Multilabel Classification for Inflow Profile Monitoring

P. 177–184.

Ignatov D. I., Spesivtsev P., Kurgansky D., Vrabie I., Elizarov S.

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: временные ряды, time series, Multi-phase flow, Bottomhole pressure, multilabel classification, предсказание забойного давления, множественная классификация, многофазное течение

In book

Proceedings of the MACSPro Workshop 2019

Vol. 2478: CEUR Workshop Proceedings. , CEUR-WS.org, 2019.