DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS - AYOUB MOHAMMED, MOHAMMED ABDALLA (2011)
Determination of pressure drop in pipeline system is difficult. Conventional methods (empirical correlations and mechanistic methods) were not successful in providing accurate estimate. Artificial Neural Networks and polynomial Group Method of Data Handling techniques had received wide recognition in terms of discovering hidden and highly nonlinear relationships between input and output patterns. The potential of both Artificial Neural Networks (ANN) and Abductory Induction Mechanism (AIM) techniques has been revealed in this study by generating generic models for pressure drop estimation in pipeline systems that carry multiphase fluids (oil, gas, and water) and with wide range of angles of inclination. No past study was found that utilizes both techniques in an attempt to solve this problem. A total number of 335 data sets collected from different Middle Eastern fields have been used in developing the models. The data covered a wide range of variables at different values such as oil rate (2200 to 25000 bbl/d), water rate (up to 8424 bbl/d), angles of inclination (-52 to 208 degrees), length of the pipe (500 to 26700 ft) and gas rate (1078 to 19658 MSCFD). For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. The ANN model has been developed using resilient back-propagation learning algorithm. The purpose for generating another model using the polynomial Group Method of Data Handling technique was to reduce the problem of dimensionality that affects the accuracy of ANN modeling. It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. The best available empirical correlations and mechanistic models adopted by the industry had been tested against the data and the developed models. Graphical and statistical tools had been utilized for comparing the performance of the new models and other empirical correlations and mechanistic models.