Energy performance monitoring and optimization (2022)

Topic last reviewed: November 2022

Sectors: Downstream, Midstream, Upstream

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Energy monitoring has long been common in downstream, primarily because downstream is a margins business. It is becoming increasingly common in upstream operations, driven by the need for demonstrable carbon reduction. Upstream assets tend to have acceptable operating envelopes for safety and reliability; therefore, optimizing with an energy-efficiency mindset is just an additional operating envelope.

The monitoring and optimizing of energy are important for older facilities that may not have incorporated energy-efficient design principles to remain competitive with newer, more energy-efficient facilities. It is also important for recently commissioned energy-efficient facilities because they may not operate efficiently if they are not monitored and optimized.

Establishing energy management systems

Every site should have a documented energy management system (EnMS). The EnMS should be reviewed regularly so that realized energy benefits are sustained and then improved upon. ISO 50001 - Energy Management, provides some guidance on how EnMSs can be set up, and this Info Sheet describes the components of a good EnMS.

Establishing a sustainable EnMS cannot be accomplished by only a few people. To have sustainable success, everyone at the facility should understand their role and accountability in improving the energy efficiency of the site. Each site should also develop, maintain, and update the site’s energy plan, including energy improvement targets.

To assess and improve the effectiveness of the site EnMS, proper communication should be agreed to and implemented. Since the roles and accountabilities span across all groups/functions of the site, specific communications should be developed to target different audiences, with appropriate metrics. The communication frequency may vary but it should always provide information on past performance and then show areas of opportunity with who is accountable.

Monitoring and target setting for facility and equipment energy usage is crucial to ensuring a facility is consistently operating as efficiently as possible. However, to have sustained performance above the historical best, energy improvement ideas should be incorporated into the site’s EnMS action plan. These ideas include projects, maintenance activities, and operational changes.

Measurement, monitoring and data analysis

Monitoring systems include electronic sensors, digital data acquisition systems, networks, and computers for collecting, storing, and analysing data.

Monitoring systems should be used at all stages of industry operations. Where possible they should be applied at all functional scales, from individual pieces of equipment to entire production plants. Examples of specific applications include:

• Monitoring of pressures, temperatures, and flow rates of different process streams in facilities to optimize performance
• Monitoring of heat exchangers to see whether performance is degrading and then to oversee the reduction or elimination of fouling issues
• Monitoring of multiphase fluid parameters in pipes to counteract ‘slugging’ transients which affect steady-state operation and decrease energy efficiency

Most well-managed sites perform both mass and energy balances. This is typically undertaken for individual systems and overall systems. To perform the balances, it is important that the sites have proper instrumentation, and this is usually added when the facility is designed. Looking at the mass and energy balances can also help a site know whether any of the instruments have errors, although mass balances in upstream operations are typically not as accurate as downstream due to uncertainty in the fluid composition (amount of water in oil, etc.).

A measurement plan should be considered that defines which instruments are used in energy monitoring and energy calculations. The following elements are recommended for an efficient measurement plan:

• A list of energy and emissions monitoring and control instruments should be defined (e.g., tagged specifically in the facility’s systems)
• The relevance of the measurements, the accuracy of the measurements, and the relationship to the energy key performance indicator (KPI)
• An identified budget for the maintenance of these instruments (although it is likely part of the general instrumentation budget)
• Procedures that the instrumentation technicians use to guarantee the accuracy of the instruments
• A list of missing instruments identified in an improvement plan

If the desired energy monitoring instrumentation is missing, in the interim the following can be considered as alternatives:

• In the absence of flowmeters, the flow can be calculated using the valve opening and valve characteristics correlation.
• Empirical models or first-principle models can be used to estimate the missing data by inferring from other available data.

New instruments not included in the original design might be fairly expensive to add later. One newer trend, that is less expensive is to use remote sensing alternatives. These include:

• Wireless instead of wired instruments, which can be more cost effective and work just as well when you are only using them to monitor and not directly control
• Instruments powered by solar, on- site heat, motion/vibration, or other means instead of the electric grid
• Drone or satellite monitoring, which can be useful if you do not need continuous monitoring

Energy monitoring should be done at every site using energy KPIs. These energy KPIs are energy influencing variables that increase or decrease energy usage. Energy KPIs have many names, including key energy indicators, key energy parameters, key energy variables, or energy performance indicators. Some examples of energy KPIs can be found in Table 1.

