×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerNanoparticle additives emerge as a modern solution to eliminate the performance gap between conventional water-based drilling fluids (WBDFs), and more superior but environmentally challenging oil-based drilling fluids (OBDFs). This study focuses on the enhancement of KCl polymer mud using nano-additives. While nano-additives like copper oxide (CuO NPs) were studied and showed promising results, another form of copper (elemental copper nanoparticles, Cu NPs) with a potential as a multifunction mud additive remains largely unexplored. This research systematically investigates the impact of Cu NPs (0.04–0.8 wt%) on the lubricity, rheology, and filtration properties of KCl polymer mud. All the measurements were done in the lab at room temperature, using lubricity tester, viscometer, and low-pressure filter press. Most additives tend to enhance one property of the mud, but the Cu NPs acted as a more superior properties enhancer, as it didn't enhance only one aspect of KCL polymer mud, but acted as multifunctional additive. For the lubricity, the effect of Cu NPs was significant on the coefficient of friction (CoF), with maximum reduction of 41.68% observed at 0.8% concentration, however at the 0.2% concentration, a relatively similar result of CoF reduction was observed with 39.78% making it the optimal concentration for the lubricity aspect. For the rheological properties, the addition of Cu NPs to the KCL polymer mud enhanced the overall rheological properties, increasing the plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength, the highest values [PV (44.5 cP), YP (69.4 lb/100ft²), AV (77.35 cP)] were observed at 0.2% concentration. Unlike its beneficial effects on lubricity and rheology, the addition of Cu NPs to KCl polymer mud resulted in increased fluid loss and thicker filter cakes. The study concludes that a concentration of 0.2 %wt of Cu NPs is optimal for the simultaneous enhancement of lubricating and rheological properties in KCl polymer mud. This study highlights the potential of Cu NPs as a multifunctional additive that can be used in advanced water–based drilling fluids systems.
Response Surface Methodology (RSM) has been widely applied in drilling engineering to model and optimize multivariable operational systems. This review critically evaluates recent RSM applications in drilling engineering, identifies their methodological limitations, and examines the extent to which bit-level hydraulic performance parameters have been incorporated into drilling optimization frameworks. A structured review of studies published between 2021 and 2026 was conducted. The selected studies were classified into three categories: mechanical performance optimization, drilling-fluid optimization, and hybrid optimization approaches. Each study was evaluated according to its input variables, response variables, modeling techniques, optimization objectives, reported performance improvements, consideration of physical and operational constraints, and integration of data-driven methods such as Artificial Neural Networks (ANN). The review indicates that most RSM-based studies prioritize outcome-based indicators, particularly rate of penetration (ROP), drilling-fluid rheology, and operational parameter selection. In contrast, bit-level hydraulic parameters, specifically Bit Hydraulic Horsepower (BHHP) and Jet Impact Force (JIF), are rarely treated as primary optimization objectives despite their direct influence on hydraulic energy transfer, rock fragmentation, and cuttings removal at the bit–rock interface. The findings also show that most existing studies employ single-objective optimization and second-order polynomial models, which may limit their ability to represent nonlinear and coupled hydraulic behavior under field operating conditions. This review adds to the existing literature by systematically identifying the limited integration of physics-based hydraulic indicators into RSM-driven drilling optimization and by defining a research direction for hybrid frameworks that combine RSM-based design-space validation, ANN-based nonlinear prediction, and constrained multi-objective optimization of BHHP and JIF. Such frameworks could support more robust, scalable, and field-applicable drilling optimization strategies.