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Data, models, workflow and change management - Halliburton

Friday, February 19, 2016

To get a production improvement system working requires a mix of data, models, workflow and change management. Sergio Sama Rubio, Industry Solutions Advisor with Halliburton, explained how to do it.

The four key components of developing a production optimisation system are data, models, workflows and change management, said Sergio Sama Rubio, Industry Solutions Advisor with Halliburton.

'If we only take care of three of them it doesn't work.'

The ultimate aim is 'best in class operational performance',' he said. In most cases, companies could see a one per cent improvement in production without additional capital investment.

If they could get a better understanding of the behaviour of the processing facility, so they could navigate the constraints better, then 'we could be producing more.'

Consider the US refining industry. Between 1990 and 2000, the overall capacity of refining stayed roughly constant, but the utilisation increased from 85 to 95 per cent.

'Refineries in the 90s got better and better at using existing installed capacity,' he said. 'I think it's fair to recognise that these guys managed to squeeze the last drop of capacity.

'There is a lot we can learn from refineries. Try to extract the lessons learned and apply the same lessons to our world.'

Data

Starting with data, there are many different aspects of data to consider - the quantity of data, is it covering the right aspects, time dimension (for example data for a historian), space dimension (which part of the asset or fields does it relate to), is it the right type of data.

'There are significant savings in from making sure we really capturing the data that will help us act better,' he said. This means 'capturing only the data that is relevant for my decision making, not capturing every single pressure, temperate and flowrate.'

We should ask, 'Do I have the data, when I need it, in the time I have allocated?' he said.

Sometimes people in your organisations object to a way of working, saying 'you don't touch my data', he said. 'This could be solved with clear guidelines, for example saying 'you can view my data but you cannot modify my data.''

Data can be managed with a 'federated' system, with the applications processing the data working directly with the source data, or with an intermediate 'operational data store', which all of the data is copied to, and made available to the various applications from there.

Models and workflows

Workflows mean having a structured way of doing certain tasks which are commonly done, such as well integrity management, steam flood, artificial lift optimisation, well performance, regulatory production reporting.

Some workflows are more complex, such as field development planning and artificial lift planning.

These workflows are about understanding what has happened in the past or what is happening today, or what will happen in the future.

By developing a structured workflow, you can make sure work is done in a standardised way, and also encapsulate the knowledge of the company's top experts, including people who have left the company.

A workflow will say, for example, after gentleman A has done a certain thing it goes to gentleman B.

Workflow is sometimes more structured or formalised than others, For example it could be a system where you decide to send a document to a colleague to see if she has anything to add.

A good workflow provides 'a natural division of labour, things that people do well and things computers do well,' he said.

'Humans are good at strategy, designs and choices, objectives and decisions. Machines are good at repetitive tasks, running models, optimising, and training.'

'Humans are able to see trends, see in a more creative manner. Computers will follow algorithms,' he said.

To take an example from the use of computers in chess, 'every chess player will agree that the combination of a human plus a chess computer program are absolutely unbeatable,' he said. 'It gives birth to a new discipline - advanced chess.'

Change management

The final step is change management, getting to the point where people are comfortable and in the habit of using the systems. A failure here has caused the failure of many projects.

It is often helpful to do a 'proof of concept' first, demonstrating to people the value that the new system can provide, before starting to implement it, he said.

For example, you could build a workflow which is used on just 10 wells. If the business can see a gain from it, the scope can be extended.

'Build a business case to convince management that this system will be able to increase uptime by 3 per cent,' he said.

When implementing workflows it might be easiest to start with regulatory compliance, because these are tasks that the company needs to do.

Or you can look at areas critical to continued operations. 'You say, 'Mr CEO, do you want the company to keep operating? Then we need a well integrity system.' But do it in a way that it is the first step in a longer journey.'

You might also want to continually revise your workflows. One Halliburton project has been through nine iterations of workflow since the first release in July 2008.

The systems need to be able to be used by people with a wide range of technical competence, he said. 'We cannot expect that everybody in our company has a PhD in geology or petroleum engineering.'

'We need to make these tools available for operators and engineers,' he said. 'They have different needs.'

A common problem with change management is organisational fatigue. 'Many companies launch initiatives, they say, 'we need to start with the data, we are going to do it in the house in a proper way.' By the time the three years have gone past, management has changed priorities.'

'Some companies are notorious in that the G+G people, production people and surface people don't talk to each other,' he said. 'This is one of the bottlenecks in our industry. Don't let siloes get in the way.



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