Date & Time: September 12th 0900-1000 CET
Speaker: Mathias Methlie Nilsen is one of the PhD students in CSSR employed at NORCE. He is in his final year of the CSSR PhD program and delivered his dissertation in August. Before beginning his PhD at CSSR, he completed a master’s degree in theoretical atomic, nuclear, and particle physics at the University of Bergen.
Mathias has published the paper Closed-loop Workflow for Short-term Optimization of Wind-powered Reservoir Management at European Association of Geoscientists & Engineers (EAGE).
This webinar presents a collaborative study conducted by Mathias M. Nilsen, Rolf Lorentzen and Andreas Størksen Stordal from NORCE, together with Olwijn Leeuwenburgh and Eduardo Barros from TNO. Below are the key points from the Webinar.
Context and Motivation
The Norwegian continental shelf faces significant CO2 emissions, primarily due to gas turbines used in the petroleum sector. These turbines burn gas to generate electricity, contributing heavily to greenhouse gas emissions. The proposed solution is to integrate nearby offshore wind farms to power production, despite the inherent variability and uncertainty of wind power compared to gas turbines.
Test Case: Drogon Reservoir
Developed by Equinor, the Drogon Reservoir entails:
- Reservoir Grid: 2 Injection wells (A5 & A6) and 5 Production wells (A1 to A4 and OP5).
- Objective: Optimize injection rates in the injectors and target oil production rates in the producers.
Power Demand and Emissions
The power system includes:
- Gas compression
- Water pumping for injection
- Energy cost of treating produced water
- A constant base load power demand The power sources involve two 8 MW wind power turbines alongside traditional gas turbines.
Methodology
- Model Components:
- Efficiency curves for injection pumps and gas compressors
- Emission rate model for gas turbines
- Power output model for wind turbines based on wind speed
- Workflow Goal: Using wind power forecasts to vary daily operational strategies, aimed at reducing emissions while maintaining the long-term operational goal of maximizing lifecycle net present value (NPV).
- Optimization Process:
- Time is divided into intervals (e.g., one month).
- Short-term optimization is performed using wind power forecasts, focusing on minimizing emissions and penalizing deviations in production and injection volumes.
- Recalibration and Coarsened Model: A coarsened model is used for faster optimization, recalibrated before each optimization to match the full model’s simulation results.
Workflow Results
The workflow involves:
- Target Strategy: Developed based on NPV optimization, focusing on target oil and injection rates.
- Performance: Successful intervals showed significant CO2 reduction with minimal NPV compromise, e.g., a 27% emission reduction and a 0.6% NPV decrease. However, certain intervals exhibited issues due to ineffective coarse model calibration.
Conclusion and Future Work The study demonstrates the potential of using wind farm power forecasts in reservoir management. Future improvements are necessary for better model accuracy, considering alternatives such as machine learning or statistical models.
For Everyone: What Does This Mean?
This webinar explained how offshore operations can shift more of their energy use to wind power by using wind forecasts in their planning. Mathias demonstrated a workflow that adjusts pumping and production schedules depending on how much wind power is expected each day. The results showed that emissions can be noticeably reduced, even when the wind varies, and production can still be maintained at stable levels. The session made clear that better forecasting and planning can allow offshore platforms to use cleaner energy without introducing operational problems.