Summary
Accurately predicting wax deposits in a crude pipeline through empirical formulas or numerical modeling is unreliable because of the incomplete mechanism and the time-dependent unsteady actual operating conditions. With the help of the data collected by the supervisory control and data acquisition system of pipelines, wax deposit prediction is made possible by developing the time-dependent data mining method. In this article, the data from a typical long-distance crude pipeline in China operating over a 4-year time period was investigated. The inlet temperature prediction was first conducted by developing the long short-term memory (LSTM)-recurrent neural networks (RNNs) model, during which the feature sequencing, overfitting problems, and optimal hyperparameters were fully considered. Because of the time sequence cell, the accuracy of the LSTM-RNN model, as well as the time consumption, is much better than the RNN model when dealing with a great deal of data over a long period of time. Taking the inlet temperature prediction results as input features, the prediction model of average wax deposit thickness was established based on the backpropagation (BP) neural network and optimized by the particle swarm optimization (PSO), chaos particle swarm optimization (CPSO), and adaptive chaos particle swarm optimization (ACPSO) algorithms. The conclusions and associated algorithm from this article help to determine the reasonable pigging circle of long-distance pipelines practically. It could also be applied to guide the wax deposit prediction in the wellbore or oil-gatheringpipes.
Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples
