The application of machine learning methods for predicting porosity of rocks based on data of x-ray fluorescence analysis and gamma-ray spectrometry

UDK: 553.98.001

DOI: 10.24887/0028-2448-2018-7-64-69

Key words: forecast, porosity, x-ray fluorescence analysis, gamma-spectrometry, machine learning

Authors: S.V. Shadrina (Tyumen Branch of SurgutNIPIneft, RF, Tyumen), A.A. Shadrin (University of Oslo, Norway, Oslo)

Developing of the non-traditional hydrocarbon reservoirs and underground water deposits allotted a number of tasks to specialists the solution of which goes beyond the traditional methodologies. The most significant problem among them is unable to predict petrophysical parameters (in particular porosity coefficient) for lithology undifferentiated section. There was examined the dependence of porosity coefficient of rocks upon the chemical composition of weakly differentiable volcanic rocks. It takes much time and much more petrophysical parameters to decide this problem by traditional mathematical-statistical methods. These factors make the topical application of modern mathematical methods to obtain evaluation of porosity based on characteristics of the rock and does not depend on the condition of the core. On the other hand, such approach allows taking into account the collectors and fluid traps geochemical mechanism formation in non-traditional objects to predict its spatial distribution. Comparison of the various modern techniques of machine learning to predict porosity is showed in the article. The best predictive capability showed methods based on decision trees-forest – Random forest and Extra trees, providing an average coefficient of determination for test samples equal to 0,52, Pearson product moment correlation coefficient is 0,722 and 0,701 respectively. In addition, these methods allow to rank factors used for predicting porosity of rocks, on the degree of their influence on the accuracy of the prediction. The received results permit to use of the methods of machine learning as a promising approach for predicting porosity through description of mechanisms of secondary mineral formation

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