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Achieving E&P data quality

Thursday, February 26, 2015

The pathway to high quality E&P data is to work out what data you need, work out how to get there, and then check what you have, says Philip Lesslar of PETRONAS

The key to getting the right upstream data quality is having the right global architecture in place, says Philip Lesslar, Principal Consultant, Regulatory Compliance & Technical Assurance (RCTA), Technical Data, with PETRONAS Exploration and Production,

He was speaking at the Digital Energy Journal conference in Kuala Lumpur on October 13, 'Doing more with Subsurface Data'.

You have to work out what kind of data quality you need, work out how to achieve it, and then work out if you have achieved what you wanted, he said.

'We don't just jump into a project, you've got to plan it a bit, the same as when you build a house,' he said.

Standardise and rationalise

The first step is to 'standardise and rationalise' how you work with data around the company, he said.

You have to define what kind of data quality you actually need, so the data can do the task it is required to do. In other words, define the 'business rules' around data. From that you can then develop your data quality 'metrics'.

You also have to define how much data you need. It is common to find you are working with far more data attributes than you actually need, he said.

'There's thousands and thousands of attributes. But how many attributes do you need to run an E&P company? It's a lot fewer.'

'Your business process doesn't need everything that's out there. The answer is always less than what you have.'

It is also helpful if the company can standardise the software applications it is using, he said.

'Data quality metrics is a lot about people, that's the first step. The second step is how we do it. How do we make sure everyone co-operates in the same way?'


Once you have defined your data metrics, the next step is to see how well data in the company complies with them, he said.

You can build 'data quality tools', which automatically interrogate databases.

You can do simple queries, to see what data is missing, or if it falls in a certain range.

You can also do more sophisticated queries, asking two different databases at once. 'You can combine multiple sets, and combine multiple targets. You can still come up with a single metric.' This is aggregation principle.

You want to be able to run your data quality tools on databases around the company. 'You need a tool with global reach. If your tool can only work in your office environment that's not going to work,' he said.

Looking at it globally, there are some countries which have restrictions on the data which can be exported, he said. If you have data restrictions, you have to run the metrics engine wherever the data is, he said, so you can guarantee that no data is extracted. This is why data architecture is so important in such a project.

This process generates data about how good the data is, which you can then display on a dashboard.

You might want to look at data metrics for different countries, different assets, different projects, or different types of data. You can also find out the trends, which data is getting better and which data is getting worse.

The data metrics are shown with a traffic light system, with green indicating compliance to the standards.

The metrics initiative has got to be 'flexible and scalable,' he said.
'Whatever we do, the whole landscape is changing.'

'A data quality metrics program is not short term. But it is key to make sure there are short term deliverables to maintain the quality and deliverability of the effort,' he said.

Mr Lesslar was asked how he defines data quality, or if data quality can be specified in a range (for example, +/- 10 per cent).

'Data quality is something we all argue about,' he replied. 'But data completeness (whether the data is there or not) is the biggest issue.'

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