Application of carbon nanomaterials in oil well drilling fluid has been previously studied and was found to enhance its filtration properties. There is a general consensus that addition of colloids in suspension will alter its rheology, i.e., carbon nanomaterials, in this research work; graphene nanoplatelets are hydrophobic materials, which require functionalisation to improve its dispersion in aqueous solution. However, different degrees of dispersion may vary the rheological properties behaviour of drilling fluid. The objective of this study was to characterize the colloidal dispersion of graphene nanoplatelets (GNP) in aqueous solution and its impact on the rheological properties behaviour of water-based drilling fluid. Dispersion of graphene nanoplatelets was achieved through noncovalent functionalisation by means of surfactant attachment. UV-visible spectroscopy was employed to analyze the dispersion of GNP in aqueous solution. The rheological test was carried out using a simple direct-indicating viscometer at six different speeds. Results revealed that the degree of dispersion of GNP using Triton X-100 was generally higher than both SDS and DTAB. Comparison between the rheological properties behaviour of drilling fluid with GNP dispersed using different surfactants shows little to no difference at low shear rates. At high shear rates, however, greater dispersion of GNP shows higher thinning properties while fluid with a low dispersion of GNP exhibited linear behaviour to thickening properties.
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 oil-based mud is preferred to drill highly technical and challenging formations due to its superior performance. However, the inadequate chemical and thermal stability of conventional additives have greatly influenced the performance of oil-based mud at high-temperature conditions. Therefore, it is critical to design an oil-based mud with additives that withstand and improve its performance at high-temperature conditions. The nanoparticles have emerged as an alternative to the conventional additives that can significantly enhance the rheological and filtration characteristics of oil-based mud at high-temperature conditions. In this research study, a novel formulation of OBM enhanced with GNP is formulated, and its performance at high-temperature conditions is investigated. An extensive experimental study has been performed to study the effect of graphene nanoplatelets on the rheological and filtration properties along with flow behaviour, viscoelastic properties, electrical stability and barite sagging of oil-based mud at high temperatures. The graphene nanoplatelets are characterised to ascertain their purity and morphology. The result shows that the graphene nanoplatelets exhibited efficient performance and improved the rheological and filtration properties of oil-based mud. The plastic viscosity and yield point are improved by 11% and 42%, with a concentration of 0.3 ppb. Similarly, the gel strength and barite sagging tendency are enhanced by 14% and 2%, respectively. The filtration loss is also significantly decreased by up to 62% and 46%, with 0.5 ppb concentration at 100 and 120 °C. The addition of GNP results in the formation of a thin mud cake compared to the base mud sample. The rheological modelling recommends the shear-thinning behaviour of oil-based mud (n < 1), which is correlated with the Herschel–Bulkley model. An Artificial Neural Network model is developed to predict the viscosity of OBM based on the four input parameters (concentration of nanoparticles, temperature, shear rate and shear stress). The results demonstrate that graphene nanoplatelets have a favourable impact on the performance of oil-based mud. The addition of graphene nanoplatelets, even at small concatenation, has significantly improved the properties of oil-based mud at high-temperature.
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