In oil & gas, every minute costs millions. And when critical equipment on an offshore rig or in a refinery fails, downtime can mean a serious loss of revenue.
Traditional maintenance models, where equipment isn’t repaired until after it breaks (reactive maintenance) or is maintained on a regular schedule, much like automated preventive maintenance, are no longer sufficient in an era where data is a useful asset.
Wouldn't it be better if companies knew in advance when a piece of equipment is about to fail? This is where intelligent apps and predictive maintenance come in.
What is predictive maintenance (PdM) and how does it reduce downtime?
To explain predictive maintenance in the context of shifting to modern digital tools: rather than waiting for your car to break down unexpectedly on the road, or strictly adhering to the owner’s manual by changing the engine oil every 10,000 kilometers, your vehicle alerts you: "The engine oil is starting to degrade. Change your oil soon."
IBM offers a clear definition: "Predictive maintenance builds on condition-based monitoring to optimize the performance and lifespan of equipment by continually assessing its health in real time." This strategy allows optimally-timed maintenance that doesn't impact operations.
Phase 1: Collecting data in real-time
Essential equipment on a rig or in a refinery, is fitted with Intelligent Sensors (IoT sensors) that continuously detect and transmit operational data, including:
- Abnormal vibrations are often the first sign of internal component wear and tear.
- Excessive temperature indicates overworking or abnormal rubbing.
- Changes in acoustic waves can reveal leaks or mechanical anomalies.
- Fluctuations in pressure might signal a clogged pipe or a problematic valve.
Phase 2: Transmitting and analyzing data
The data streamed from these sensors is fed into an intelligent app powered by artificial intelligence (AI) and machine learning (ML). The AI first learns the normal operating patterns for each piece of machinery. Whenever incoming data deviates from these patterns, the AI flags it as a warning sign of a potential future malfunction.
Phase 3: Predicting and alerting
The core of this proactive strategy is to forecast the remaining useful life of equipment before it fails. Once the AI analyzes which asset is at risk, the system sends an alert to the engineering and maintenance teams, complete with useful details.
- Identifying the specific part expected to fail.
- Assessing the severity of the issue.
- Predicting the timeframe for failure.
Phase 4: Predictive Maintenance Planning
This step directly reduces downtime. Once a team receives an alerts, they can:
- Turn emergency stops into planned shutdowns by scheduling maintenance during times of minimal impact instead of suddenly halting the entire production line.
- Prepare spares and personnel in advance by immediately ordering necessary spare parts and making sure specialized technicians are ready.
- Reduce repair time by knowing the root cause of the problem in advance, allowing equipment to return to operation faster.
A deeper look at 5 tangible benefits of using intelligent apps for predictive maintenance
Using intelligent apps in a predictive maintenance strategy in oil & gas is an investment that generates clear, measurable returns, where every component impacts cost and safety. Here are the five main benefits your organization will gain:
Reduced unplanned downtime
The most apparent benefit is changing emergency repairs into planned maintenance. This shift is important because when a critical machine unexpectedly shuts down on an offshore rig or in a refinery, it's like halting the business's main lifeline.
- Instead of scrambling for technicians and engineers to repair a broken compressor in the middle of the night, an intelligent app provides alerts weeks in advance: "The bearings on this compressor are showing abnormal shaking and are expected to fail in 15 days." This warning allows the company to plan maintenance during an already scheduled shutdown, without impacting production targets at all.
- Intelligent apps help maintain the stability of the production process for as long as possible. This stability translates directly to higher output and sustained revenue in the long run.
Optimized maintenance efficiency
- Maintenance teams no longer waste time inspecting every machine according to a fixed schedule. Instead, they receive clear information from the app: "Go check cooling pump P-168 on Level 3." This guidance allows them to head straight to high-risk equipment.
- When work is precise and quick, one team of technicians can be responsible for a greater number of machines, or they have more time for other proactive tasks that boost production output, instead of regular routine inspections.
Longer lasting assets
Since machinery and equipment in the Oil & Gas industry are extremely high-value assets, extending their service life even slightly translates to huge savings in capital expenditure.
- Severe damage often begins with small, overlooked problems, such as shaft misalignment or insufficient lubrication in machine components. An intelligent app can detect these anomalies from changes in shaking or temperature data, allowing the maintenance team to intervene immediately. Catching these early prevents severe wear and extends the service life of the entire machine system.
