The trend in many mature basins like the North Sea, both UK and Norway, has been one of steady divestment by the supermajors and resurgence by a new generation of smaller oil and gas operators.  Through aggressive acquisition of dozens of small and medium size fields, these younger challenger operators are building an exciting future for these mature basins and indeed the sector at large.

Some, like Harbour Energy, have grown fast through acquisition and are already delivering production figures in the hundreds of thousands barrels of oil equivalent per day; more indeed than some of the supermajors still in the basin.

But these younger operators face a number of challenges.  Not least because the supermajors who originally developed the assets, followed different field development practices and operational strategies.

They installed – and in many cases designed and built – their own systems and applications. The result is the new generation of operators has been left with complex, disconnected and heavily bespoke systems and data structures for each of the assets in their portfolios.

So how can these operators take advantage of potential economies of scale across their growing asset portfolios without breaking the bank?  Especially when stopping to create a clean sheet is not an option.

Over the past fifteen years, we have helped a number of fast-growing operators, including some of those now beginning to dominate the North Sea, as they seek to integrate their portfolios. Below we summarise a few lessons we have learnt over the years in tackling this challenge:

1. Don’t lift and shift: it’s tempting, faced with the challenge of integrating data from multiple applications and systems, from multiple assets, to lift and shift all your data into a single place, like a data lake, and believe everything will be simpler.

The proposition is compelling: copy all the data to the lake, normalise the context and ontology, and have all your data in arm’s reach, guaranteeing fast      access, no matter the query complexity.  But operators who have gone down this route, tell us the reality is far less compelling: “more of a stagnant pond   than a dynamic lake” one told us.

The truth is you can’t build a reliable, high-fidelity data repository for something as dynamic as live operations, particularly in a complex domain like oil and gas. Certainly not coupled with an aggressive acquisition strategy, when the goal posts are moving all the time.  The first time data quality  from the lake is questioned, confidence quickly fades and things begin to unravel. Nothing replaces the original systems of record for data fidelity.

2. Connect data dynamically: millions of years of human evolution have shown how knowledge is acquired and processed via networks that can scale without boundaries – not by trying – and failing – to define those boundaries up front.

A combination of cloud and knowledge graph technology makes this possible and lends itself well to operators with dynamic asset portfolios needing to capture value fast.   No data is copied; all data remains in the original systems        of record, fully featured and rich with metadata. The knowledge graph surfaces the context and the standardised ontology, so that applications can reference objects in a standard way.

At Eigen, we use Neo4j, the leading open-source knowledge graph technology. We can get up and running in a few hours to link to an operator’s data from multiple sources to build analyses, automated workflows, reports, alerts etc. The result is a shorter time to integration and a shorter time to value, particularly when delivered use-case by use-case.

3. Give power to the user: In today’s world, data comes in the form of real time process data, relational databases, documents, series data  embedded in Excel sheets and PDFs, API feeds, and many other forms. Users can quickly become overloaded by different vendor front-ends to these data, most of which don’t talk to one another.

Instead, demand system-agnostic front-end tools that can communicate with     the data abstraction layer to seamlessly access and combine data from different data sources, including real time, to enable collaboration, co-editing       of content and the capacity to support a growing industrial-scale operation.

Fast growing operators may naturally feel overwhelmed by the challenge of integrating diverse and complex asset portfolios, without hemorrhaging cash.  But technology solutions, like knowledge graphs, cloud and system-agnostic visualisation tools, are at hand that we could only dream of a few years back.  And they cost much less today, with the armoury of open-source options available.

Technology offers choice without lock-ins and without losing control. And this benefits the supermajor divesting its portfolio just as much as the fast-growing operator on the acquisition trail.