Oil and Gas

The production of oil and gas is a high-tech industry with complex chemical and fluid-dynamic processes. Although computer-controlled and monitored by often thousands of sensors, certain disruptions are just inevitable due to the high complexity and the vast amount of operational data. aivis helps you to understand this data and avoid those disruptions.

AI in Oil and Gas

An autonomous AI for operations data like aivis enables you to realize tremendous savings and improvements along the entire production chain – be it by preventing all kinds of process disruptions, by monitoring critical components with anomaly detection, or by improving the information base through soft sensors.

Prevent known process disruptions

Understand and prevent all kinds of process disruptions that show up regularly and whose occurrences can be found in the historical data of the process.

Monitor the health of critical components

Monitor the health of critical components with anomaly detection to receive timely warnings of emerging malfunctions and avoid unplanned downtimes.

Measure critical parameters with soft sensors

Create virtual sensors for critical process parameters that are hard or even impossible to measure and increase transparency and safety.

Preventing known process disruptions

The oil and gas production is full of high-end equipment and complex chemical and fluid-dynamik processes. Although being computer-controlled and monitored by hundreds, sometimes thousands of sensors, certain kinds of disruptions just keep on occurring. The high complexity and large volumes of data make it hard to maintain an overview and initiate necessary countermeasures in good time.

This is where aivis comes into play: The powerful AI for operational data can handle even the most extensive amounts of data of thousands of sensors and autonomously look for root causes and warn in time about arising disruptions.

Just by looking into your data, aivis finds root causes that are yet unknown.

How it works

All aivis requires to start is a hint to recognize all occurrences of the disruption in the past. It is somewhat similar to giving a scent sample to a tracking dog to pick up the scent.

aivis then automated, unbiased, and autonomously searches all historical operational data to identify critical dynamics that usually were present right before the disruption occurred.

aivis then creates a report that allows to understand those dynamics and find suitable countermeasures. Furthermore, it makes a model which detects those dynamics to apply the countermeasures in time.

Example

Avoiding crude oil foaming

Crude oil foams form during the extraction of live oil in the production well or the separation of oil from water and gas in the tank due to pressure differences, which expel the gas dissolved in the oil. Damaging downstream compressors and causing spills from tanks and equipment, these foams pose significant problems for the operator.

Although foam formation and its many influencing factors have been well researched in principle, foam prevention during production is still very difficult. This is because each case is highly individual, and it is difficult to identify and initiate the appropriate countermeasures at any time from the large volume of operational data and complex interrelationships.

aivis figures out all the dynamics that lead to foaming just from analyzing historical data. It creates a model that creates warnings about arising foaming, including the current reasons, to timely apply suitable countermeasures.

In general, aivis can do the same for any regularly occurring problem recorded in the historian of the asset in question.

Scalable health monitoring of critical components

Critical components like e.g. proportional valves can be found in large numbers. So, even if one component fails only rarely, their large number makes unplanned process interruptions due to component failure inevitable. This has a negative impact on productivity.

With a suitable monitoring solution based on anomaly detection, impending component failures could be detected and dealt with in a timely manner before they actually occur. But usually creating such monitoring models is difficult and expensive, because a lot of pre-knowledge has to be built into the model to reflect the experiences of skilled operators and monitor the relevant relations. This made the generation of monitor models in large numbers impossible.

With aivis, it get’s a lot easier. The only relevant input for aivis is the signal, that reflects the loss in performance, usually a target-actual comparison. aivis then finds out on its own all relevant relationships to monitor for watching the components health, enabling a predictable and plannable maintenance of the components during planned downtimes.

With aivis, watching critical components is so simple, that it can easily be scaled up.

Example

Watching proportional valves

Proportional hydraulic valves are sensitive for unexpected maintenance which causes unplanned downtimes and lowers productivity. However, an anomaly detection model watching the valves can significantly lower these downtimes. Because, luckily, only in the rarest of cases does the valve fail from one moment to the next. Usually, impending failure looms hours or even days before the actual breakdown enabling timely maintenance during planned downtimes and maintenance windows.

For creating the anomaly detection model, to watch a valve, aivis only requires the target-actual comparison signal. It then automatically searches all available signals of the valve for critical relationships that make the unhealthy behavior of the valve visible. The model can be easily deployed into the components live data streams.

In general, aivis can do the same for any critical components or system that provides enough data.

Creating soft sensors for critical quantities

In oil and gas production, not every process parameter can just be measured. Some parameters are very difficult or even impossible to measure directly, such as e.g. the oil viscosity.

As a result, operators have to derive these variables from their experience and other data. But as processes get more complex and data volumes more extensive, this becomes more and more challenging. So operators sometimes have to literally fly blind, which lowers productivity and can even be dangerous.

This is where soft sensors can make a huge difference. These sensors use a mathematical model to deduce the critical process parameters from more accessible process parameters. The model is gained by analyzing and learning from the relations of the target signal to the other signals in the historical data.

As soft sensors have been known and used for several decades now, their creation has always been a struggle. Data scientists and process engineers had to work hand in hand to work out critical relations between the signals and cast them into a model. Furthermore, the soft sensors required continuous maintenance since their models tended to drift and had to be realigned.

This changes with aivis: You just have to define your target parameter and press start. aivis then figures out all relevant relationships on its own, quickly creating an accurate and durable soft sensor.

With aivis, creating soft sensors becomes fast and simple.

Example

Predicting oil viscosity

The dead oil viscosity is a critical process parameter that heavily depends on the type of oil. Even slight differences in composition can dramatically impact the viscosity, making it almost impossible to address this issue with classical black oil correlations.

aivis can easily create an estimator for the oil viscosity. At each moment, this estimator can be fed with the current process parameters giving back the resulting oil viscosity.

In general, aivis can do the same for any process parameter than can be derrived by other parameters.

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