Abstract Machine Learning (ML) has demonstrated its immense contribution to reservoir
engineering, particularly reservoir simulation. The coupling of ML and metaheuristic …

Reservoir history matching represents a crucial stage in the reservoir development process
and purposes to match model predictions with various observed field data, including …

In history matching, the calibration of a prior reservoir model is computationally expensive
because many forward reservoir simulation runs are required. Multiple posterior (or …

This study integrates a convolutional neural network (CNN) within the framework of robust
optimization for determining the placement of an oil production well at a petroleum reservoir …

One of the main problems associated with applying data assimilation methods for facies
models is the lack of geological plausibility in updates. This issue is even more acute for …

Production prediction has been playing an essential role in reservoir development and
management. Reservoir history matching, typically involving time-consuming numerical …

Data assimilation (DA) for uncertainty reduction using reservoir simulation models normally
demands high computational time; it may take days or even weeks to run a single reservoir …

This study proposes three-phase saturation identification using X-ray computerized
tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation …

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand
active faults or other causes of earthquakes, thereby facilitating the preparation of early …

Accurate identification of groundwater contamination sources is important for designing
efficacious site remediation strategies. Currently, the methods for identifying contamination …