Engineering tools allowing pressure gradient calculation in the pipe segment commonly use stationary correlation and mechanistic models such as Beggs and Brill, Ansary, etc. This is well known and convenient way which gives rough estimate of pressure gradient due to friction losses and liquid phase interference. It avoids solving complex dynamic pressure equation and derives quick results with comfortable precision margin for large scale systems, such as horizontal pipes and wells. In order to enlarge the applicability zone and accuracy of existing methods, a new method of pressure gradient definition is evolved. It is included three surrogate models that are based on Machine Learning (ML) algorithms. The first model predicts liquid holdup in the segment, the second defines flow pattern and the third predicts pressure gradient. In order to create these models, several ML algorithms are applied such as Random Forest, Gradient Boosting, Support Vector Machine and Artificial Neuron Network. Involvement of the latest machine learning algorithms will allow applying this method to wider range of input data compared with standard multiphase flow correlations and mechanistic models. The proposed method demonstrates high accuracy - on the collected experimental data set it gives R 2 = 0.985 for pressure gradient prediction. That is why it could help to carry out correct calculation of bottom hole pressure and pressure distribution along the length of the pipeline.
|Journal||IOP Conference Series: Earth and Environmental Science|
|Publication status||Published - 30 Oct 2018|
|Event||5th All-Russian Conference with International Participation on Polar Mechanics 2018 - Novosibirsk, Akademgorodok, Russian Federation|
Duration: 9 Oct 2018 → 11 Oct 2018