It is difficult to directly observe the states of rock and fluids in underground reservoirs. Only various data of hydrocarbon production and collected data from underground oil and gas are used to infer structures. The data is used to understand the lithologic variation of formation and reserves and the flow state of underground oil and gas. Artificial intelligence (AI) technology could become a great asset in collecting and analyzing this vital information.

The demand for AI technology is increasing every day. AI technology includes optimization of well drilling and completion, optimization of water injection and gas injection, scheme, design of tertiary oil recovery technology (e.g., chemical flooding, microbial flooding, nano-intelligent flooding, etc), optimization of unconventional oil and gas reservoir hydraulic fracturing, engineering design of huff and puff technology and prediction of oil/gas production rate and pressure/saturation profiles.

This Special Issue aims to bring together original research and review articles highlighting the combination of AI methods and traditional engineering for underground hydrocarbons. We hope that this Issue explores the application of AI algorithms (e.g., artificial neural networks (ANN), deep recurrent networks (RNN), decision trees, convolution neural networks (CNN), generative advanced networks (GAN), Bayesian neural network (BNN) and etc.) in investigating practical problems of exploring and exploiting underground oil and gas.

Potential topics include but are not limited to the following:

• Data discovery of micro-scale or multiscale flow mechanisms
• Fast numerical simulation of heat and mass transfer for thermal recovery
• Fine geological characterization of unconventional reservoirs
• Smart engineering design of second or tertiary oil recovery technology
• Fast reservoir data matching without repeated grid-based numerical simulation
• Neural network-based reservoir prediction
• Real-time optimization of well drilling, well completion, and hydraulic fracturing
• Smart monitoring and control with limited information
• Data-driven numerical simulation of multiphase flow
• New AI technology for petroleum engineering