This paper delves into the creation of a groundbreaking Generative AI (GPT) model, specifically designed for oil and gas production forecasting. It outlines the objectives behind developing this first-of-its-kind model, emphasizing the focus on enhancing prediction accuracy and overcoming existing forecasting challenges in the oil and gas sector.
Our approach was to construct a 60 million parameter GPT model, utilizing a novel generative AI methodology. The development process involved collecting and analyzing extensive temporal data from the Permian Basin. We encountered and overcame multiple challenges, including data heterogeneity and model scalability. The model's robustness was tested through comprehensive back testing and forward forecasting, ensuring its capability to handle the complexities of oil and gas production data.
The development of this GPT model marks a significant advancement in the field of oil and gas production forecasting. Our observations showed that the model could adapt to the diverse and complex nature of production data, providing highly accurate forecasts. The challenges encountered during development, such as managing large datasets and ensuring model reliability, were successfully addressed, resulting in a robust and scalable forecasting tool. Conclusively, the model demonstrated a significant improvement in prediction accuracy, outperforming traditional forecasting methods by a wide margin. This indicates a promising future for AI-driven approaches in the oil and gas industry, particularly in areas where precision is critical.
This paper presents pioneering work in the realm of oil and gas forecasting, showcasing the development and implementation of a unique Generative AI model. It provides new insights into tackling complex forecasting challenges, offering a significant contribution to the field and serving as a valuable resource for engineers and data scientists in the industry.
Production Forecasting in Oil and Gas
Production forecasting in the oil and gas sector is critical for predicting future output from wells or fields, playing an essential role in optimizing resource allocation, enhancing financial planning, and informing strategic decisions. This task becomes particularly complex in unconventional reservoirs, characterized by intricate geologies and heterogeneous formations. These features pose significant challenges in accurately predicting flow rates and recovery efficiencies. Traditional production curves, such as those initially formulated by Arps (1945) for conventional reservoirs, often prove inadequate in these contexts, as highlighted by Sharma and Lee (2016). They advocate for the adaptation of these models through new workflows to accommodate the distinct characteristics of unconventional reservoirs.