Abstract The maturing of the global oil and gas industry means that information resources must be effectively managed to insure a measurable impact on financial success. As the value of information technology becomes more accepted and analyzed, there is a need for a comparative measure of how organizations have advanced their information management strategies. A data management maturity model has been proposed as a method of establishing this comparison (D'Angelo and Troy, 2000). The model analyzes organizations as they move from a base level to a fully optimized structure in which data, information, and knowledge add value at all stages in a life of field project cycle. Operations are categorized into data management maturity levels by analyzing processes, technology, consistency of results, and ability to quantify value.
Methodology The data management maturity model was originally developed as part of a confidential and anonymous study of 15 North American oil and gas organizations over the period 1997 to 2000. The objective was to tie an organization's maturity level to its finding effectiveness. Based on the observation that up to 50% of geoscientist's time was spent looking for data, managing data, and accessing data rather than looking for oil, the study proposed to link an ability to reduce that time with improved finding and development costs and reduced overhead.
The study established a correlation between a company's position in the maturity model and its success in finding hydrocarbons while benchmarking competitive data management relationships between the participating organizations (Figure 1).
More importantly, it demonstrated that exploration- and production-driven organizations needed to begin migrating toward expert data management technologies, and identified the internal activities and changes required to move ahead in the model. This was one of several initiatives that attempted to understand and measure data management practices, and establish a relationship to improved business results. It led to an eventual improvement in the recognition of the value of integrated data management by providing measurements and identifying processes to bring about increases in that value for offshore and onshore E&P efforts.
Study queries attempted to determine the level of integration and sophistication in interpretation workflows supported by data management practices, successful strategies for reducing prospect generation time and finding costs, barriers to investment and implementation, and examples of measurable improvements. Participants in the survey expected to additionally identify steps required to move progressively up the model for the benefit of shareholders and the bottom line business result.
Results of the study showed comparison of value realized from data management solutions, provided a new analytical framework for comparing data management performance, identified a baseline for each participant for which they could measure yearly progress, and determined potential increases in discounted cash flow rate (DCFR), or return on investment (ROI), resulting from data management implementations.