The chemical sand consolidation methods involve pumping of chemical materials, like furan resin and silicate non-polymer materials into unconsolidated sandstone formations, in order to minimize sand production with the fluids produced from the hydrocarbon reservoirs. The injected chemical material, predominantly polymer, bonds sand grains together, lead to higher compressive strength of the rock. Hence, less amounts of sand particles are entrained in the produced fluids. However, the effect of this bonding may impose a negative impact on the formation productivity due to the reduction in rock permeability. Therefore, it is always essential to select a chemical material that can provide the highest possible compressive strength with minimum permeability reduction. This review article discusses the chemical materials used for sand consolidation and presents an in-depth evaluation between these materials to serve as a screening tool that can assist in the selection of chemical sand consolidation material, which in turn, helps optimize the sand control performance. The review paper also highlights the progressive improvement in chemical sand consolidation methods, from using different types of polymers to nanoparticles utilization, as well as track the impact of the improvement in sand consolidation efficiency and production performance. Based on this review, the nanoparticle-related martials are highly recommended to be applied as sand consolidation agents, due to their ability to generate acceptable rock strength with insignificant reduction in rock permeability.

Water-based mud (WBM) faces challenges in high-temperature, high-pressure (HTHP) conditions due to fluid loss and property degradation. Enhancing eco-friendly drilling fluids with optimal rheology is crucial for sustainable, cost-effective, and environmentally safe drilling operations. This study formulated a WBM using green-synthesized zinc oxide (ZnO) nanoparticles (NPs, ~ 45 nm) and tragacanth gum (TG), a biodegradable natural polymer. The synthesized ZnO NPs were comprehensively characterized using energy-dispersive X-ray spectroscopy (EDS), field-emission scanning electron microscopy (FE-SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA/DTG) to determine their structural, morphological, and chemical properties. Rheological properties, including flow behavior index (n), consistency index (K), plastic viscosity (PV), and yield point (YP), were analyzed at 25, 50, and 75 °C using the Bingham-plastic and Power-law models. The accuracy of the model was validated using Analysis of Variance (ANOVA), which assessed the significance of the results. Additionally, Design Expert software was utilized to optimize the concentrations of TG and ZnO for elevated temperature applications. Moreover, the response surface methodology (RSM) results were evaluated by reporting the R² and accuracy metrics, confirming the strong correlation between predicted and actual values, which demonstrates the model’s robustness. Three optimal samples underwent HTHP filtration tests at 120 °C and 500 psi. The ideal formulation of 750 ppm TG and 0.25 wt% ZnO NPs improved PV by 27.84%, YP by 43.16%, reduced fluid loss by 54.16%, and mud cake thickness by 25%. The optimized sample showed superior performance, with a ‘K’ of 56.12 cp and a ‘n’ of 0.2272, ensuring effectiveness under HTHP conditions. This sustainable formulation reduced environmental contamination risks and drilling fluid consumption while enhancing operational efficiency.

Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel–Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

The article discusses experimental investigation of a polymeric nanocomposite composed of HPAM/GO-SiO₂. The DLS, zeta potential, scanning electron microscopy, and infrared spectroscopy are utilized to analyze the resulting composite. As the interfacial tension of a nanopolymeric solution is dependent on a few critical variables, the research simulated interfacial tension using response surface methodology. The results indicate that interfacial tension is not equally critical for all parameters. There was no evidence presented to demonstrate the model's inadequacy. The central composite design had an R² of 86.81%, indicating that it was the optimal choice for evaluating the impact of hybrid polymeric nanofluids.