large amounts of data from systems like OSI PI, Maximo and SAP to couple this data with historical machine failure data, maintenance records, technician data on where the most qualified person is to make the repair and then algorithmically check the spare parts refurbishment inventory is where the magic of artificial intelligence begins to transform how companies start to move from the old inefficient time based maintenance to condition based maintenance.
Safety & Risk mitigation is a big focus for industrial companies as well, especially pipeline companies that have assets spanning across hundreds of thousands of miles through out the U.S. and globally. Artificial Intelligence can process caustic and corrosion values of various types of liquids and materials running through the pipes asking things like: At what rates do the corrosive materials start to breakdown the pipelines and affect the structural integrity?
This is key because then artificial intelligence can give key stakeholders insights into where to go inspect next. Artificial intelligence can give senior level management personnel insights into where to write their next check or allocate financial resources to mitigate their risks and raise shareholder value by keeping as much materials as they can to pump through the pipelines which is how these companies make money at the end of the day.
In the digital oilfield there is still a big gap between having insights that are leading up to failure versus just having production data. 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 like Guager’s that literally drive around from well site to well site writing down analog readings from the meters on production data. Often times it is only when these Guagers go out to the well site is when they discover the well site has a broken sucker rod or failed pump shaft is broken. This old way of doing business is inefficient, costly and at the end of the day how the old world of oil and gas works.
So how do we understand events that are leading up to failure of a sucker rod? First, we need to understand the anatomy of a sucker road. 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. Brass, nickel carbide coated, plain steel and nickel carbide coated.
As you can see with these various types of sucker rods and various types of materials there is in inherently variation on their strengths and tolerances when it comes to performing in the field. With artificial intelligence the platform can ingest these values and provide insights into the failure behavior and signals that are leading up to failure. This can be a valuable tool for pump operators, designers and manufacturers to have insights into reducing total and complete failure of a well sites.
For upstream operations there is still a big opportunity in the market to help companies understand signals that are leading up to failure that can greatly affect their production. If something like a top drive, cat walk or casing running tool (CRT) fails in some cases there might be a spare waiting on the sideline but what happens if that fails? To be able to understand signals that are leading up to failure for these types of components of an upstream operation is not only essential to these operators it is also essential to the OEM’s that are supplying these critical components of the oilfield operations. This is why more and more companies are turning to artificial intelligence to help then build a better product, better understand the performance of their machines, have insights into signals that are leading up to failure and enabling their workforces to Take Action and Not Just Have Insights.