The energy KPIs should be measured and compared with their targets. Energy KPI targets and ranges can be set based on historical best performance, conditions from a performance test, or design conditions (as a starting point). System models can also be used. These targets may need to be dynamic so that they can shift based on unit rates, operating conditions (pressure, temperature, flow), fluid composition, or desired products.

Table 1: Examples of energy key performance indicators

Dashboards or other visualization tools can be developed to track energy performance. They can also display the financial value of the lost opportunity by not hitting these targets.

There may be separate dashboards for operators, engineers, and management. For example, management dashboards can provide overall energy consumption and facility benchmarks. Operations dashboards can provide equipment or unit level performances and flag excursions of energy KPIs for operators or engineers to address. Periodic system performance reviews can also be helpful.

Benchmarking is important at both the unit and facility level. Refining has standardized on a complexity-adjusted energy intensity index. Benchmarking is more complicated in upstream operations as the processes are different from location to location due to different characteristics of the fluids, reservoir formations, and geography. Currently there is no upstream industry-accepted, asset-level, energy-efficiency indicator that can be used for benchmarking. There are, however, several companies that offer various methodologies that could be used within an organization to assess the performance of their operations.

Data analyses (trending, regression, etc.) can be used to understand equipment or a facility’s historical best energy performance compared with how it is operating now. If the data are worse than the historical best, this can allow the operator or engineer to investigate further to understand the cause. The data analyses can also be used to adjust energy correlations that were originally assembled in designing the unit.

With dynamic and constantly changing markets, unit operations tend to vary more. Improved monitoring helps to identify where equipment has degraded and other areas where market changes have resulted in structural energy inefficiencies. New monitoring trends include EnMS platforms that use artificial intelligence and machine learning to monitor performance and flag excursions. These are being developed by third parties. They oen include a virtual platform (i.e., digital twin) to assess different situations. These models depend strongly on the availability of good data in near real time that allow its analytic capabilities to be fully exploited.

Many of these newer systems are cloud-based and leverage data science techniques. These should be complemented by facility expertise to ensure that the machine learning models are robust and provide sound recommendations.

These EnMS platforms include descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. More information can be found online but brief descriptions follow [Reference 1]:

• Descriptive analytics utilize historical data to provide insights via statistical analysis and visualization; for example, the historical energy consumption of the site can indicate when energy use peaks.
• Diagnostic analytics take descriptive data one step further and provides insights into why the historical event(s) has occurred; for example, high energy consumption could be due to specific feedstock changes, while other operating parameters are maintained.
• Predictive analytics apply historical data in a machine learning model that considers the interactions between the different variables to forecast future events; for example, it may predict that the energy consumption will increase in the future with the predicted feedstock change.
• Prescriptive analytics utilize the predictive model together with an optimizer to provide recommendations on how the equipment/facility could be operated differently; for example, it may recommend a different set of operating parameters when there is a feedstock change

Process control

Control systems include feedback control systems and actuators for automatically adjusting operating parameters. This may include power management systems as well to optimize system loads and maintain power quality (e.g., power factor, harmonic levels) in the power distribution system. Computer models of the systems being controlled are also frequently used to predict deviations from expected/ optimal performance. Control soware may also be used to automatically initiate maintenance activities and optimize the control systems themselves, such as automatically tuning closed-loop feedback controllers. Although less ideal, in some cases, simulation tools are also used in open-loop mode to generate results, which are then analysed and implemented by operators or engineers.

Modern measurement, automation, and control systems to optimize energy may not have been included in the original facility design but may be added during a facility retrofit. Installing monitoring and control equipment can allow operational improvements so that the facility uses less energy per unit of output, for example barrels of oil equivalent, thus generating less CO2. This may allow the facility to avoid or at least defer other hardware upgrades or retrofits for post-combustion CO2 control (e.g., carbon capture).

Frequently, basic control capability is all that is needed to achieve most of the efficiency gains. However, in some more complex systems and operations, use of advanced process control (APC) can help realize additional benefits. APC can reduce energy consumption by removing process instabilities, quickly compensating for disturbances, and continuously pushing the process towards optimal constraints.

APC is more common in downstream operations and is only needed in select upstream facilities that are more complex. The expected energy saving achievable by the APC varies a lot depending on the application, it can be typically around 5-10 % up to 30% in best cases.

