cuttings by XRF analysis. Neural network evaluation system was constructed for objective performance judgment based on Accuracy, Kappa, Recall and training speed, and BPNN for lithology identification was established and optimized by limiting the number of nodes in the hidden layer to a small range. Meanwhile, six basic elements that can be used for fuzzy identification were determined by cross plot and four sensitive elements were proposed based on the existing research, both of which were combined to establish sixteen test schemes. A large number of tests are performed to explore the best element combination, and the result of experiments indicate that the improved combination has obvious advantages in identification performance and training speed. The author’s pioneer work has contributed to the neural network evaluation system for lithology identification and the optimization of input elements based on BPNN.

Ocena składu mineralnego oraz parametrów złożowych skał na podstawie korelacyjnych wykresów krzyżowych

Bała M., Jarzyna J.

Geologia / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

1998

T. 24, z. 2

151-160

W pracy zaprezentowano metodę interpretacji profilowań geofizyki otworowej przy wykorzystaniu korelacyjnych wykresów krzyżowych. Pozwalają one na ocenę składu litologicznego oraz zidentyfikowanie minerałów ilastych występujących w badanych skałach. Idea wykresów krzyżowych opiera się na różnym reagowaniu profilowań geofizycznych, zwłaszcza PN, PGG, PA, na litologię (skład mineralny), porowatość skały, nasycenie wodą czy węglowodorami (ropa, gaz)

In the paper a construction of cross-plots as a method of interpretation in well logging is presented. The authors focused in cross-plots for lithology classification and for identification of clay minerals in investigated rocks. Detailed analysis of the results of spectral gamma ray is the base for distinguishing clay minerals between groups of illite, kaolinite, montmorillonite, micas and glauconite. Spectral litho-density log enables spectacular distinguishing between two minerals: calcite and dolomite, which have the photoelectric absorption index Pe quite different. The examples of interpretation basing on cross-plots constructed in Zechstein rocks and Carboniferous formation in Sudetic monocline are enclosed