Fuel The Future of Oil & Gas Indsutry with AI

In the Rumaila supergiant oil field, detecting well issues like scaling, cessation, or slugging is critical to maintaining productivity. This project employs cutting-edge change point detection techniques to break down time-series data into sub-trends, each representing a distinct phase of well behavior. By analyzing statistical properties of these sub-trends, we identified problematic patterns, enabling swift action to maximize production uptime.

Production anomalies can signal sensor errors or significant operational events. By applying advanced unsupervised techniques such as ADTK, Local Outlier Factor (LOF), and Isolation Forest, this project identified irregularities in Digital Oilfield (DOF) data. The result? Enhanced monitoring capabilities that highlight potential issues before they escalate, saving time and resources

Navigating complex HSE protocols in oil and gas can be daunting. This virtual advisor acts as a 24/7 assistant, delivering accurate, human-like responses to queries about safety guidelines, incident management, and best practices. Designed specifically for the industry, it ensures personnel can make informed decisions quickly, enhancing safety and compliance

Permeability and porosity are the backbone of reservoir evaluation. This project introduced a robust machine learning framework to predict these properties with remarkable accuracy, transforming raw well data into actionable insights. The result is a deeper understanding of fluid flow and reservoir quality.

Drilling efficiency is vital in complex well trajectories. This framework leveraged real-time data and prior knowledge to optimize the rate of penetration in S-shaped wells. By combining data-driven insights with operational expertise, the project minimized downtime and reduced drilling costs in Southern Iraq fields.