A.I. and predictive maintenance for oil and gas
The oil, gas and chemical industry is going through a major transformation in the CMMS field. Traditionally, the oil and chemical companies relied heavily on human interaction for preventative maintenance, and part of that required human analysts to schedule the maintenance. However, CMMS software is now using advanced algorithms and analytics in preventative maintenance to transform the field into superior predictive maintenance. This is seen where new CMMS maintenance systems use enhanced artificial intelligence to lead scheduled maintenance. An example of this is software linked to sensors on production equipment that measure performance parameters such as vibration or heat in oil exploration. When any of these parameters stray outside a predefined range, the software uses an artificial intelligence (AI) engine to determine the most likely cause and will also suggests work orders to maintain or replace certain equipment. Another example of this is seen in oil filtration, where an advanced CMMS system may monitor a single or primary solid particle sensor, but on alarm condition, will then access data from other secondary solid particle sensors mounted on the system. This may determine whether the filter has failed or a work-end component is in the failure initiation mode. Alternatively, information from the moisture sensor may indicate that the cause of increased solid particles is related to moisture. Petroleum marketing and refining companies are actively using this level of CMMS predictive software to monitor rotating equipment, centrifugal pumps, turbines, gas compressors, displacement pumps, and a number of other instrumentation.
Despite the advances in artificial intelligence, these algorithms provide only limited capability in assessing problems. This means that the analyst will continue to play an important role in the asset optimization process through his/her understanding of the correlation between data sets and other maintenance techniques. However, the artificial intelligence software will allow the analyst to focus on critical issues across the plant and to strategically evolve the program through the development of better lubrication plans.
There is tremendous value creation for the oil and gas sector, particularly petroleum exploration using artificial intelligence in an integrated preventative maintenance system. Lowering maintenance time will have immediate benefits to the life of lubricants and filters, as well as maintaining design specification operating parameters to achieve maximum productivity and quality. Further, analyzing the CMMS measurements will highlight areas of concern, such as excessive lubricant consumption or short filter life, and help focus the equipment utilization more effectively.
Moreover, original equipment manufacturers for the oil and gas industry indirectly benefit by understanding exactly what equipment and replacement parts clients need when managing oil and gas exploration projects. Engineering manufacturers can learn from the exercise by understanding past performance and enhancing design specification to meet increased production demands and work order flow. This maintains inventory and purchasing to an optimal level as well reducing the inherent financial risks of holding excess inventory of parts. Work order progression is another direct benefit for the maintenance analyst in this field. Data collected will assist in the success of implementation of corrective work since the job’s completion has been logged in the preventative maintenance infrastructure. In addition, the actual maintenance work order is more efficiently requested by the analyst straight into the CMMS system. The benefits received from these new innovative CMMS techniques in the energy sector reduce costs significantly for an industry with massive capital expenditures, leading to a more efficient energy enterprise for the long-term.