APC works by establishing a relationship between manipulated variables and controlled variables.

• Manipulated variables are independent variables that are adjusted dynamically to keep the controlled variables at their desired values.
• Controlled variables are variables that depend on the manipulated variables. These are the variables which quantify the performance or quality of the final product. They are also called output variables.

The models behind APC can be divided into empirical models and theoretical models.

• Empirical models are derived exclusively from process test data. During model set-up, tests are performed to check how the controlled variables behave when one or more manipulated variables are modified. The model is then made of a set of equations based on what is observed for the real plant.
• Theoretical models or first-principle models are based on equations that aim to reproduce the physics of the system. A theoretical model should contain the equations of state of the fluids (e.g., Peng–Robinson or Soave– Redlich–Kwong for oil and gas) and the equations of the machines, such as compressors, pumps, and valves

An example of APC in downstream operations is on a crude unit preheat train. In a complicated preheat train with several parallel splits, APC can be used to control the flow rate of crude through the different circuits to maximize preheat (while providing the heat removal that the tower needs) and minimize firing in the crude furnace

Energy optimisation review

Periodic energy optimization reviews are important for an EMS. They help an asset identify energy- (and cost-) saving opportunities and develop the needed energy KPIs and systems to sustain energy-efficiency improvements. The reviews focus on items such as:

• Equipment efficiency
• Process efficiency
• Utility efficiency
• Reliability and maintenance improvements that will help improve efficiency

The reviews could be led by a central energy management team or by the asset itself if someone has experience leading such a review. They should have strong business unit support to ensure success. Ideally, the review team should be cross- functional, including representatives from facilities engineering, operations, HSE (for GHG and energy reporting), production engineering, and, depending on scope, others, such as maintenance, subsurface petroleum engineers, and drilling. As part of the review there may be a need to build energy management fluency of the site staff.

Key steps in an energy review include:

• Define the boundary of the review (facility, unit(s), etc.).
• Calculate the metrics to understand energy and carbon performance of the asset(s).
• Use available data and tools to assess historical and current performance.
• Baseline and benchmark energy and GHG performance; Sankey flow diagrams are a very useful tool to help visualize and understand energy usage and losses in an oil and gas asset.
• Conduct brainstorming workshops and in-person interviews to capture optimization opportunities.
• Supplement with data analysis and process simulations if needed. Capture all opportunities in a register.

Some examples of items to look for in workshops include:

• Are pumps and compressors the correct size and running at their BEPs?
• Is fired equipment running at reasonable fuel/air ratios, O2 percentage, stack temperatures, and loading?
• How is the overall steam balance and steam shedding plan? How much steam is being let down across valves? Is there any steam being vented?
• Does the facility have a load-shedding plan (electrical)?
• Is excess heat energy being recovered?

Spinning reserve minimization: Open- cycle gas turbines typically supply electricity at offshore production sites. Historically, excess gas turbines are operated (spinning reserve) to ‘protect’ production from plant vulnerabilities, including power generation reliability. These additional spinning gas turbines consume fuel while making CO2, without any direct benefit. Spinning reserve reduction is about minimizing the number of machines running. By reducing spinning power, there may be greater production exposure to power fluctuations. Therefore, the spinning reserve needs to be optimized, not minimized.

To help facilitate the workshop, it may be helpful to walk the team through an upstream process flow, from the subsurface reservoir to the surface facilities, pausing at each ‘node’ in the system to look at the data, assess performance, and identify efficiency opportunities. These nodes may include systems such as:

• Reservoir management, including both production and enhanced oil recovery injection streams
• Wells: drilling and completions, artificial lift
• Off-plot piping and fluid transport both to and from a well
• Production export facilities
• Fluid disposal facilities
• Electrical distribution systems
• Power and heat generation utilities, including all fired equipment

Screen and prioritize improvement opportunities using criteria developed by the team (e.g., energy or GHG reduction, financial payback, time and effort to implement, risks, etc.). This may involve an initial screening to get the top 20% of ideas and then a more detailed assessment to find the top ideas to take forward. Note: it is important to assess operational risks and unintended consequences (e.g., optimizing boilers to save gas leads to flaring; reducing spinning reserves leads to power outages).

