Application of Machine Learning Algorithms in Predicting Pyrolytic Analysis Result

Geochemical studies of organic matter in source rocks play an important role in predicting the oil and gas accumulation of any territory, especially in oil and gas shale. For deep understanding, pyrolytic analyses are often carried out on samples before and after extraction of hydrocarbon with chloroform. However, extraction is a laborious and time-consuming process and the workload of laboratory equipment and time doubles. In this work, machine learning regression algorithms is applied for forecasting S2ex based on the pyrolytic analytic result of non-extracted samples. This study is carried out using more than 300 samples from 3 different wells in Bazhenov formation, Western Siberia. For developing a prediction model, 5 different machine learning regression algorithms including Multiple Linear Regression, Polynomial Regression, Support vector regression, Decision tree and Random forest have been tested and compared. The performance of these algorithms is examined by R-squared coefficient. The data of the X2 well was used for building a model. Simultaneously, this data is divided into 2 parts – 80% for training and 20% for checking. The model also was used for prediction of wells X1 and X3. Then, these predictive results were compared with the real results, which had been obtained from standard experiments. Despite limited amount of data, the result exceeded all expectations. The result of prediction also showcases that the relationship between before and after extraction parameters are complex and non-linear. The proof is R2 value of Multiple Linear Regression and Polynomial Regression is negative, which means the model is broken. However, Random forest and Decision tree give us a good performance. With the same algorithms, we can apply for prediction all geochemical parameters by depth or utilize them for well-logging data.