Fluid identification in geological exploration using transformer models and deep reinforcement learning

In geological exploration, identifying fluids is essential for assessing the potential of oil and gas reservoirs. This research presents a refined method for fluid detection using log data by integrating advanced transformer models with deep reinforcement learning, aiming to enhance the precision and efficiency of fluid identification. We use traditional well-logging data as input, with transformer models employed for feature extraction. These models excel at processing sequential data and capturing long-range dependencies within logging sequences, which leads to a more accurate depiction of geological formations. Furthermore, the self-attention mechanisms within transformers enhance their flexibility and precision in analyzing complex geological structures. We incorporate deep reinforcement learning (DRL) strategies to improve fluid identification further. The DRL framework establishes an environment where an agent learns to maximize fluid identification accuracy by dynamically selecting and adjusting model parameters in response to different geological conditions. This integration increases the model's adaptability and bolsters its robustness when encountering unfamiliar or variable geological settings. We validated the effectiveness of this approach using well-logging datasets from the Tarim Oilfield. The experimental results indicate significant improvements in accuracy and operational efficiency compared to traditional methods. Specifically, the proposed approach excels in handling complex geological structures, significantly reducing misidentification rates and enhancing overall identification accuracy.