Multilabel Classification for Inflow Profile Monitoring ⋆ Dmitry I. Ignatov 1[0000-0002-6584-8534] dignatov@hse.ru, Pavel Spesivtsev 2 PSpesivtsev@slb.com, Dmitry Kurgansky 1 mykurgansky@mail.ru, Ivan Vrabie 1,2 vrabie93@mail.ru, Svyatoslav Elizarov 1 sorkerrer@gmail.com, and Vladimir Zyuzin 2,3 VZyuzin@slb.com 1 National Research University Higher School of Economics, Moscow, Russia 2 Schlumberger Moscow Research, Moscow, Russia 3 Moscow Institute of Physics and Technology, Moscow, Russia Abstract. The purpose of this study is to identify the position of non- performing inflow zones (sources) in a wellbore by means of machine learning techniques. The training data are obtained using the transient multiphase simulators and represented as the following time-series: bottom- hole pressure, well-head pressure, flowrates of gas, oil, and water along with a target vector of size N, where each element is a binary variable indicating the productivity of the respective inflow zone. The goal is to predict the target vector of active and non-active inflow sources given the surface parameters for an unseen well. A variety of machine learning techniques has been applied to solve this task including feature extrac- tion and generation, dimensionality reduction, ensembles and cascades of learning algorithms, and deep learning. The results of the study can be used to provide more efficient and accurate monitoring of gas and oil production and informed decision making. Keywords: Multi-phase flow, multilabel classification, time series, bot- tomhole pressure 1 Introduction During the production phase of oil and gas wells it often happens that oil does not enter every inflow point, which leads to a decrease in the efficiency of the operation and undesired economic consequences 4 . It is beneficial to determine which of the inflow points are inactive to properly design the intervention opera- tions. The main research hypothesis here is as follows: using the machine learning approaches, the active and non-active inflow points can be predicted based on the measurements of certain parameters at the wellhead, including pressure and total gas and oil productivity. ⋆ Copyright c  2019 for this paper by its authors. Use permitted under Creative Com- mons License Attribution 4.0 International (CC BY 4.0). 4 This paper continues our research “Development of Data Analytics Algorithms for Predicting the Parameters of Oil and Gas Well Flows” [8,5]