Abstract

Accurate forecasting of drilling time and cost remains a persistent challenge in complex directional well operations, particularly in heterogeneous geological settings. Traditional estimation approaches often lack the fidelity required to account for subsurface variability and historical field performance, resulting in budgetary overruns and operational inefficiencies. This paper presents a comprehensive conceptual model designed to integrate geomechanical parameters and historical drilling data to enhance predictive accuracy for time and cost planning in directional drilling. The model incorporates inputs such as formation pressure, rock strength, well trajectory, and lithological profiles alongside time-depth records and cost logs from prior wells. By mapping functional relationships—such as rate of penetration versus unconfined compressive strength—and embedding them within a logical structure, the framework enables dual-layer output forecasting with risk-adjusted confidence intervals. The model promotes cross-functional collaboration by aligning geological, engineering, and financial perspectives into a unified forecasting tool. It offers a data-driven basis for proactive decision-making, enabling improved planning, resource allocation, and operational reliability. Future development may include real-time data integration and machine learning-based calibration to refine predictive performance further.