Online Journal for E&P Geoscientists

Li, David N.^*1; Wei, Adam S.¹; Mashouq, Omar ¹; Zagzoog, Mohammed A.¹
(1) ETSD, Saudi Aramco, Dhahran, Saudi Arabia.

Data quality, as defined here, refers to the degree of excellence exhibited by the fundamental dimensions of completeness, correctness, consistency, and timeliness for the concerned data. Availability of high quality exploration data is critical for geoscientists, engineers, and management, enabling them to explore for, and produce oil and gas resources. The Exploration Data Management Division at Saudi Aramco is responsible for ensuring timely capture and delivery of quality assured data to the corporate data repositories as well as the Geological and Geophysical project databases. This means that data managers must ensure timely data delivery and data consistency across multiple data repositories from the source to the data consumers.

To ensure data of the highest quality and integrity, Saudi Aramco has deployed Data Quality Management (DQM) processes for its corporate well data, including: (1) monitoring the capture of the data from the data producers to the corporate data repositories; (2) auto-detecting new data availability in the corporate database and delivering these new, or updated, data to the clients’ project data repositories; (3) data consistency checking and synchronization between data repositories; and (4) deploying Six Sigma DQM methodology, originally developed by the manufacturing industry and recently adopted in E&P data management.

The successful deployment of Six Sigma DQM processes for Saudi Aramco’s well data has significantly improved the data quality for some data types, such as well headers and directional surveys. These DQM processes also provide the capability to assess the well data quality based on each of the quality dimensions. The direct results of enhanced data quality are mainly twofold: reduced cost of finding data and improved data management efficiency.

This paper presents Saudi Aramco’s current DQM best practices. Several major challenges are addressed as well, including the diverse well data types, complex data workflows and disparate data repositories. Significant effort and allocation of data management resources are required to tackle these challenges, many of which are issues faced industry-wide.