Automatic Identification of Sedimentary Facies Based on a Support Vector Machine in the Aryskum Graben, Kazakhstan

Abstract

The Aryskum Depression in the South Turgay Basin has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research. Among them, lithologic stratigraphic reservoirs have gradually become one of the focuses of oil and gas exploration. At present, deduction of the sedimentary characteristics of the target layer through core wells using artificial exploration has become an urgent problem to be solved. We selected 16 artificially interpreted coring wells in the Aryskum Graben for this study. Using the parameters of the gamma-ray (GR) curve of coring wells and support vector machine (SVM) classification algorithms, we developed an automatic identification model of sedimentary facies in the study area. The application of the SVM includes the following steps: Firstly, using the GR curve of 16 coring wells, six quantitative indexes defined as standard deviation, relative gravity, curve amplitude ratio, average median, average slope, and mutation amplitude, are selected to quantify the logging curve in the study area, thus realizing the description of the logging curve form. Secondly, training samples are selected to establish an SVM classification model. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional facies. Field application shows that this solution can be effectively used in uncored wells to identify depositional facies with a rate of accuracy approaching 70%. Our results provide new methods for the identification of sedimentary facies in the study area. The results will also provide a theoretical basis, as well as data basis, for further fine division of microfacies in the study area. Appl. Sci. 2019, 9, 4489 2 of 15 research, and lithologic stratigraphic reservoirs have gradually become some of the main focuses of oil and gas exploration.The determination of sedimentary facies plays an important role in the exploration of lithologic reservoirs, especially in the prediction of residual oil production. In addition, coring and logging are seldom carried out in development wells. Therefore, increasing attention has been paid to the method of studying sedimentary facies by logging curves. However, the process of manual identification often incurs many problems, such as an intense workload of data statistics and mapping, low work efficiency, poor quantification, and non-uniformity of the criteria for manual identification of sedimentary facies in uncored wells. These problems highlight that there are many shortcomings in the artificial identification of multi-well and multi-layer logging sedimentary facies. With the popularization of digital logging technology, digital logging curve technology, and computer technology, the machine learning method in particular has the advantages of dealing with non-linear mapping relations. In recent years, machine learning has been preliminarily applied in processing logging data and predicting reservoir pore and pe...

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