Application of Big Data Tools for Unstructured Data Analysis to Improve ESP Operation Efficiency
Authors: Sarapulov N. P. , Khabibullin R. A. 
Published: October 2017

Abstract
The paper is devoted to the assessment of the possibility of implementing Big Data techniques for processing large amount of data to identify cause-effect relationships (patterns) that precede an event and to develop proactive measures to prevent them. To carry out such an assessment, existing sources of information, communication channels, and database structures were studied. Based on the analysis and the pilot project, areas of possible introduction of operational and forecasting analytics were identified. The required volume and structure of the data for analysis of data in the Big Data analytical complexes were evaluated with identification of patterns preceding various emergency situations during ESP operation, such as jamming, overheating, unsuccessful start-up, etc. Recommendations for the ESP operation data collection, storage and processing are developed.
Events chain visualization examples related to emergency ESP shutdowns and unsuccessful starts are shown using the Aster analytical system. Sankey diagrams showing cause-effect relationships distribution are constructed. Analysis of these models has yielded several results. Estimation of the average time for putting the well into the "Normal Work" mode after a shutdown showed that when starting automatically, unlike manual start-up, the time savings can be from 30 minutes to one hour. That is, the proper ESP's control station configuration combined with smooth startup precautions can significantly reduce the equipment downtime and oil production losses.
Analysis of the time series anomalies associated with the ESP motor loading revealed a pattern - starting load exceeded 80% lead to a subsequent system underload, and then - to an emergency stop. Pattern causes analysis revealed that, in low gas content in the wellbore conditions the ESP with an overestimated start-up frequency, quickly removes liquid from the annulus to a critical level, followed by an ESP gas lock and shutdown. Another revealed pattern shows that unsuccessful starts often occur when high-temperature motor protection and low-pressure protection are triggered. The analysis of the ESP's control station settings showed a strong correlation of the start-up problems with the improper ESP's control station settings.
An analytical model of the data during the research made it possible to analyze other phenomena related to malfunctions in the operation of pumps. We are talking about the effect of turbine rotation, which leads to the drainage of the produced liquid from the tubing when the motor is stopped. Since the ESP control station clearly fixes the time of the end of the turbine rotation, it was possible to determine oil losses and build check valves rating.
The use of the big data tools made it possible to formulate and test a set of hypotheses about the causes of emergency shutdowns and failures in the ESP startup associated with improperly configured protections and the PVT properties in control station. Several observations obtained during the experiment considered be to used for programming ESP's control stations.
During the analysis, it was noted that often the separation of different well's data between different groups of specialists or departments creates obstacles for a comprehensive analysis. So, production data and formation properties, downhole equipment operation data, electricity consumption data, hydraulic measurements in the well and on the surface, should ideally be stored in one database and analyzed jointly. Unfortunately, this obvious consideration is often underestimated in practice.