Prediction of geophysical properties of rocks on rare well data and attributes of seismic waves by machine learning methods on the example of the Achimov formation

TL;DR

This study employs machine learning techniques to predict geophysical properties of rocks in the Achimov formation using limited well data and seismic attributes, aiming to improve reservoir characterization.

Contribution

It introduces a comprehensive machine learning approach, including data enrichment and feature creation, for predicting rock properties with limited well data.

Findings

Successful modeling of radioactivity and seismic attributes relationship

Model validated by cross-validation and new well data

Enhanced prediction accuracy with data enrichment techniques

Abstract

Purpose of this research is to forecast the development of sand bodies in productive sediments based on well log data and seismic attributes. The object of the study is the productive intervals of Achimov sedimentary complex in the part of oil field located in Western Siberia. The research shows a technological stack of machine learning algorithms, methods for enriching the source data with synthetic ones and algorithms for creating new features. The result was the model of regression relationship between the values of natural radioactivity of rocks and seismic wave field attributes with an acceptable prediction quality. Acceptable quality of the forecast is confirmed both by model cross validation, and by the data obtained following the results of new well.