win德赢

Abstract: We propose an integrated method of data-driven and mechanism models for well logging formation evaluation, explicitly focusing on predicting reservoir parameters, such as porosity and water saturation. Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas. However, with the increasing complexity of geological conditions in this industry, there is a growing demand for improved accuracy in reservoir parameter prediction, leading to higher costs associated with manual interpretation. The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters, which suffer from low interpretation efficiency, intense subjectivity, and suitability for ideal conditions. The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods. It is expected to improve the accuracy and efficiency of the interpretation. If large and high-quality datasets exist, data-driven models can reveal relationships of arbitrary complexity. Nevertheless, constructing sufficiently large logging datasets with reliable labels remains challenging, making it difficult to apply data-driven models effectively in logging data interpretation. Furthermore, data-driven models often act as “black boxes” without explaining their predictions or ensuring compliance with primary physical constraints. This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models. Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure, loss function, and optimization algorithm. We employ the Physically Informed Auto-Encoder (PIAE) to predict porosity and water saturation, which can be trained without labeled reservoir parameters using self-supervised learning techniques. This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.