Intelligent Data Quality Control of Real-time Rig Data

Abstract

Abstract There is an increasing requirement for centralized analysis of drilling data to improve performance. Typically, data from service companies is transmitted from the rig to the clients Real Time Operating Centre (RTOC). A major problem is the quality of this data, which can lead to incorrect analysis and a breakdown in trust between the rig and the RTOC. Currently real time data is, at best, only checked for completeness. In this work, we present a model for data quality control in real-time operating centres. The following points are considered in our suggested model: Monitoring the quality of real time data for:CompletenessContinuityTimelinessValidityAccuracyConsistencyIntegrityAlarming of all unexpected data;Generating daily data quality reports. The suggested data quality control model has different groups of key performance indicators, calculated in real time from the streamed data. The model then takes the values of these performance indicators as input, and evaluates different properties of data quality as the output. Additionally, a new group of intelligent model-based key performance indicators is suggested. These indicators give the possibility to monitor actual quality and measure it against the expected one. The result is a measurement of Quality of Service (QoS) supplied by data provider at the rig.