A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.

No abstract

This contribution presents a comprehensive methodology for the real-time estimation of the rain intensity from downlink satellite signals. The enhanced system leverages on Extremely Randomized Trees Classifiers to automatically perform rainfall detection along Earth-satellite links and successively employs an improved procedure to determine the corresponding slant-path rain attenuation. The latter quantity is then exploited to yield realtime rainfall rate estimates with 1-minute time resolution. The accuracy of the proposed methodology is tested using Ka and Q band propagation data, collected in two different sites (Milan and Madrid) and in the framework of the propagation experiments. The results demonstrate the reliability of the automated rain event detector, as well as a satisfactory accuracy in estimating the slant-path rain attenuation and the point rainfall rate. The accuracy is assessed both on a statistical and on an instantaneous basis through the evaluation of different error figures and by inspection of individual time series.

Summary The formation of deposits is a very common issue in oil and gas pipeline transportation systems. Such sediments, mainly wax and paraffine for crude oil, or hydrates and water for gas, progressively reduce the free cross-sectional area of the pipe, leading in some cases to the complete occlusion of the conduit. The overall result is a decrease in the transportation performance, with negative economic, environmental, and safety consequences. To prevent this issue, the amount of inner deposits must be continuously and accurately monitored, such that the corresponding cleaning procedures can be performed when necessary. Currently, the former operation is still dictated by best-practice rules pertaining to preventive or reactive approaches, yet the demand from the industry is for predictive solutions that can be deployed online for real-time monitoring applications. The paper moves toward this direction by presenting a machine learning methodology that leverages pressure measurements to perform online monitoring of the inner deposits in crude oil trunklines. The key point is that the attenuation of pressure transients within the fluid is dependent on the free cross-sectional area of the pipe. Pressure signals, collected from two or more distinct locations along a pipeline, can therefore be exploited to estimate and track in real time the presence and thickness of the deposits. Several statistical indicators, derived from the attenuation of such pressure transients between adjacent acquisition points, are fed to a data-driven regression algorithm that automatically outputs a numeric indicator representing the amount of inner pipe debris. The procedure is applied to the pressure measurements collected for one and a half years on discrete points at a relative distance of 40 and 60 km along an oil pipeline in Italy (100 km length, 16-in. inner diameter pipes). The availability of historical data prepipe and postpipe cleaning campaigns further enriches the proposed data-driven approach. Experimental results demonstrate that the proposed predictive monitoring strategy is capable of tracking the conditions of the entire conduit and of individual pipeline sections, thus determining which portion of the line is subject to the highest occlusion levels. In addition, our methodology allows for real-time acquisition and processing of data, thus enabling the opportunity for online monitoring. Prediction accuracy is assessed by evaluating the typical metrics used in the statistical analysis of regression problems.

No abstract

No abstract

In recent years, big data technologies have paved the way for digital transformation in oil and gas industry. Multi-domain measurements are collected by advanced sensor systems and processed using data-driven approaches, allowing to derive constitutive relations between the operational status of the asset and the measured variables. In addition, historical pressure measurements can be exploited for advanced pipeline monitoring. This paper presents a methodology, applied to a case history, where legacy data are repurposed and employed both to track pump health and to enhance the digital conversion. The dataset consists of past pressure signals collected by Eni for several years at the pumping terminal of a crude oil transportation pipeline, which has a length of 100 km and 16" diameter pipes, located in Italy. Pressure transients' variance, kurtosis and variation range, computed on appropriate window lengths, are fed to an unsupervised clustering procedure based on a Gaussian Mixture Model (GMM), which automatically identifies four clusters. An expert analysis of the labeled data reveals that each cluster corresponds to a well-defined and different pump operational mode, namely: standby (pumps off), transition (pumps switching on/off), normal (line flowing) and anomalous. The latter mode is connected to a high value in the pressure transients' variance and kurtosis: during such regime, pump maintenance logs report a failure and replacement of a system part. Interestingly, the anomalous condition starts to show up several days before the actual part replacement. The proposed case history reveals the potentiality of: adding value to legacy data, as they can be reprocessed, tagged and used as supervised examples in the training phase of new data-driven procedures; comparing, merging and complementing monitoring strategies of assets at different digitalization stages; aiding the development of predictive maintenance strategies.

No abstract