- Scheduled maintenance sometimes means replacing parts that are still in good working order. This habit is wasteful and can lead to installing errors. PdM makes sure that we only repair or replace components when they are approaching the end of their useful life.
Reduced inventory costs
Spare parts inventory represents a significant sunk cost for the business. Overstocking parts just-in-case is wasteful and consumes both space and capital.
- When an intelligent app can predict that a specific turbine bearing will need replacement two months from now, the procurement department can plan to order that exact part for on-time delivery. There is no longer a need to order and hold inventory in the warehouse for years.
- Lowering the amount of spare parts in stock lowers the cost of warehouse management and improves the company's working capital cash flow.
Better workplace safety
In a high-risk industry like oil & gas, safety is the top priority. Faulty or damaged equipment can lead to severe accidents, chemical spills and leaks, or environmental impact.
- The ability to predict the failure of a pressure control valve or a pump seal in advance is a direct way to prevent leaks of flammable gas or oil that could cause fires or explosions.
- An intelligent app is not just a maintenance tool. It is a key tool for Health, Safety, and Environment (HSE). It helps companies operate safely and strictly adhere to rigorous environmental compliance regulations.
Examples of using intelligent apps to prevent costly failures in oil & gas
There is no better way to learn than by examining case studies from the world’s leading oil & gas businesses. Let’s explore how these companies transformed their operational models with intelligent apps and smart sensors.
Shell and predictive maintenance for control valves
Technology: Intelligent apps and predictive maintenance technology using AI
Challenge
In Shell's refineries and petrochemical plants, there are tens of thousands of major control valves that manage the flow and pressure of liquids and gases. If these valves malfunction or fail, it can cause production outages, lead to safety issues, or result in off-spec product quality. Traditional scheduled maintenance (preventive maintenance) often causes unnecessary process shutdowns and sometimes fails to detect problems before they occur.
How intelligent apps provide a solution
Shell developed a strong predictive maintenance platform for Control Valves, which is integrated as part of its large data infrastructure.
- The app works by collecting data in real-time from sensors attached to the valves, such as valve position, pressure, flow rate, and information from the process control system.
- This set of data is used for creating a digital twin (or virtual model) of each valve, which allows for simulating various operating conditions and statuses.
- By analyzing with AI, the machine learning algorithm finds patterns of abnormality from the digital twin that indicate the valve is beginning to degrade or is likely to malfunction.
- The platform provides alerts and recommendations when a risk is detected, showing engineers which valve is having an issue and suggesting when inspection or repair should take place.
BP and production improvement with the APEX application
Technology: APEX intelligent app for production improvement
Challenge
Operating oil and gas rigs is highly difficult. Decisions to adjust parameters like pressure or flow rates in production wells often rely on the engineer's experience, which may not always deliver the highest production output. In addition, each adjustment requires a lot of analyzing and simulating time.
How intelligent apps provide a solution
BP developed an intelligent software platform called APEX that acts as a digital brain for its engineers. The main principles of its operation are as follows:
- The software creates a digital twin, which is a real-time virtual replica of the production system from the bottom of the well to the surface production facility.
- Engineers can simulate scenarios via the software interface and using it as a safe engineering playground to experiment with different parameters on the digital twin without risking actual equipment.
- The platform finds the best point with AI by running thousands of simulations rapidly to identify system settings that yield the highest production with the least energy.
- The system provides operational recommendations by displaying simulation results via a dashboard. This allows for confident and precise application to real-world operations.
These two examples show that intelligent apps are already making a huge impact on oil & gas. This success is evident through visible improvements in cost cuts, better speed, and higher safety standards.
Modernize systems with intelligent apps, AI, and predictive maintenance for early problem prevention
The shift to the digital era through digital transformation is no longer optional for the oil & gas companies. It is essential for survival and competitiveness. Intelligent apps powered by ai and machine learning have proven to be a powerful tool for predictive maintenance. Such technology helps shift maintenance from a cost burden to a competitive strategy that boosts output, reduces costs, and raises safety standards.
Seven Peaks, as a leader in digital product development from start to finish, has the understanding and specialist skills needed to create solutions that address the true heart of a problem. We have a track record of collaborating with global oil and gas companies, including Draga, Sekal, and many others. If you are looking for someone to manage your needs and turn your vision into reality, contact us right away.