Well Control Optimization using Derivative-Free Algorithms and a Multiscale Approach

TL;DR: In this article , a multi-solution, surrogate models (SMs)-assisted optimization framework is presented to deliver diverse, close-to-optimum well placement scenarios at a reasonable computational cost.

TL;DR: A framework that combines optimization algorithms and the reservoir simulator together is proposed to solve the determination of optimal well locations and well controls in the horizontal-well steamflooding of heavy-oil reservoirs.

TL;DR: In this work, several techniques, with an emphasis on parameterisation, are proposed and used to address all the three exercises of Olympus optimisation challenge, and it is suggested that the variables corresponding to the location of platform can be separated from the placement of the wells.

TL;DR: This work uses the Hough transform method to characterize complex fracture network from microseismic data and compares the performance of WOA with ensemble–smoother with multiple data–assimilation (ES-MDA), and recommends WOA is recommended in the future application of history matching.

TL;DR: This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation and reveals local and global search properties of the evolution strategy with and without covariance matrix adaptation.

TL;DR: The style of the entries in the Encyclopedia of Machine Learning is expository and tutorial, making the book a practical resource for machine learning experts, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.

TL;DR: This work focuses on the optimization of particle Swarm Optimization for TRIBES or co-operation of tribes with a focus on the dynamics of a swarm.