Digital transformation in energy — how does it fit in with the mature asset?

 

In the mature asset, equipment and infrastructure is older, and opportunities for data collection are correspondingly more limited. At the same time, the opportunities are bigger. To extract the hydrocarbons left in place, more precise production execution is required. This includes responding to feedback (production characteristics) quickly. Also, with older equipment, the value of following strategies that optimize reliable flow and production is higher.

 

Value

 

The drive for efficiency, reliability and lower CAPEX is certainly built upon the opportunities presented by technology — and reinforced by the level of automation provided by connectivity and mobility which supports remote operations. And of course it’s capped by the sophistication of fast decision-making provided through automation of knowledge work, by big data analysis techniques and by the breakthrough insights of machine learning as applied to prescriptive maintenance by approaches like Aspen Mtell^®.

 

Upstream asset owners and developers are facing the need to replace production. They also have an urgency to continue to control both CAPEX and OPEX in the development of new assets and expansion of existing ones, and are enticed to do so by the current “mini stability” of oil and gas commodity pricing.

 

The New Asset

 

So, for new assets, the digitalization story is an obvious one. The surface facilities being specified and put in place today are, by their nature, highly “sensored,” and it is not only “one-way” sensors. Much of the major oilfield equipment is being IIoT-enabled, with the capability of connectivity in two directions. Cyber-secure mobile networks, increasingly with the prospect of moving the sophisticated methods and models “close to the edge” (i.e. distributed into the asset locations themselves), reduce the logistics and labor intensity of moving specialized engineering experts around remote assets.  

 

This is the promise of the future of the much more intelligent and automated oil and gas field. The operator is able to rely on intelligent sensors and models to make basic production decisions, leaving the operating staff and technical experts to spend time on higher and strategic levels of optimization from centralized, convenient, collaborative locations.

 

The Older Asset

 

But what about the mature asset? Digital transformation can help a lot.

 

Models online are one example. Rigorous models are now faster, more compact, straightforward to connect to process production data and easier to understand by way of visual role-specific dashboards.

 

Technicians talk about “virtual sensors,” which simply means that the model can take what data exists and calculate (in place of absence of sensors) needed key performance parameters. These are now relatively cheap and powerful to implement. 

 

Rigorous (engineering) models can now be run at lightning speed to provide insight and advice online. Together with automated methods to take and “condition” production data, these models can be run continuously and help the on-site or remote operator to make faster and better decisions. 

 

One mature field operator we are working with has implemented a simple online model of compressor trains for lift gas injection. The model substitutes for lack of sensing data in a 40-year-old field, calculates critical operating performance parameters, presents a visual operator dashboard and drives better gas yields. In four weeks, the model was credited with over $1 million USD in incremental crude production. 

 

The mobile KPI dashboard is available equally at the remote operating site, the engineering office and the general manager’s conference room. It is this mobility and flexibility that is also a key part of opportunities presented by digital transformation.

 

These models can be used in many other ways to optimize returns from mature assets.  These include employing models of gather networks to make the best possible drilling and CAPEX sequencing decisions and using models combined with emissions data to ensure staying within environmentally permitted limits while maximizing production.

 

For more ideas, read our white paper on upstream reliability and digital transformation.