This paper provides details on the formulation and execution of the RasGas Company Limited (RasGas) Petroleum Engineering Department's "Data Management Acceleration Project", also known as DMX.

The objective of this project is to install processes that will manage the entire life cycle of subsurface data, from initial capture to utilization. This objective is actually very different from the conventional objective of most data management projects, which seek to capture, QC and load data into a database. The difference between this project and past RasGas projects is due to how this project was originally conceived.

RasGas is a young company which has experienced a more than six-fold growth in the number of wells and an eight-fold increase in gas production since 1999 when production from RasGas' North Field blocks commenced. The amount of subsurface related data generated by this growth has been substantial and requires a wide range of project data stores to handle the diverse data.

These activities required a wide range of specialized applications and databases to support specific technical workflows. These workflows grew organically as the activity grew, with most of the data collection, quality control, and storage processes being very effective, yet ad-hoc. The nature of the workflows required a substantial multi-disciplinary approach.

Most previous RasGas projects tended to concentrate on technology (software applications) and legacy data quality assurance and loading. The focus of the Data Management Acceleration project is on the development of continuous, sustainable processes that handle the data from initial capture through its utilization, with the key criteria being assurance of data quality, and rapid delivery to end users. The technology to be used is of secondary importance.

The project is structured around the treatment of individual data types (e.g. perforations) as "mini-projects". The work breakdown structure for each of these data types is similar, and focused on creating those processes that are the key contributors to the data's life cycle process. This keeps the focus of activity on the desired objective from the project.

The typical project stages normally seen in conventional data management projects are incidental by-products to the creation of these processes, rather than being key deliverables. While most, if not all, of these activities are still performed in the project, they are steps along the way to success, rather than key deliverables.

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