Public data standards such as PPDM, developed by the Professional Petroleum Data Management Association, are crucial for the management of Exploration & Production (E&P) data. Our experience as data migration specialists working with PPDM has shown us the importance of data standards, but also thrown up challenges in both moving to PPDM for the first time and transitioning between different versions of the PPDM model. This blog post explores the reasons for the growing complications of E&P data management, while next week we\u2019ll take a look at a methodology-based solution.<\/p>\n
In the PPDM context, we have noticed two factors that have been key drivers of change. These are data explosion and data interpretation lineage.<\/p>\n
E&P data explosion<\/strong><\/p>\n We live in an era of unprecedented change. This means that new devices are being used in every phase of E&P, gathering more data with which better decisions can be made. Timescales are also collapsing. Once, drilling and logging data were distinct activities separated by days, but now they happen simultaneously. It is not just that new devices return more terabytes of raw data; this data is also more sophisticated than was previously possible.<\/p>\n Metadata (in the Dublin core and ISO 19115 sense) are becoming ever more important in providing context. This has a direct impact on proprietary database design and functionality. It is also being factored into PPDM model development and this is why PPDM will evolve to promote adoption and use. The price of progress is growing data complexity.<\/p>\n Interpretation lineage<\/strong><\/p>\n There is also something unique about E&P, in that the assets remain hidden deep underground. There is no certainty that a prospect is viable before it is drilled, and massive investment decisions are made on interpretations of current and historic data.<\/p>\n Professional interpreters work with raw data to derive increasingly valuable knowledge that underpins how decisions are made. This is intrinsically complex because most interpretation is based on previous interpretation, which can be wrong. To complicate matters further, theories of interpretation also change. So PPDM must not just store raw data, it must also store the accumulated knowledge.<\/p>\n Tracking the history of previous interpretations is also essential and we must record:<\/p>\n Data governance, MDM initiatives and chains of custody all want answers to the ‘when, what, how, who, why’ question. This can identify inconsistencies and indicate where errors of judgment or poor data were used.<\/p>\n To provide such answers, data models develop and a time series data ownership graph becomes crucial. If we don’t understand this process, complexity is buried; and we can\u2019t fix problems we can\u2019t see.<\/p>\n A PPDM example of complexity driven by change<\/strong><\/p>\n This example is based on recent experiences building a PPDM data connector for a major client in the Oil & Gas industry, while investigating migration from earlier versions of PPDM. We will use the example of check shot surveys and how the data is represented in PPDM.<\/p>\n\n
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