Abstract: Affected by the complex variability of reservoir physical properties, fluid characteristics, and technological measures during hydrocarbon development, the utilization of production data largely depends on the professional experience of petroleum scientists and engineers, resulting in high computational and time costs. This makes it difficult to meet the demands of efficient modern hydrocarbon reservoir development, necessitating the discovery of more efficient methods for hydrocarbon production prediction. In recent years, machine learning methods represented by deep neural networks, random forest algorithms, and transfer learning have achieved significant application results in hydrocarbon production prediction due to their unique advantages in handling high-dimensional data, capturing long-term dependencies in time-series data, and extracting development data features. This paper analyzes the principles, advantages, and disadvantages of commonly used machine learning methods for hydrocarbon production prediction, elaborates on the current application status of these methods in the field of hydrocarbon production prediction, summarizes potential issues during application, and prospects the development trends of hydrocarbon production prediction methods. In the future, on one hand, research on integrating physical constraints into machine learning models should be strengthened to enhance model interpretability and avoid overly idealized prediction results; on the other hand, algorithms and transfer learning techniques suitable for small-sample scenarios should be developed to fully utilize historical production data, providing better data analysis and technical support for hydrocarbon production prediction. This research has theoretical significance for improving intelligent hydrocarbon production prediction technology.

Key words: oil and gas field development, production prediction, machine learning, neural network, transfer learning