In times of slump, process optimization guards your margins.
by Ahmed Habib and Jahangir Malik, INTECH Process Automation
The prevailing oil price slump has left everyone shaken, be it independent shale producers with a few rigs in their backyards or oil & gas giants who have dominated the oil market for years. The one thing that everyone has come to appreciate however, is the value of optimization. When selling prices get painfully close to operating costs, any effort to shave off that extra dollar overhead is well worth its rewards. And while this is entirely true for the upstream, there is a lot of incentive for midstream and downstream facilities to follow suit. Process optimization is one of the least capital-intensive methods of improving margins – ideal for prolonged slumps where stakeholders and managers rarely see eye-to-eye.
What is Process Optimization?
Process optimization is a systematic improvement to all aspects of an operational process in order to improve output, utilize fewer resources and increase plant life and performance. Its implementation can vary from the slightest changes, e.g. changes in inlet temperature, operating temperature, reagent concentrations etc., to redesigning entire parts of processes for improved utilization and yields. There are four main areas where process optimization can be implemented. These are:
- The process itself
- Process parameters and operating constraints
- Equipment, instruments and devices associated with the process, and
- Personnel tasked with oversight/operation of the process
Optimizing the Process:
Optimizing a process encompasses redesigning or revising the process to uncover hidden efficiencies or rectify obvious design inefficiencies. While most modern plants and facilities are extremely unlikely to have design inefficiencies, sometimes factors like changes in demand and expansion etc. can render the original process inefficient. For example, a compressor station that had been operating at peak efficiency with three compressors found excessive costs in compressor maintenance when gas demand was significantly lowered – this warranted redesigning how often each compressor worked to improve uptime under a lower load and reduce maintenance costs.
Sometimes this might mean incurring extra capital costs to make the desired changes within the plant or facility, choosing whether to go ahead depends on the return on such measures – not all of which may be tangible. In another example, managers at a particular gas plant that was flaring excessive unwanted gases decided to reuse the excess gas instead and redirected it to generator sets. These generators provided power to the residential quarters for personnel, effectively increasing power availability and reducing electricity overheads.
Optimizing Process Parameters and Constraints:
Operators at aged facilities are all too familiar with the feeling of being overwhelmed with alarms. As a plant ages, its instruments lose accuracy, control valves and rotary equipment wear out, pipes corrode and electronics start failing. Optimizing process parameters and constraints covers analyzing the process with respect to its plant’s condition and revising its parameters to make the best use of what the plant has to offer. Managing equipment loading/running time, adjusting pressures and temperatures where needed and reassessing the amount of reagents needed go a long way in improving process output and reliability. To top it all off, a healthy alarm management system can reduce the load on operators and prevent unwanted process hindrances.
While all of these activities are highly recommended for gas processing and LNG plants, a similar level of optimization can be achieved on other assets, like pipelines, compressor/pumping stations, tank farms and even valve stations. For example a newly added gathering station having slightly more corrosive and wetter gas than what the compressor station works with, will require a revised amount of corrosion resistant and drag reducing agents to prolong pipeline life. If the station has a separator and/or sweetener as well, they will required revised parameters too.
Equipment, Instruments and Devices:
Just as process improvements can enhance aging facilities that lose productivity due to losses and wearing, another way to optimize processes is to address the failing equipment and instruments themselves. Sensors tend to lose sensitivity over time, just as control valves get rigid and rotary equipment wears out. Maintaining instruments and devices and regularly tuning them helps contribute to process yield. Transmitters can be calibrated, control valves inspected for damage and tuned for optimized PID values, other equipment duly tested and maintained as needed. This may not contribute in a net positive process output, but it will ensure no decreases occur in the short term.
Typically all these activities are part of routine maintenance at any facility anyway, but conducting that maintenance in a smart and efficient manner can help contribute significantly to maintenance overheads and prolong uptime. This brings us to the last factor discussed below.
Optimizing Human Interaction:
Even the most sophisticated unmanned facilities require some level of human interaction for sustained operation, be it routine maintenance or mere operations oversight. Optimizing how and when these interactions occur can be a defining factor in productivity. The simplest of these tasks is proper operator training. An operator who knows the entire process and all its intricacies will be much better equipped to respond to anomalies and prevent serious damage if such a situation arises. In addition to operator training, efficient alarm optimization and aesthetic HMI designs can improve operator concentration and reduce overloading.
One of the most significant routine costs at any plant or facility is the cost of maintenance. Standard maintenance procedures require routine inspections, taking measurements and filling check-sheets. While this method of maintenance is widely practiced, it often wastes time of the personnel checking perfectly functional equipment and unnecessarily extends planned maintenance time. The advent of highly accurate and precise digital sensors and the concept of Digital Oil Fields has opened up a window of possibilities for optimizing maintenance processes and reducing overheads.
Condition-Based Monitoring and Maintenance (CBM) is a relatively new domain with great potential for predictive maintenance. With large-scale data storage capabilities available to even the remotest stations, all it takes is are a few analyses of equipment and instruments to determine which are close to failing and which do not require any maintenance – in effect reducing maintenance time and cost significantly. The failure trends of all devices also help design a predictive spares inventory, reducing warehousing costs and the time needed to get replacements for failed components.
To top at all off, combining these capabilities with Industrial Internet of Things and cloud storage/hosting, all this analytical data and trends can be shown directly at a central facility or even to managers on their smart devices. This holistic view of all midstream assets and facilities complete with health status and maintenance reports can help managers be more responsive to keeping processes up and running while the top management can make better assessments of productivity.
Most optimization activities will always be a mixture of the different areas of optimization discussed above. What really makes process optimization work is a well-experienced team of experts who can take care of minor details and provide adequate support and training where needed. It may seem a daunting tasks for end users who don’t have very experienced optimization teams or the means to carry out these tasks themselves, but there are many system integrators and OEMs who provide these services throughout the world. Often a third-party expert can look at things from an entirely different perspective and help identify areas of improvement that were entirely overlooked earlier.
This article was originally published in Control Engineering; INTECH Process Automation’s content partner