Virtual Assistant Using Generative Pre-Trained Transformers on Reservoir Management Context

In Petroleum Engineering, particularly in the Reservoirs discipline, the analysis and interpretation of data from diverse sources, formats, and quality levels is a constant challenge. Extensive datasets, including seismic data, well drilling information, production records, and reservoir studies, demand both broad technical knowledge and proficiency with specialized computational systems featuring advanced interfaces. In Data Science and Artificial Intelligence, natural language is mathematically represented to be processed by computational systems. Language models, using machine learning, replicate the behavior of natural language by identifying correlations between words, enabling them to predict sequences, translate text, and answer questions intelligently. Inspired by the popularization of chatbots based on derivatives of the Generative Pre-trained Transformer (GPT) model, this work proposes an intelligent virtual assistant specialized in Reservoir Management systems. The trained model proved effective for building an assistant capable of providing a more natural interface, allowing users to concentrate on their core tasks rather than system navigation. The analysis of the model's loss curve showed the network's error tending toward zero, indicating strong performance in test scenarios. Further tests confirmed the assistant's ability to handle natural language requests while avoiding sensitive or out-of-context responses. This development presents a virtual assistant capable of automating complex, domain-specific tasks within a corporate environment, accelerating analyses, trend identification, and decision-making. It stands out as a practical, efficient tool to support Reservoir Engineering teams in their daily operations.