• 文献标题:   Evaluating the influence of graphene nanoplatelets on the performance of invert emulsion drilling fluid in high-temperature wells
  • 文献类型:   Article
  • 作  者:   ARAIN AH, RIDHA S, ILYAS SU, MOHYALDINN ME, SUPPIAH RR
  • 作者关键词:   oilbased mud, graphene nanoplatelet, rheological propertie, filtration characteristic, artificial neural network
  • 出版物名称:   JOURNAL OF PETROLEUM EXPLORATION PRODUCTION TECHNOLOGY
  • ISSN:   2190-0558 EI 2190-0566
  • 通讯作者地址:  
  • 被引频次:   3
  • DOI:   10.1007/s13202-022-01501-5 EA APR 2022
  • 出版年:   2022

▎ 摘  要

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 degrees 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. [GRAPHICS]