Oil Flow Data Research Articles

Oil Flow Analysis in the Maritime Silk Road Region Using AIS Data

Monitoring maritime oil flow is important for the security and stability of energy transportation, especially since the “21st Century Maritime Silk Road” (MSR) concept was proposed. The U.S. Energy Information Administration (EIA) provides public annual oil flow data of maritime oil chokepoints, which do not reflect subtle changes. Therefore, we used the automatic identification system (AIS) data from 2014 to 2016 and applied the proposed technical framework to four chokepoints (the straits of Malacca, Hormuz, Bab el-Mandeb, and the Cape of Good Hope) within the MSR region. The deviations and the statistical values of the annual oil flow from the results estimated by the AIS data and the EIA data, as well as the general direction of the oil flow, demonstrate the reliability of the proposed framework. Further, the monthly and seasonal cycles of the oil flows through the four chokepoints differ significantly in terms of the value and trend but generally show an upward trend. Besides, the first trough of the oil flow through the straits of Hormuz and Malacca corresponds with the military activities of the U.S. in 2014, while the second is owing to the outbreak of the Middle East Respiratory Syndrome in 2015.

An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

An accurate estimation of the remaining useful life (RUL) not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance (CBM). For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work, an adaptive-order particle filter (AOPF) prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump’s degradation. With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method.

A New Comprehensive Model for Predicting the Pressure Drop of Flow in the Horizontal Wellbore

Over the past two decades, the modeling of flow in a perforated pipe with influx through wall openings has been recognized as a key topic especially in the field of horizontal wells. In this paper, based on the theoretical analysis and previous research achievements, combining with the new measured data sets stemming from the large-scale experimental apparatus designed and constructed recently at China University of Petroleum (CUP), a new comprehensive model has been developed for the prediction of pressure drop regarding single-phase flow in horizontal perforated pipes with wall influx, in which new correlations for calculating the hydraulic friction factor and momentum correction factor of variable mass flow are given. The presented model is then implemented using the visual basic.net package and validated against two data sets obtained on single-phase water flow and single-phase oil flow. Predictions of the new model and frequently used Ouyang model are also compared based on the new experimental data. Results show that the model given in this article can not only properly represent the complex mechanisms of flow in the horizontal wellbore, such as the resistance caused by wall perforations and the drag reduction or so-called lubrication effect caused by wall injection, but also has a preferable prediction accuracy. Compared with the water flow data and the oil flow data, the absolute average percentage errors of the proposed model are, respectively, 4.5% and 5.0%, which demonstrates better performance and wider application range than Ouyang model.

Incipient Separation of a Supersonic Turbulent Boundary Layer at High Reynolds Numbers

Two-dimensional compression corner and axisymmetric flare geometries were used in this study of shock wave interaction with a compressible turbulent boundary layer. The study was carried out at a Mach number of 2.9 and over a Reynolds number range of 10 5<Re60<107 (Re^up to 10 9). Detailed surface pressure, schlieren, and oil-flow data were obtained for several corner angles. A major finding of this study is that incipient separation is a gradual rather than an abrupt phenomenon. Incipient separation corner angles were found to be within a band of about 16°-18° and essentially independent of Reynolds number over the range studied.