Around 35 turned out last month for the 1st PPDM European User Group, hosted by BP at its Sunbury, UK location. PPDM CEO Trudy Curtis kicked off the proceedings with an insightful analysis of the current situation regarding upstream master data management (MDM). Companies are faced with the challenge of multiple lists of ‘wells’ that use widely differing definitions. Such differences make it nigh impossible to work across subsurface, drilling production and finances. Each domain only lists ‘owned’ wells. Company lists are thus incomplete, overlapping and use ‘competing and conflicting’ names. When you do try to pull everything together, you discover that folks are talking different ‘stuff.’ This is the classic MDM problem that sparked off PPDM’s ‘What is a Well?’ (WIAW) workgroup, which kicked off in 2008. To demonstrate the nature of the WIAW challenge, Curtis set attendees a short practical exercise involving various combinations of producing intervals, lateral boreholes and completions. The results showed that beyond the simplest configurations, there was little consensus on exactly how the various geometries and completion configurations should be named. Curtis observed that ‘Some come to fisticuffs over such issues! In general, as an industry, we don’t agree on these fundamental revenue generating objects.’
The WIAW group is working to define terms, eliminate ambiguity and duplication by mapping usage across applications, and by figuring out how to address gaps and overlaps. The idea is to create a WIAW/MDM ‘Rosetta stone.’ Anadarko, BP, Chesapeake, Chevron, ConocoPhillips, Hess and Nexen were involved in WIAW Phase I and now have ‘baseline definitions’ in an Adobe Flash application on whatisawell.org. These include regulatory constraints on naming for the US, Canada and Australia. PPDM is keen to do the UK and Norway next. All WIAW is mapped to PPDM 3.8.
Fred Kunzinger (Hess) and Shannon Tassin (Noah Consulting) gave back to back presentations on how Hess has leveraged the WIAW work to ‘raise the bar’ on data management—transforming it from a ‘necessary evil’ to a critical factor in Hess’ success. Hess has 4 different well log data bases and ‘no money to merge them—despite multi-million logging jobs in a single GOM well!’ Hess upstream mission statement includes ‘standard processes, practices and technology’ to support its global business’ and ‘credible data for fact-based decisions.’ Hess engaged Noah Consulting to develop a technical information lifecycle (TIL) strategy before initiating a structured data governance approach. Hess spent 14 months on an off-the-shelf solution from Schlumberger and Halliburton but this ‘did not fit with what we were trying to do.’ In the end a bespoke solution was developed around PPDM and Volant’s EnerConnect middleware.
The solution, a ‘technically validated database’ (TVDB), uses the PPDM 3.8 data model and is now the hub of a constellation of application software including Hess’ Well Master (on PPDM 3.7), SDE Spatial, GeoQuest, Paradigm and Petrel. Validation is assured by assigning authoritative sources, standard naming conventions, data ownership, roles and responsibilities—all with documented procedures. Consistency with Hess’ standard technical architecture is ‘where battle lines are drawn!’ Kunzinger wound up by warning of possible confusion between the ‘single source of data’ approach and the TVDB. The latter embeds the former but crucially adds data governance.
Host Gavin Goodland noted that some of BP’s data acquisition categories cost ‘in excess of a billion dollars per year.’ The data thus acquired is carried as an asset and it is possible to calculate the cost of not managing data in terms of data asset degradation, time lost/wasted and e-discovery costs (BP is sued several times per year in the US).
BP carried out some competitor analysis on data management in the upstream by comparing return on investment with how centralized the data role was. The result was a ‘U’ curve with companies with a clearly centralized or distributed approach doing well while companies with a ‘wishy-washy’ approach performing poorly.
BP units are increasingly following standard processes, as laid down in a ‘Standard IT Bill of technology’ that includes a BP operating model for data management. Data governance includes ‘smart standards,’ i.e. ‘you don’t have to standardize everything—particularly business processes.’ BP initially ‘wrestled’ with data management. Is it an IT or business function? In fact it is both! Technology and tools represent a small part of the equation and are very subsidiary to performance management inter alia. BP outsources much of its data management. Key elements of data governance include genuine authority to take decisions, visible executive-level support, and a business that owns and accounts for its data. BP has instigated data governance boards with representation from all business segments. On the topic of standards Goodland observed that ‘there are many and there is a temptation to drink in all the standards’ saloons.’ Often companies may participate in a standards effort but the benefits may be unclear. With help from Matthew West, BP has mapped the standards landscape across activities and domains and filtered down the potential watering holes. These include PPDM (Goodland is now on the PPDM board), various ISO standards including 15926, PODS, Energistics and others. What will actually be officialized is as yet undecided. The plan is to ‘harvest’ short term potential and later, to identify gaps and overlaps where harmonization can take place.
Matthew West (Information Junction and ISO 15926 luminary) described a ‘comprehensive approach to information quality,’ noting that managing data and documents represented essentially the same problem set. West enumerated some information quality (IQ) myths as follows.
1) IQ is hard. Not true—there is no intellectual challenge. IQ is about attention to detail—the problem is that it is not generally done and companies prefer to ‘reconcile’ data with armies of accountants.
2) IQ adds costs. Not true it is not about adding checks, it is about getting it right first time.
3) IQ is about being more accurate. No, just fit for purpose. It is the lack of quality that adds costs—through having to ‘fix’ bad data or by making bad decisions.
In the end, quality is about meeting customer requirements—although these may involve educating the customer as to the risk of poor data quality. West recommends use of the ISO 9001 product quality standard since, ‘information is just another product.’ Quality needs to be built in to an Enterprise Architecture since ‘all IT is about information.’ More from ppdm.org.
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