A Comparison of Two Machine Learning Techniques for the Prediction of Initial Oil in Place in the Niger Delta Region

Abstract

Conventionally, the knowledge of experts on the drilling features of a potential oil well is practically used to predict the volume of initial oil in place. Experts used different knowledge-based models such as volumetric, material balancing, analogy to predict the initial oil in place. In this study, 816 datasets were collected from Shell petroleum development company (SPDC) where the volumetric method is used for their prediction. These datasets were preprocessed and applied on two machine learning techniques of random forest and supervised vector regressor to predict the initial oil in place and the results obtained were compared with that obtained from SPDC.The results of computation using 4 principal features from the 9 features were closer to that obtained from SPDC than the computations using all the 9 features. The results of computations with random forest were also compared with that of supervised vector regressor. The results of random forest covary strongly (0.970) with the field results more than that of the support vector regressor (0.832). The uniqueness of this study is shown in the use of 4 predicting features (independent variables) to obtain prediction values that are very close to that obtained in the field with 9 features. This is obtained with random forest, so it can be recommended as a reliable machine technique for the prediction of initial oil in place in the Niger delta region.