Understanding Well Events with Machine Learning

Abstract

The key to successful planning of well interventions and other well actions is to understand the current state and the history of the well. Due to the large spread of telemetry systems with high-frequency (up to 1 measurement per second) measurement of parameters, it is possible to use machine learning methods for well events recognition. In this paper we consider well analysis with, aim to identify equipment failures and other influences affecting the behavior of wells. Typically, several parameters are recorded at the wellhead with high frequency: wellhead and bottom-hole pressure and temperature, flow line pressure and temperature. Also, readings of downhole measuring devices and well logs are periodically made and recorded. The readings of well parameters can be influenced by many factors: manual manipulations on the well, changes in the composition of the produced products, well integrity issues and others. This work suggests an approach that allows to identify and classify events at the well. The approach is based on the results of constructed synthetic dynamic models of wells and observation of the real behavior of wells. It allows to identify the behavior of individual measured parameters and classify events using all measured parameters in aggregate. The proposed algorithm allows retrospective analysis of data and identification of different events, such as well tests that occurred in the past. The algorithm also allows the analysis of incoming data and identification of well events in real time. Retrospective analysis of the data was useful not only for detecting anomalies and malfunctions, but also for building a real log of events at the well, monitoring well interventions and building reports on well performance. The analysis of event records demonstrates that only minor part of well events is normally captured in central databases. The developed algorithm for natural flowing wells can be easily extended to wells equipped with mechanized oil production systems. For example, for wells with a gaslift or ESP installation. The algorithm can be easily integrated into corporate monitoring systems as an auxiliary tool.