• Work with business unit to resource and execute the prioritized opportunities.
• Make sure the necessary KPIs and measures are in place to sustain the improvements over time.

Ideally, the review should also include a follow-up assessment in six to twelve months to ensure:

• The efficiency projects have been implemented or are progressing
• The completed projects are performing as expected and the energy saved is as projected

The Energy-Efficient Design for Carbon Dioxide Reduction Info Sheet has additional information that can be useful when a site is adding equipment to improve energy efficiency.

Application of technology, including relationship between reliability and efficiency

High reliability and availability improve energy efficiency as steady processes allow equipment to run at design conditions and use less energy. Specific energy consumption (energy divided by production) is also less as production stays constant. Additionally, operators and engineers have more time to focus on energy efficiency. Therefore, performance monitoring should include KPIs for both energy efficiency and reliability.

Some specific examples of reliability synergies and trade-offs include:

• Spinning reserve minimization: As discussed, minimizing the number of gas turbines in operation can reduce energy usage but also decrease the reliability of electricity production.
• Selecting the correct pumps to match process demand so that they run at BEP: These pumps are more reliable and use less energy.
• Exchanger fouling/scaling: This increases energy usage and can reduce capacity and cause downtime for cleaning or repair. Operating data should be analysed to find the optimum cleaning interval such that the combined long-term cost of fouling and cleaning is minimized.
• Motors with high torque: These might be both less reliable and less energy efficient.

When monitoring and optimizing energy consumption it is important that the full life cycle of the products is considered. Moving CO2 emission from upstream to midstream is not helpful if the same company operates both. It is also not helpful to the planet if emissions are just pushed to a third party.

Technology maturity

Key metrics

Decision drivers

Operational issues/risks

Excessive measurement error leads to suboptimal system control and operation. Therefore, calibration and testing at adequate intervals is necessary. Also, incorporating some data reconciliation and redundancy between monitoring sensors/systems can help mitigate measurement error risk. Measurement error is more likely in upstream processing than refining because some measurements in upstream processing that effect energy efficiency do not also affect operating costs. Therefore, less attention is paid to it.

Outages of equipment or processes may be necessary to install monitoring instrumentation. This may require waiting until a scheduled outage that is several years in the future. Also, scheduled outages may be of short duration and therefore of insufficient length to install the instrumentation.

As the field declines or things change it may become less energy efficient. This is discussed in more detail in the topic Energy-Efficient Design for Carbon Dioxide Reduction

As an operator adds more measurement and monitoring for energy efficiency, there is a greater cyber security risk. These risks are manageable and are already being addressed within most operator’s’ normal instrumentation and controls protocols.

Opportunities/business case

Energy performance monitoring and optimization can reduce GHG intensity and operating costs. Sometimes it can improve reliability, although there are situations where it can decrease reliability. See the ‘Application of technology’ section for more information.

Industry case studies

Case study 1: Implementation of advanced process control at a gas processing complex

APC was added to an existing gas processing facility. Energy savings of 2–5% occurred by optimizing column operating conditions and using a recycle stream to recover energy from various parts of the plant.

Case study 2: Reduced energy usage at gas seperation plant

Implementation of procedural automation at a gas separation facility resulted in fuel reduction of two oil heaters through control of a non-linear air damper to optimize the fuel/air ratio, based on O2 measurements, and electricity savings through optimization of the number of running fans based on ambient temperature readings and flow control of loading pumps.

Case study 3: Fouling - implementation of performance monitoring (Reference 2)

A large oil refinery had asphaltene deposits in the preheat train heat exchangers, requiring extra fuel to the furnace to make up the temperature for efficient distillation. Fouling monitoring software was used to pinpoint the exact time that the fouling event occurred, which exchangers fouled, how much fouling had occurred, and the value of the cleaning of the fouled exchangers. The fouling monitoring program calculated the fouling factor daily for the exchanger so that changes in fouling behaviour because of crude type could be identified on time.

References

1. https://www.analytics8.com/blog/what-are-the-four-types-of-analytics-and-how-do-you-use-them (Accessed 27 July 2022)
2. Waters AJ, Akinradewo CG, and Lamb D. “Fouling: Implementation of a Crude Preheat Train Performance Monitoring Application at the Irving Oil Refinery.”Proceedings of International Conference on Heat Exchanger Fouling and Cleaning VIII. Schladming, Austria. 14-19 June 2009