Using Predictive Maintenance in the Oil and Gas Industry
Interested in learning about the impact of predictive maintenance within the field of oil and gas production?
Over the last 20 years, predictive maintenance strategies have been deployed widely across the industry. Due to its cyclical and volatile nature, oil and gas organizations have benefited from investing in predictive maintenance, cutting operational costs and driving productivity.
In this article, we will share what a successful predictive maintenance strategy in an oil and gas company looks like.
What is Predictive Maintenance?
Predictive maintenance is a proactive approach to asset management. And just like any other proactive maintenance method, the focus of predictive maintenance is to keep pieces of equipment in their optimal working condition, increasing their lifecycle and avoiding costly reactive maintenance activities.
While preventive maintenance is scheduled on a regular basis, from time to time, predictive maintenance is scheduled as needed, based on the asset’s condition.
It uses condition-monitoring methods to keep track of machines’ performance during normal operation, detecting possible defects. Through this early detection, maintenance technicians get the chance to inspect flagged parts and fix them before the assets break down.
The idea is to catch potential equipment failures as early as possible to avoid the need for more complex, dangerous, and costly maintenance activities.
Why is PdM crucial in the oil and gas industry?
Machine failure can have severe consequences for companies operating in the natural gas and oil space.
Less than 4 days of unscheduled downtime represent an average loss of over 5 million dollars, according to Kimberlie. This means that a complex breakdown in a vital asset can have an enormous impact on the organization’s bottom line and even lead to financial collapse.
Predictive maintenance software is able to use machine learning to organize data collected by the sensors installed in the assets. With this information, the software makes predictions and judgments on machines’ conditions, in terms of thermography, lubrication, and electrical connections.
Internet of Things (IoT) technology is particularly helpful to oil and gas organizations because these commodities are often found and mined in remote locations.
Being able to monitor all asset performance in real-time from one dashboard, regardless of whether the machines are in one place or in ten different locations makes it easy for the maintenance department to address issues in a much more efficient and quicker way.
Another important aspect to keep in mind is the employees’ safety.
In a two years interval, oil and gas extraction workers were involved in 602 incidents, 481 hospitalizations, and 166 amputations, according to the Centers for Disease Control (CDC).
With IIoT predictive maintenance, the remote monitoring of equipment can help reduce these numbers, allowing workers to know exactly where the problem is occurring and if the environment is safe to go in and fix it. With IoT predictive maintenance, the remote monitoring of equipment can help reduce these numbers, allowing workers to know exactly where the problem is occurring and if the environment is safe to go in and fix it.
The industrial internet of things takes risk management to the next level by reducing the need for technicians to do unnecessary dangerous inspections. By using big data sets to do predictive analysis, maintenance workers know exactly where and what the issue is.
Remote monitoring also helps teams verify whether the environment is safe. Once the maintenance is performed, it is possible to use assets’ sensor data to verify the efficacy of the operation before restarting machines, which avoids accidents and additional unplanned downtime.
How to implement a PdM plan
Implementing a predictive maintenance strategy can be a challenge for organizations that never leveraged this approach on oil and gas operations before. However, it is definitely a game-changer.
PdM requires the implementation of the Internet of Things sensors within the assets you want to include in the predictive maintenance program. These sensors must be connected to maintenance software to track the utilization and performance of an asset in real-time. IoT sensors send out signals to the CMMS software, which organizes the data into an easy to read dashboard. This way, maintenance managers always have up-to-date information about each asset’s particularities and can quickly make decisions.
Maintenance managers can set up the CMMS software to automatically send alerts when the sensor detects that equipment is operating outside of the preset parameters. Some CMMS software also allows managers to set up the system in a way that a work order is instantly created as the algorithms determine a potential failure.
These alerts make it easy to manage not only maintenance needs but also technicians in charge of the corrective maintenance.
Through modern CMMS software, technicians can also verify detailed asset data before getting into the field. This helps them to judge what type of work needs to be done. Some maintenance software, such as Limble CMMS also offers mobile apps, which makes it possible for workers to verify data while out in the field and even record additional information.
Assets to include in the PdM plan
Leveraging artificial intelligence can increase the productivity of equipment over all three segments of the oil and gas industry — from exploration to refining. This includes upstream, midstream, and downstream.
Upstream: Exploration and Production Phase
Equipment involved in the development, exploration, and drilling processes are complex and have high replacement costs. It is crucial to leverage predictive maintenance technologies to increase these assets’ lifecycle.
In upstream, predictive maintenance is applied to monitor the state of massive equipment such as semi-submersible platforms, floating production storage, and floating liquified natural gas bases.
The sensors are installed in strategic components of these assets, such as pressure valves, submersible pumps, condensers, heat exchangers, turbines, gas flares, and compressors. Sensors are programmed to measure either temperature, pressure, torque, or vibration, depending on where it is placed.
Sensors’ data is then sent to the predictive maintenance solution (CMMS) to be combined with context data. This complex data is transformed into digestible data analytics in the form of predictive models.
Midstream: Transportation and Storage
Assets used in the midstream stage also require predictive maintenance investment since reactive maintenance costs are significantly high — not to mention the loss in profitability associated with downtime.
Midstream sector involves equipment such as floating oil storage areas, liquified natural gas storage areas, as well as on-shore storage and processing areas. Maintenance managers operating in this area must leverage predictive maintenance technology to ensure reliability of the piping, crude oil treatment systems, and gas treating equipment.
The data sensors used in the sector are used to detect sound variations indicating liquid leakages or hydrocarbon leaks. If a unit needs to install a sensor to detect crude oil leakage, for example, they would use either fiber-optic distributed acoustic sensors, ultrasonic sensors, or temperature sensing systems to catch sound variations.
To detect hydrocarbon leaks, maintenance teams tend to use hydrocarbon sensing cables.
CMMS software receives the data from each sensor and triggers an alert if there is any abnormal deviation.
Downstream: Refining and Processing
On average, refineries in the United States lose over 6 billion dollars every year due to unexpected downtime. And most downtime situations could be avoided by refinery equipment maintenance.
In this stage, the sensors collect vibration data, temperature data, and flow rate data to make complete predictive analysis. This type of data is combined with environmental data to enrich the conclusions.
The most important components associated with the downstream phase are diesel hydrotreating units, fluid catalytic cracking units, and sulfur recovery units. Pumps and compressors from distillation units are also often included on the PdM plan.
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Start your oil and gas predictive maintenance plan
Predictive maintenance technology is vital to organizations dealing with many valuable physical assets. Organizations in the oil and gas industry can’t afford to rely on preventive maintenance only, to avoid machine failures.
Considering the volatility of the industry, oil and gas companies must be looking for every cost-saving move to gain a competitive edge.
Investing in automation systems to streamline enterprise asset management is the best way to prevent aging infrastructure from causing a financial disaster.
Book a demo with one of our team members if you want more personalized advice on what is the best way to implement predictive maintenance strategy in your organization.
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