Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

As the main parent and guardian, mothers are often concerned with the study performance of their children. More specifically, most mothers are eager to know the concrete examination scores of their children. However, with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking (PERR) method to attempt at balancing teachers’ privacy disclosure concerns and the mothers’ concerns over their children’s examination performance. By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.