Block Development Prediction Model Based on Machine Learning

This study uses machine - learning techniques to build an accurate yield prediction model for oilfield development. It applies algorithms like linear regression, GBDT, and MLP to analyze reservoir data. The yield dataset from numerical simulation undergoes pre - processing, including cleaning, transformation, and min - max normalization. As a result, 2,698 valid data sets are obtained, and 8 features are chosen considering practical situations. Correlation analysis reveals that geological parameters have a high correlation with cumulative oil production, with the oil bearing interval length having a coefficient of 0.67, while fracturing parameters have a low correlation. Based on these, a machine - learning - based model is established. The particle swarm optimization algorithm is used to optimize model parameters, and metrics such as RMSE and (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) are adopted to evaluate its performance. Experimental results show the model has excellent accuracy and generalization, providing reliable support for yield prediction in block development plans and enhancing oilfield development efficiency.