Sponsored Content: IIoT And Field Automation For The Upstream Oil And Gas Industry

By Dave Lafferty, President, Scientific Technical Services

This article was published on the Talking IoT in Energy blog in lead-up to the 2nd Annual Big Data, IoT & Machine Learning in Oil & Gas Canada Conference.

New and Difficult Challenges

The onshore oil and gas industry is facing new and difficult challenges. Not only are there the obvious cost pressures from the dropping oil prices, but there are other hurdles making it difficult run a profitable business. One of those challenges is that operators are being asked to operate in larger and larger fields. Some of the fields like the Eagle Ford in Texas are now more than 800 square miles (2000 square kilometers) in size. Fields like the Permian in Texas, USA, Marcellus in the Eastern US and Bakken in North Dakota have huge areas of operations.

Figure 1 – Larger North American Fields – Source EIA

Another key factor is that many fields – especially the unconventional reservoirs – are moving from the exploration phase, where drilling and completions are the main concern, to more of a “brown field” phase where production optimization of existing well are becoming the main focus.

But the field automation technology landscape is also rapidly changing. Sensors are getting smarter and they are capable of taking new and different measurements. This includes 20 or 30 additional values being generated by conventional sensors, waveform data being generated by vibration sensors and fiber optics cables used as distributed sensors generating a measurement every meter along the cable. But also there is a new set of data to contend with, associated with all the OEM “smart” equipment (pumps, valves, engines, etc.) with embedded sensors generating performance and equipment health data.

Figure 2 – “Smart” Distributed Sensors – Source: Ziebel

Field Automation Must Help Improve Financial Performance

What are the implications of all these new challenges to the onshore oil and gas field automation world?

Perhaps the greatest is that companies are looking to field automation to help reduce costs. While the industry has seen significant reductions in drilling and completion costs, those cost savings have not been realized in the operations space.

One of the new strategies to reduce costs is to use “management by exception”, where field automation handles the routine tasks such as flow readings and tank levels while the field workers are directed to higher value work. This means they are on task specific trips to the well site dealing with exception from normal operating conditions, rather than just doing routes to collect data.

This results in less “windshield” time, which reduces labor costs and improves safety due to less driving. Field automation is also being asked to improve revenues by providing more frequent data to help optimize production. This allows operators to make better informed decisions about areas such as infield drilling and well interventions.

Cost savings are being expected by providing more reliable operations. Sensors feeding equipment health monitoring systems improve reliability and reduce unplanned outages. Monitoring data such as tanks levels can alert before a well shutdown is necessary – thus improving production.

And chemical treatment monitoring can ensure there are adequate chemicals at the well site and that those chemicals are being dosed at the correct rate, resulting in fewer well shut-ins and lower chemical costs.

To optimize investments, time to first oil must be shortened. Field automation must deliver quicker implementations by scaling to much larger areas of operations. Gone are the days when one can take years to build out private infrastructure – the market demands much quicker time to first oil.

Brownfield Projects Present Different Priorities

The transitions to “brown fields” mean that operators have different priorities than when they were operating new fields. Field automation systems must address the asset integrity management issues of a “brown field” environment so as to ensure long term performance of the field.

This can be done by using field automation to capture corrosion, well integrity and equipment reliability information. Field automation also needs to help provide better enhanced production recovery methods resulting in increased EUR (Estimated Ultimate Recovery). 

That need is driving automation control into the field for optimization of enhanced recovery devices such as pump jacks and electrical submersible pumps (ESP). This addition data is also needed to gauge the performance of the various intervention methods to optimize the investments in well interventions.

New Data Implies New Problems

Another huge implication is that field automation systems must scale to handle the tide wave of new data. Some well sites are seeing up to a terabyte of data per day being generated. Field automation systems must change to communicate, store, analyze and visualize this new data. Conventional systems are too expensive to expand and do not scale to this new world of big data.

Also the backend data storage systems must be more than a conventional historian where it can deal with the volume of data – both in terms of the number of wells and in the types of data (such as waveforms).

Figure 4 – Wellhead Monitoring Data Locations – Source: ABB

IIoT Can Help Address These Challenges

But how does one address all these new automation challenges and still remain in a competitive position?

By using an Industrial Internet of Things (IIoT) architecture that provides a separate path for non-control data that goes around the traditional process control network. It is flattening the traditional Purdue/ISA99 architecture into an enterprise model. Perhaps the sweet spot for IIoT implementations are in areas such as equipment health, corrosion and production chemical management.

Figure 3 – Conventional ISA99 vs. Enterprise Model