Research on Sand Ground Ratio Prediction Method Based on Neural Network Machine Learning

Abstract

We combine logging data, seismic and inversion prediction results, and realize seismic multi-attribute fusion through machine learning method, so as to determine the favorable area of tight sandstone oil and gas enrichment in Songliao Basin and predict the distribution of sand to ground ratio. It is found that the change of sand ground ratio will cause the change of inversion and seismic reflection characteristics, In this way, the inversion and waveform characteristic parameters can be used to predict the sand ground ratio, the single inversion and reflection characteristic information often corresponds to a variety of geological phenomena, using single parameter to predict sand ground ratio is not of universal significance, so it is reasonable to use multiple parameters to predict sand ground ratio. There are many kinds of attributes contained in impedance and seismic response, so how to select a reasonable attribute combination is the key to predict sand ground ratio. This paper uses neural network machine learning to realize the best combination of multiple attribute results. It mainly synthesizes a variety of data, uses neural network to train the model, and then uses the model to realize sand ground ratio prediction. The prediction results show that the sand to ground ratio predicted by machine learning has a high coincidence rate with the sand to ground ratio in the well, which has clear reference value and guiding significance for the optimization of favorable target areas and the deployment and drilling of well locations.

Copyright 2022, IFEDC Organizing Committee.

This paper was prepared for presentation at the 2022 International Field Exploration and Development Conference in **’an, China, 16–18 November 2022.

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