Digital Twin-Assisted Reservoir Management for Infill Drill Optimization

This study presents the development of an advanced digital twin technology designed to optimize infill drill locations within coal-bed methane reservoirs. By facilitating data-driven decision-making, the technology identifies the most productive drilling locations, enhancing overall reservoir management. The methodology encompasses designing well trajectories and the identification of coal seams, followed by the characterization of reservoir and geological properties. This is succeeded by the optimization of hydraulic fracturing parameters and the assessment of offset well interference at the planned well locations. These factors are used to train AI models capable of forecasting gas and water production during both transient and steady-state phases, with careful consideration of the varying physics and reservoir dynamics inherent to each phase. Testing and blind datasets validate these forecasts, demonstrating high accuracy. Additionally, the digital twin architecture performs Key Performance Indicator (KPI) analysis to identify the most influential input variables affecting production forecasts. By analyzing production forecasts, coal seam data, and reservoir characteristics, the technology effectively determines the optimal drilling locations. The modular design of the system supports continuous learning and customization with live datasets, aligning predictions with field observations. This leads to significant improvements in reservoir management, drastically reducing analysis time from weeks to hours.