Accelerated ensemble optimization using momentum methods.

Area of Science:

• Petroleum Engineering
• Optimization Methods
• Machine Learning

Background:

• Momentum gradient descent methods are widely used in machine learning.
• Their application to time-consuming dynamic problems like petroleum reservoir management is not well-understood.

Purpose of the Study:

• To investigate the performance of various momentum methods combined with ensemble gradient approximation for accelerated optimization.
• To evaluate these methods in the context of petroleum reservoir production optimization.

Main Methods:

• Extensive testing of four different momentum methods.
• Application to a reservoir test case under deterministic and robust settings.
• Utilizing ensemble approximation of gradients for optimization.

Main Results:

• Momentum strategies demonstrated improved performance in numerical experiments.
• Higher average net present value was achieved compared to standard methods.
• Fewer simulations were required to reach optimal solutions.

Conclusions:

• Momentum methods are effective for accelerating petroleum reservoir optimization.
• These methods offer a promising approach for improving efficiency and economic outcomes in reservoir management.
• The findings suggest broader applicability of momentum techniques in complex dynamic optimization problems.