Projects & Work

Developed an end-to-end production forecasting workflow using Python and pandas on Equinor's Volve field dataset. Successfully modeled trend analysis to evaluate critical design parameters, demonstrating AI-driven well performance prediction and reservoir management optimization.

Built a machine learning model for oil & gas pumps and valves using real-time sensor data (temperature, pressure, vibration, flow rate). Achieved high prediction accuracy, enabling early fault detection and reducing unplanned downtime in plant operations.

Designed and developed an interactive Power BI dashboard to analyze well production using multi-source datasets. Conducted trend analysis, decline behavior evaluation, and anomaly identification to highlight underperforming wells and operational inefficiencies.

Conducted in-depth study of API 11D1 design principles for packer development. Collaborated with engineering team to perform advanced calculations ensuring designs met packer envelope specifications. Assisted in documentation and audit preparation for successful API certification.

Designed and optimized gravel-pack and lower-completion configurations for offshore wells using CWOL principles, carrier-fluid leakoff evaluation, and screen selection to improve sand exclusion and inflow performance.

Developed machine learning model using time-series data to analyze reservoir performance. Incorporated oil, gas, and water production rates for accurate WOR & GOR prediction. Optimized model outputs to support freshwater consumption minimization strategies in unconventional reservoirs.