Optimizing Drilling Operations with Artificial Neural Networks: A Novel Framework for Fluid-Loss Prediction in Conventional Oil-Based Mud

The objective of this paper is to investigate the feasibility of using Artificial Neural Networks (ANN) to predict fluid loss in oil-based mud, including collecting and analyzing data from high-temperature and high-pressure experiments, identifying key parameters, and evaluating the ANN model's accuracy. This approach consists of four stages: data acquisition, statistical analysis, modeling, and validation. A comprehensive database of 400 data points is compiled, featuring parameters such as yield point, plastic viscosity, and density. A programming platform is utilized to implement the ANN algorithm, and the dataset is divided into 70% training and 30% testing data to assess the model's accuracy and performance. The training data is used to optimize the model's parameters, while the testing data is used to evaluate the model's predictive capabilities, ensuring a robust and reliable prediction of fluid loss in oil-based mud. The developed ANN model achieves remarkable predictive accuracy for fluid loss in conventional oil-based mud, with a notable 95% accuracy rate. Correlation coefficients exceeding 0.9 indicate strong agreement between predicted and actual values, confirming the model's reliability. The study highlights the importance of rigorous statistical analysis and careful data partitioning for optimal model performance. The model's accuracy remains stable across varying operating conditions, reinforcing its applicability in real-world scenarios. Furthermore, the results demonstrate that the ANN model can effectively capture the complex relationships between the input parameters and the fluid loss, providing valuable insights for drilling operators to optimize their operations. Additionally, the model's high accuracy and robustness make it a valuable tool for predicting fluid loss in a wide range of drilling scenarios, including high-temperature and high-pressure environments. This paper introduces a novel ANN framework for predicting fluid loss in oil-based mud, enhancing the understanding of complex interactions governing fluid loss phenomena. The methodology provides a cutting-edge analytical tool for optimizing drilling operations and mitigating associated risks, enriching the existing body of knowledge in the petroleum industry.