Production prediction and main controlling factors in a highly heterogeneous sandstone reservoir: Analysis on the basis of machine learning

Abstract

Abstract Owing to a lengthy oil‐bearing interval, strong anisotropism, and significant difference in the fluid properties of the sandstone oil reservoir in P Oilfield, it is quite challenging to accurately the productivity of the oil well at the initial stage. In this study, a deep neural network model is established, based on a gradient boosting algorithm, XGBoost, to forecast the initial productivity of oil wells, followed by an evaluation of the main controlling factors of productivity. One hundred oil wells in the study area were divided into training and verification groups. With a specific productivity index of an oil well with a stable period of approximately 6 months at the initial production stage as the target data, and geological, engineering, and oil reservoir parameters as input data, hyper‐parameters for adjustment and optimization were selected, and a deep‐learning‐based unconsolidated sandstone productivity forecast model was established to forecast the initial productivity of oil wells in the target area. The mean square root error of the forecast result was crude oil viscosity > median grain size > lithology index > well completion method > flow zone indicator. Machine learning has the advantages of effective forecasting of oil well productivity and the main controlling factors using multiple dimensions and big data.

Keywords

• deep learning
• microscopic pore structure
• productivity prediction
• unconsolidated sandstone