With the rapidly evolving IIoT revolution happening all around us, there are significant business
opportunities for pipeline companies, OEMs and oil & gas companies to reduce cost, increase revenues and mitigate safety risks.
Vibration analysis can help companies manage assets for measured success, plan service calls, track failure modes and increase responsiveness to faults for mining, oil & gas, petrochemical, refineries and original-equipment manufacturers. However, when vibration analysis is paired with an artificial-intelligence platform that can ingest large amounts of data from systems like OSI PI, Maximo and SAP, this is where new frontiers are opened.
Gathering historical machine-failure data, maintenance records, technician data on qualifications to make the repair and then algorithmically checking the spare-parts-refurbishment inventory is where the magic of AI begins. It has the power to transform how companies move from old, inefficient time-based maintenance to modern, time and money saving condition-based maintenance.
Safety/risk-mitigation is a big focus for industrial companies, especially pipeline companies that have assets spanning hundreds of thousands of miles throughout the US and the world. Artificial intelligence can process caustic and corrosion values for various types of liquids and materials running through pipes. The solution provides the answer to questions like “At what rates do corrosive materials start to break down the pipelines and affect their structural integrity?”
This is key because artificial intelligence provides key stakeholders with insights into where to inspect next. Artificial intelligence can give senior-management personnel insight into priorities of need and help them decide where to write their next check and allocate resources to mitigate their risks. AI raises shareholder value by providing information to stakeholders, allowing them to keep as much material as possible pumping through pipelines—as this is how these companies make money at the end of the day.
In the digital oilfield, there is still a large gap between having insights into potential failures versus simply having production data. In the digital oilfield, there is a huge opportunity to help companies move from condition-based maintenance to time-based maintenance. There are still thousands of well sites being driven to on a daily basis by personnel (think guagers). People literally drive from well site to well site writing down analog readings from the meters on production data. Often it is only when guagers go to a well site that they discover it has a broken sucker rod or a broken, failed pump shaft.
This old way of doing business is inefficient and costly.
How do we understand events that are leading up to a failure of a sucker rod? First, we need to understand the anatomy. A sucker-rod barrel is a single-piece, hollow tube with threads on both ends. The structure of the barrel's materials can be divided into two groups: base materials and the coating or surface-treatment layer. The most common base materials are steel, brass, stainless, nickel alloy and low-alloy steel. These base materials’ abrasion and corrosion resistances are enhanced by plating and other surface-treatment processes.
The most common coatings and treatment processes are chrome plating, electroless nickel carbide composite plating, carbonitriding, carburizing and induction-case hardening. Coated or plated barrels have the largest market share because barrels experience the most wear and operate in severe, abrasive and corrosive environments. The most commonly sold barrel types are stainless steel, chrome plated, plain steel, chrome plated, brass, chrome plated and nickel-carbide coated.
As you can see with various types of sucker rods and various types of materials, there is inherent variation in their strengths and tolerances when performing in the field. With AI, the platform can ingest these values and provide insights into the failure behavior and the signals that are pointing to an upcoming failure. This can be a valuable tool for pump operators, designers and manufacturers, enabling them to reduce the failure of well sites.
For upstream operations, there is still a big market opportunity to help companies understand signals that are leading up to failure, which can greatly affect production. If something like a top drive, catwalk or casing running tool (CRT) fails, in some cases there might be a spare waiting on the sideline. (But what happens if that spare fails, as well?)
Understanding signals that are leading up to failure for the components of an upstream operation is not only essential to these operators, but also essential to the OEMs that are supplying the critical components of the oilfield operations. This is why companies are turning to artificial intelligence. AI insight helps build a better product, helps us better understand the performance of machines, and provides insight into signals that are leading up to failure.
The knowledge that AI imparts enables workforces to take action to avoid catastrophic failure.
Greg Slater is general manager at Flutura.
