AI-Powered Enhanced Oil Recovery Screening with Deep Convolution Networks

Oil spill detection is vital for environmental monitoring and disaster management, helping to minimize ecological damage. Traditional methods, such as satellite imagery analysis and radar sensing, often struggle with challenges like low spatial resolution, weather interference, and difficulty in distinguishing oil from similar substances. These drawbacks lead to inaccurate detection and delayed response time. To overcome these issues, this paper proposes an integrated topology combining attention mechanisms with deep convolution networks for Enhanced Oil Recovery (EOR) screening. The process begins with input data undergoing essential preprocessing steps, which include exploratory data analysis, normalization, and scaling. It helps to identify the underlying patterns and structures within the dataset, followed by graphical visualization to identify complex data features. A classification model based on attention-driven Deep Convolution Neural Networks (DCNN) is employed to focus on relevant object features, ensuring enhanced detection capability. DCNN leverages attention mechanisms to improve feature extraction, thus allowing for more precise object localization. To evaluate the system's effectiveness, the python software is implemented. The outcomes prove that higher accuracy of 98% is attained with the aid of proposed topology.