New multiphase flow measurements for slug control.

Abstract

Severe slug flow is undesirable in offshore oil production systems, particularly for late-life fields. Active control through choking is one of the effective approaches to mitigating/controlling severe slug flow in oil production pipeline-riser systems. However, existing active slug control systems may limit oil production due to overchoking. Another problem in most active control systems is their dependency on information obtained from subsea measurements such as riser base pressure for active slug flow control. Both of these control challenges have been satisfactorily solved through the introduction of new multiphase flow topside measurements that are reliable and efficient in providing flow information for active slug control systems. By using Venturi multiphase flow topside measurements and Doppler ultrasonic measurements, an active slug flow control system is proposed to suppress severe slug flows without limiting oil production. Experimental and simulated results demonstrate that under active slug control, the proposed system is able not only to suppress slug flow but also to increase oil production compared to manual choking. Another objective of this research was to assess the applicability of continuous-wave Doppler ultrasonic (CWDU) techniques for accurate identification of gas-liquid flow regimes in pipeline-riser systems. Firstly, flow regime classification using the kernel multi-class support-vector machine (SVM) approach from machine learning (ML) was investigated. For a successful industrial application of this approach, the feasibility of conducting principal component analysis (PCA) for visualising the information from intrinsic flow regime features in two-dimensional space was also investigated. The classifier attained 84.6% accuracy on test samples and 85.7% accuracy on training samples. This approach showed the success of the CWDU, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which would be possible for industrial application. Secondly, an approach that classifies the flow regime by means of a neural network operating on extracted features from the flow’s ultrasonic signals using either discrete wavelet transform (DWT) or power spectral density (PSD) was proposed. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thereby showing the superiority of the DWT in feature extraction of flow regime classification. This approach demonstrates the employment of a neural network and DWT for flow regime identification in industrial applications, using CWDU. The scheme has significant advantages over other techniques in that it uses a non-radioactive and non-intrusive sensor. The two investigated methods for gas-liquid two-phase flow regime identification appear to be the first known successful attempts to objectively identify gas-liquid flow regimes in an S-shape riser using CWDU. The CWDU approaches for flow regime classification on pipeline-riser systems were successful and proved possible in industrial applications.