Advances in Monitoring, Modeling and Control of Multiphase Flow in Artificially Lifted Wells

Dear Colleagues,

This Topic showcases end-to-end advances that enable reliable, efficient, and low-emission production from artificially lifted wells by integrating monitoring, modeling, and control. We welcome contributions that bridge field data, laboratory/rig experiments, mechanistic theory, computational modeling (CFD and reduced-order), and data-driven/AI methods to support real-time decision-making across ESP, rod-pump/beam-pump, PCP, jet-pump, gas-lift, plunger-lift, and hybrid systems. While centered on oil and gas, we also encourage relevant work in geothermal and subsurface fluid management (e.g., produced water/CCUS), where multiphase lift and flow assurance are critical.

Topics may include, but are not limited to, the following：

1. Monitoring and Diagnostics: Downhole/fiber-optic sensing (DTS/DAS/pressure/temperature), acoustics and magnetics, surface metering and virtual metering, sensor fusion and edge/IIoT, flow-pattern and GVF identification, sand-/water-cut monitoring, calibration and uncertainty quantification, and open datasets and benchmarks.
2. Modeling and Digital Twins: Mechanistic and transient multiphase wellbore models (slugging, flow-regime transitions), coupled reservoir–well–network simulation, CFD and surrogate/ROMs, physics-informed ML, system identification, parameter estimation, and validation under HPHT/deep/deviated conditions.
3. Control and Optimization: VSD control of ESPs, gas-lift rate allocation and network optimization, plunger-lift cycle optimization, model-predictive control and reinforcement learning, startup/deliquification strategies, anomaly detection/predictive maintenance, energy efficiency and methane/emissions reduction, and constraints from sand/wax/scale and HSE.
4. Applications and Case Studies: Field trials and pilot implementations, comparative assessments of lift technologies, software/tools and reproducible workflows, techno-economic and lifecycle assessments, and best practices and standards.

Accepted article types: Original research, reviews, perspectives, data descriptors/benchmarks, and software notes.

Dr. Jianjun Zhu
Prof. Dr. Jianli Wang
Dr. Haiwen Zhu
Topic Editors