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Go to Editorial ManagerResponse 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.
In this paper, a new correlation was developed (139 data sets) in this study to predict the oil recovery for sandstone solution gas drive reservoirs (SGDR) using NLMR with a coefficient of determination, R2 of 0.90 (compared to 0.87 for Gulstad correlation). An artificial neural network (ANN) model was developed for this reservoir giving an R2 of 0.92. A correlation was developed (111 data sets) in this study to predict the oil recovery for sandstone water drive reservoirs (SWDR) using NLMR with a coefficient of determination, R2 of 0.93 (compared to 0.91 for American Petroleum Institute, API correlation). The developed ANN model for this reservoir gives an R2 of 0.94.
The aim of this study is to optimize ESP performance by evaluating the current conditions and the performance optimization of the electrical submersible pump (ESP) for six oil wells in the Rmelan oil field. fluid and reservoir properties (API = 23, T = 78 C°, pressure of reservoir = 160 atm and the WC is 70%). This paper presents a sensitivity assay conducted by Nodal Analysis (Using PIPESIM Software) on the pump frequency and wellhead pressure. The outflow tubing performance and inflow performance relationship were generated and plotted for each well. The curves are investigated, indicating problems in some wells (W-12R, W-21KH, and W-21SH). The results of this study show that we can increase the flow rate by optimizing the ESP performance by decreasing the wellhead pressure to 71.58 psi and raising the frequency of ESP to a specific value of about 65 Hz based on the limites of production of each types pump capacity . Increasing the frequency from 55 to 65 Hz resulted in increasing the production from 634 to 1092 bb/day for W-12R, from 1928 to 2806 bbl/day for W-21KH, and from 1722 to 2279 bbl/day for W-21SH.
Nanoparticle 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.
The growing demand for energy, coupled with the continued dominance of fossil fuels as the primary energy source, necessitates eco-friendly technologies that simultaneously enhance oil recovery (EOR) and reduce the impact of their emissions. Only one task, which is the CO2-EOR project, can combine these two sustainable development goals. Further, employing green nanotechnology, including nanoparticles and nanofluids, ensures a sustainable approach to controlling and enhancing rock wettability, thereby enhancing hydrocarbon production and carbon storage. However, the performance of nanofluids in subsurface formations is limited by the stability of these nano-dispersions at the harsh conditions of reservoirs. This work thus synthesizes silica nanoparticles from waste bentonite as a green source and modifies the surface properties with a silane group to formulate a stable nanofluid for subsurface applications. The produced nanoparticles were characterized via Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), zetasizer, and dynamic light scattering (DLS). Moreover, the efficiency of nanoparticles as wettability-modifying agents was studied using contact angle and spontaneous imbibition tests. FTIR measurements confirmed the presence of silane on the surface of hybrid silica nanoparticles, as indicated within the Wavenumber 2950 cm-1. Moreover, XRD measurements revealed that hybrid nanoparticles showed lower noise than pure ones. Results also showed that silane-treated nanoparticles (hybrid) are more tolerant to high salinity (≥ 0.5wt% brine), and green-synthesized nanoparticles have a drastic ability to invert the wettability of oil-wet surfaces (θ≥123°) to water-wet (θ ≤ 28°) at ambient conditions and also reduce the contact angle from 175° to 68°) at CO2-EOR conditions. The study concludes that these green nanofluids are highly efficient for EOR and carbon geosequestration projects when properly formulated.
This paper reviews the developments of modeling hydraulic fracturing in tight gas formations, progressing from elementary analytical models to more advanced and coupled geomechanical-flow simulators. We discuss the significant progress that has been made in understanding fluid flow behavior of ultra-low permeability formations, which has significantly improved methodology for analyzing this complex problem. Findings demonstrate the importance of using Discrete Fracture Network (DFN) and Embedded Discrete Fracture Model (EDFM) for representation of complex fracture geometries and connectivity. However, it remains a great challenge to model the stress-dependent changes in permeability and porosity and the dynamic changes of fracture properties during fracturing, as well as the multi-scale interactions between induced hydraulic fractures and natural ones. This paper provides a novel iterative modeling framework that integrates multi-scale interactions and proposes a roadmap for data-driven modeling coupled with fluid flow to enhance predictive accuracy in TGR stimulation.