Abstract

This paper presents a novel approach to improve the accuracy of rate of penetration (ROP) predictions, addressing a critical need for reducing drilling costs and optimizing performance, particularly in geothermal well drilling. The proposed framework employs least-square regression on field data, considering a broader spectrum of ROP-influencing factors. These factors are categorized into drilling parameters (e.g., weight on bit, rotary speed, standpipe pressure, bit diameter, and flow rate), mud properties (e.g., mud weight, plastic viscosity, yield point, gel strength, and solid content), and formation characteristics (e.g., uniaxial compressive strength and porosity). The model was developed using 183 ft of sandstone formation data from a well in Saudi Arabia, achieving a correlation coefficient (R²) of 0.8. Testing on a similar 39 ft sandstone formation in another well resulted in an average error of 4.4%, significantly outperforming Maurer’s model, which showed a 22.8% error. This represents an 80% improvement. By analyzing power exponents in the model, the dependency on various factors was quantified, enabling optimized drilling operations based on cost and time. The study bridges a gap in existing models by integrating drilling parameters, mud properties, and formation characteristics, achieving error margins below 5% and enabling robust ROP predictions and optimizations.