Self-adaptive early warning of undesirable gas-liquid flow pattern in offshore oil and gas pipeline-riser system

Abstract

It is an important function of submarine pipeline flow assurance system to monitor undesirable gas-liquid flow patterns in pipeline in real time, and release early warning ahead of the formation of these flow patterns. In order to tackle the problem of existing method that requires a large training set, and A method of self-adaptive early warning of transition to unstable regime is developed based on transient experiment of gas-liquid two-phase flow performed on a 150-m-long laboratorial pipeline-riser system. The responses of different differential pressure signals were investigated first, so as to pick out the optimum ones used for early warning. Then, the long-short time memory (LSTM) neural network combined with empirical mode decomposition (EMD) was adopted to judge transient condition. In the next step, a series of EMD-LSTM models were trained in order to predict whether undesirable flow pattern would occur, and the warning would be released if the result was ‘yes’. Within the range of experiments, all transient cases were correctly judged. The principles of proposed method were then tested and verified by data from a longer laboratorial pipeline-riser system and an offshore oil field, indicating good adaptability of the method.