Electric treatment is a promising method to improve the cold flowability of waxy crude oil by taking advantage of the electrorheological (ER) effect of the crude oil. However, the efficacy of viscosity reduction under an electric field differs significantly among various crude oils. This paper investigates the role of graphene nanoparticles (GNPs) in enhancing the ER effect of a waxy crude oil with less sensitivity to the electric field. It is found that the addition of GNPs may significantly enhance the ER effect and its durability. Quantitative results revealed that with 200 ppm GNPs, the maximum viscosity reduction reaches 78.4% (vs. 57.1% for undoped oil) at 34 ℃, and the treatment time to achieve 57.1% viscosity reduction is shortened from 10 min to 2 min. Furthermore, GNPs reduce the energy density threshold for maximum viscosity reduction from 1.08 J/mL to 0.96 J/mL, weaken the structure of the gelled oil (yield stress reduction: 80.1% vs. 43.5% for undoped oil), and extend the durability of viscosity reduction from 5 to 12 h. It is further demonstrated that both the added GNPs and charged particles in crude oil accumulate on the surfaces of wax particles under an electric field, and the effect of GNPs’ nano-templating and the electrostatic repulsion of charged particles work together, weakening the wax particle interactions and thereby enhancing the ER effect. The study provides an approach to the enhancement of the cold flowability improvement efficacy by electric treatment for waxy crude oil.

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments, and the velocities and pressure drops data labeled by the corresponding flow regimes are collected. Combined with the flow regimes data of other GLCC positions from other literatures in existence, the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes. The choosing of input data types takes the availability of data for practical industry fields into consideration, and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification, including the typical ensemble models, SVM, KNN, Bayesian Model and MLP. The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning. Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more. For the pressure drops as the input of each algorithm, it is not the suitable as gas and liquid velocities, and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately. The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes, the flow regimes map, and the principles of algorithms. The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.