In the area of reservoir engineering, the optimization of oil and gas production is a complex
task involving a myriad of interconnected decision variables sha** the production system's …

Evolutionary algorithms require large number of function evaluations to locate the global
optimum, making it computationally prohibitive on dealing with expensive problems …

Production optimization led by computing intelligence can greatly improve oilfield economic
effectiveness. However, it is confronted with huge computational challenge because of the …

We consider a technique to estimate an approximate gradient using an ensemble of
randomly chosen control vectors, known as Ensemble Optimization (EnOpt) in the oil and …

Closed-loop field development and management (CLFDM) is defined as a periodic update
of an uncertain field model using the latest measurements (data assimilation), followed by …

Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving
computationally expensive optimization problems (EOPs). However, the performance of …

Surrogate models, which have become a popular approach to oil‐reservoir production‐
optimization problems, use a computationally inexpensive approximation function to replace …

In this study, we present an efficient and flexible adjoint-based framework for history
matching and forecasting geothermal energy extraction at a large scale. In this framework …

For the cost-effective optimization of well locations and types under geologic uncertainty,
proxy modeling or surrogate modeling of reservoir simulation is required. Recently, a …

Abstract Multi-objective optimization (MOO), which involves more than one conflicting
objective to be optimized simultaneously, is expected to provide efficient and …