Predicting the Success and Value of E&P Data Standards
Lack of standardization of information technology systems has long been recognized as a cost carried by the industry. One estimate places the total value of such nonstandardization at $40 billion, with the bulk of that tied to improved recovery of reserves. Given the opportunity to capture value, over the years the industry has launched a number of standardization initiatives. However, it is generally recognized that many of these initiatives failed to reach the levels of performance, adoption, or benefit promised. This CERA Decision Brief investigates these initiatives and proposes an empirical model for predicting the success of any newly proposed standard. The production mark-up language standard is used as a test case for the model. We found these three factors to be common to successful standards:
* Timing: early or late. Standards are applied to an underlying technology (well logs, production data systems); those proposed early or late in the life cycle of an underlying technology have a greater chance of adoption. Those proposed during the middle or growth phase of a technology have shown little chance of success.
* Vendor selection frequency: high. In activities where exploration and production companies make very frequent vendor selection choices (drilling rigs, wireline logging, seismic surveying), standardization is more likely to succeed. A low frequency of vendor selection (as for geotechnical platforms or production data acquisition) reduces that probability.
* Architectural approach: data transfer over data model. Initiatives that define a data model or other architectural component tend to have lower rates of achieving broad industry adoption. Initiatives adopting a data transfer approach (reducing the need for modification of internal components by technology providers) have shown a greater chance of success.
