Data Management using Neural Network for managing Well and Seismic Data

The oil and gas industry generates vast amounts of data throughout exploration and production activities, particularly from seismic surveys and well logging operations. These datasets are often unstructured and repetitive, posing significant challenges in terms of storage, retrieval, and analysis. Efficient management of such data is critical for reducing operational costs and enhancing decision-making processes. To address these challenges, data science methodologies—especially supervised learning—have emerged as powerful tools for classifying and interpreting seismic and well data. Supervised learning involves training algorithms on labelled datasets to recognize patterns and make predictions. This approach is particularly effective in identifying non-obvious yet valuable relationships within large volumes of data, thereby supporting strategic decisions in exploration and reservoir management (Kelleher, 2019). The primary objective of this study is to analyse numeric attributes using neural network models to detect patterns within seismic and well datasets. By leveraging these models, the industry can improve the accuracy of subsurface interpretations, optimize drilling strategies, and enhance overall operational efficiency.