Online Journal for E&P Geoscientists

Wei, Shixin ^*1; Li, David N.¹; Zagzoog, Mohammed A.¹; Mashouq, Omar ¹
(1) Exploration Technical Services, Saudi Aramco, Dhahran, Saudi Arabia.

Data Quality Management (DQM) provides the ability to assess, analyze, correct, and synchronize E&P data in the corporate and project databases. DQM also provides the capability to measure the data quality quantitatively based on the Six Sigma principle, the quality assurance business management strategy originally developed by the manufacturing industry, and to improve data quality continuously by correction and synchronization.

This case study focuses on the experience and the lessons learned from the implementation of DQM for well data in Saudi Aramco. Using the Six Sigma DQM implementation, the overall quality of the well data in terms of completeness, accuracy, uniqueness and validity is measured regularly, and data corrections and synchronization are highly automated. The average Six Sigma scores for well headers, deviation surveys and wellpaths in the corporate databases and the software application projects, have reached and exceeded, the 4.0 sigma levels. This is equivalent to having 99+% data correctness.

In general, prior to implementing the DQM process, data quality concerns called “friction points” should be identified between the data managers and the geoscientists. DQM rules should also be jointly defined, to ensure problems with data quality issues are identified and corrected. In addition, it is essential to customize the adapters for the corporate and project databases, and to enhance the adapters for different workflows.

The successful implementation of DQM relies on the commitment and collaborative efforts between the data managers and geoscience professionals, as well as the customization of DQM processes during the deployment of the solution. DQM is also a proactive continual process, not a one-time database cleanup event. Running the assessment and correction tasks on a regular basis is the key to ensuring high quality data and maintaining the geoscientist’s trust of the E&P data.