Innovative Ai-Driven Field Development Planning: A Case Study in a Giant GOS Field Utilizing a Top-Down Modeling Approach

Abstract The Gulf of Suez Petroleum Company (GUPCO), which has been overseeing mature offshore oil fields in Egypt since the late 1960s, seeks to harness advanced artificial intelligence (AI) technologies to optimize the management of its extensive field data. This initiative is centered on leveraging AI to maximize the value derived from GUPCO's substantial dataset, amassed through the drilling and operation of hundreds of producing and injecting wells, with particular emphasis on the company's two largest fields. Despite the inherent challenges and data gaps characteristic of brownfields, this dataset constitutes an invaluable yet underutilized asset, rich with potential insights. The primary objective of this project is the development of an AI-driven model designed to recommend optimal off-take strategies, identify opportunities for drilling and well interventions, and establish a scalable prototype applicable across GUPCO’s broader field portfolio. Furthermore, this initiative seeks to cultivate organizational expertise in AI applications for oilfield operations. The model is founded upon a purely data-driven methodology, relying exclusively on empirically measured field variables, thereby obviating the necessity for interpretative assumptions. This extensive dataset, spanning five decades, undergoes rigorous processing through advanced fuzzy pattern recognition techniques to extract key parameters. A highly integrated system of artificial neural networks (ANNs) is employed to train, calibrate, and validate the model. To ensure robust predictive capabilities, recent data is reserved for blind validation, enabling the AI model to discern and learn concealed field patterns. Once history-matched, the model is subsequently deployed across various scenarios to forecast production, conduct sensitivity analyses, and identify optimal locations for new drilling activities. The model continuously evolves as new data is assimilated, ensuring its adaptability and reliability over time. Although this initiative remains in its early stages at the time of writing This Paper, significant progress has already been realized. A meticulous process of data review and assurance has been prioritized to uphold model accuracy. Data for the most giant field of GUPCO have undergone extensive QC/QA. The model is expected successfully achieve history matching for wellhead pressure and production rates—including oil, gas, and water—across all wells via a fully automated process, yielding exceptionally high accuracy. Its forecasting capabilities will be rigorously validated through blind testing on spatial and temporal data previously unseen by the model. Extensive simulations—numbering in the hundreds—are expected to be conducted to account for reservoir uncertainties, and to produce reliable production forecasts and comprehensive sensitivity analyses for the projection period. Furthermore, the model is intended to identify several high-potential candidates for infill well drilling, underscoring its capacity to support critical operational decision-making. This study introduces a pioneering AI-based technological advancement to GUPCO, aiming to fundamentally transform the efficiency and depth of technical analyses that underpin field management strategies. This advancement will significantly enhance decision-making capabilities, enabling more rapid and informed responses to operational challenges. Additionally, this initiative represents a strategic investment in human capital, fostering expertise within GUPCO in the application of AI to oilfield operations. As a secondary benefit, the project entails a comprehensive review of GUPCO’s extensive data archive, rendering it a more structured and actionable resource to support future initiatives and assessments. Ultimately, this initiative positions GUPCO at the forefront of AI-driven oilfield management in Egypt, setting a precedent for technological innovation within the industry.