A Comprehensive Approach to Measure the RealTime Data Quality Using Key Performance Indicators

Abstract

After the setup of a real-time drilling data feed from the Rig site to the operations center, the obvious question is - How good is the data that is being streamed? Real-Time rig sensor data could be an effective input for drilling optimization, However, the confidence in the results of these analysis or interpretations is directly associated to the trustworthiness of the data acquired. Measuring the quality of the data has been a difficult issue for many years. This paper shows the approach used by a large oil & gas operator to measure the quality of the information received from the rig providers, which is obtained from the sensors installed on every rig. The Real Time Data Quality is measured by analyzing six different parameters: completeness, uniformity, sensibility, resolution, structure and format. The combination of these six indexes provides a deep understanding of the level of the quality of the data received, as well as the elements to be improved. Various KPIs including Footage KPI, Data QC Availability and Data QC Channels, are generated by the powerful engine of Automated Rig Activity Measurement (ARAM) to check the data quality of different vendors. Starting from having a check on data availability, these KPI's are capable of analyzing every row of time based data in real-time. Data generated by a proprietary system for these KPI's is also being used in a Real Time Data Quality Dashboard. This measurement provides the drilling engineers with a systematic way to filter analysis results based on data of poor quality by specifying their acceptable data quality range. This daily analysis also allows the operator to identify Rigs that are performing better and rigs that needs to be pushed for higher quality data.

A Novel Approach for Enhancing Drilling Efficiency Through Flat Time Optimization

“…These KPIs are utilized within a Real-Time Data Quality Dashboard, allowing drilling engineers to filter analysis results based on acceptable data quality ranges. This systematic methodology facilitates the identification of high-performing rigs and those in need of improvement [8] Javed et.al highlighted the critical role of flat-time operations in oil and gas well construction, specifically focusing on the Blowout Preventer (BOP) handling to control costs and enhance safety. Through a case study of 14 wells in Malaysia, it was found that BOP-related activities significantly contribute to overall flat time.…”

A Game-Theoretic Approach to Improve Energy-Related Data

“… 10 The rig site practices propose to add/develop more sensors 7 , 11 and to standardize the transmission and text abbreviation. 12 The administrative approaches propose using key performance indicators (KPIs) to evaluate the quality of data 12 and to develop a data quality dashboard, 13 real-time data quality center, 12 , 14 and others. The method approaches apply a manual cleansing process, 7 remove bias, 15 remove outliers, 16 correlate the data together, 17 smooth the data set, 7 and others.…”

A Metric and Visualization of Completeness in Multi-Dimensional Data Sets of Sensor and Actuator Data Applied to a Condition Monitoring Use Case

“…Besides the literature defining metrics for completeness, further research is performed using and evaluating the introduced metrics for sensor and actuator data. For example, Otalvora et al, 2016 [41] use task-independent record completeness to assess the data quality of sensor data from drilling rigs. However, on record level, the task-dependent attribute is limited to concerns regarding the amount of data.…”