Quantitative Analysis of the Main Controlling Factors of Oil Saturation Variation

Abstract

With the high-speed development of artificial intelligence, machine learning methods have become key technologies for intelligent exploration, development, and production in oil and gas fields. This article presents a workflow analysing the main controlling factors of oil saturation variation utilizing machine learning algorithms based on static and dynamic data from actual reservoirs. The dataset in this study generated from 468 wells includes thickness, permeability, porosity, net-to-gross (NTG) ratio, oil production variation (OPV), water production variation (WPV), water cut variation (WCV), neighbouring liquid production variation (NLPV), neighbouring water injection variation (NWIV), and oil saturation variation (OSV). A data processing workflow has been implemented to replace outliers and to increase model accuracy. A total of 10 machine learning algorithms are tested and compared in the dataset. Random forest (RF) and gradient boosting (GBT) are optimal and selected to conduct quantitative analysis of the main controlling factors. Analysis results show that NWIV is the variable with the highest degree of impact on OSV; impact factor is 0.276. Optimization measures are proposed for the development of this kind of sandstone reservoir based on main controlling factor analysis. This study proposes a reference case for oil saturation quantitative analysis based on machine learning methods that will help reservoir engineers make better decision.