Automated Wellhead Monitoring Using Deep Learning From Multimodal Imaging

Automatic Recognition Of Homogeneous And Heterogeneous Reservoirs Using Deep Learning Technique

Automatic Well Test Validation Empowered By Machine Learning And Natural Language Processing

Supervised Machine Learning For Liquid Rate Estimation In Oil Fields With Limited Well Monitoring Data: A Case Study Of The Handil Oil Field

Development Of An Ai Model To Estimate Flowing Bottom-hole Pressure In High-pressure High-temperature Gas Well

Digital Twin AI Based Model Of Multiphase Flow Loop Test To Develop New Generation Of Production Technologies

Autonomous ESP Optimization: A Case Study Of Implementation At The Wellsite-level In The Permian Basin

Development Of A Hybrid Modeling Approach For Estimating Bottom Hole Pressure In Shale And Tight Sand Wells Using Production Data And Machine Learning Techniques

Development Of Two Robust Data-driven Models Using Machine Learning And Artificial Neural Network Methods To Predict Wells Fluid Rate In A Challenging Offshore Brown Field

Increased ESP Runtime And Reduced Deferment At Oriente Basin By Applying Digital Rapid Response Ranking And Event Detection System

Multi-phase Virtual Flow Metering With Deep Learning For Well-aggregated Stationary Prediction