How AI is reducing fugitive emissions in upstream oil and gas

Artificial intelligence in oil and gas system insights

• In this article, we will explore how artificial intelligence is being used to optimize plunger lift systems and reduce vented emissions.
• By optimizing the operation of these systems and minimizing the need for venting cycles, AI is helping to reduce greenhouse gas emissions and protect the environment.

Methane emissions from fossil fuel operations have long been a major contributor to climate change, accounting for more than one-third of all methane emissions from human activity. The production segment of the oil and natural gas industry is a significant contributor to these emissions, accounting for 60% of the total.

The industry is facing increasing pressure to reduce its environmental impact and improve sustainability. One area where there is significant potential for improvement is in the operation of plunger lift systems, which are used to clear liquid/gaseous slugs out of the gas or oil wells. Plunger lift systems are often unable to clear the wells, venting is done to support the plunger arrival, which contributes to major greenhouse gas emissions.

However, by optimizing the operation of plunger lift systems, it is possible to reduce emissions and improve sustainability. Artificial intelligence is being used to optimize plunger lift systems and reduce vented emissions.

Figure 1: Data representing oil and gas methane emissions by segment in 2020. Reported by Inventory of U.S Greenhouse Gas Emissions and Sinks. Courtesy: Estimates of Methane Emissions by Segment in the United States | US Environmental Protection Agency

Operational stages of plunger lift systems

A plunger lift system is a type of artificial lift method used in oil and gas production to remove liquids from the wellbore and improve production rates. The operational stages of a plunger lift system can be divided into four main phases: shut-in, unloading, venting and after-flow.

During the shut-in phase, the surface valve is closed, which prevents flow from the wellbore. As the valve is closed, pressure begins to build up in the tubing and casing, which is known as the shut-in pressure. This is the pressure differential of upward and downward pressure caused by the casing pressure and slug in the wellbore respectively.

In the unloading phase, the surface valve is opened, which allows the shut-in pressure to push the plunger upward. The plunger is a device that is lowered into the wellbore on a wireline and is designed to remove liquids from the wellbore. As the plunger moves upward, it removes the liquid load from the wellbore and clears the well, allowing for improved production rates.

If the plunger does not arrive at the surface, the venting phase begins, where the pressure differential between the wellbore and the surface increases by releasing the gasses above the plunger to the environment, pushing the plunger to the surface and removing the liquid load from the wellbore. This process is the main contributor to the emissions of greenhouse gasses.

Figure 2: Contribution of liquids unloading scenarios to basinwide average methane emissions. Error bars represent the fifth and 95th percentiles obtained via Monte Carlo simulation. Courtesy: https://pubs.acs.org/doi/10.1021/acs.est.8b05546#fig3

Finally, in the after-flow phase, gas production begins as the wellbore is cleared of liquids and the plunger has arrived at the surface. This phase allows for the continued production of gas from the well.

Despite the effectiveness of plunger lifts in reducing emissions, these systems are not able to eliminate the venting cycle. This is because the performance of a plunger lift system depends on the specific well conditions, which can vary significantly across different basins. As a result, plunger lift systems may lose their effectiveness in reducing emissions over time, leading to an increase in the frequency of venting.

One of the major challenges with plunger lift systems is that the shut-in and after-flow cycles are often manually operated, relying on human experiences and guesswork. This can result in frequent nonarrivals of the plunger, leading to venting. In addition, the unloading of wells with plunger lifts occurs on average more than 200 times per year, adding to the potential for emissions.

Figure 3: This shows different phases of a cycle in a plunger lift system. The leaf nodes represent the objectives of the artificial intelligence-assisted plunger lift system and their parent nodes are the different types of phases, which positively and negatively affects the objective. Courtesy: Qult Technologies

AI-assisted automated plunger lift

An AI-assisted plunger lift system is designed to optimize the performance of plunger lift operation while reducing emissions and improving efficiency. The system is powered by advanced AI algorithms that analyze raw and derived features such as instantaneous casing and tubing pressures, flow rates and liquid load to make predictions and provide recommendations. This includes predicting plunger velocities, forecasting gas production rates and optimizing the duration of shut-in and after-flow phases through triggers.

One of the key features of the system is its ability to fine-tune the shut-in end trigger to only activate within a specific range of velocities. This ensures that the plunger lift system is operating within safe limits, minimizing equipment wear and tear and maximizing gas production rates. In addition, the system can optimize the duration of the after-flow phase to maximize gas production and minimize the frequency of shut-ins. This helps to reduce the probability of nonarrivals, which can lead to the venting of natural gasses into the atmosphere.

It also includes a user interface for real-time data monitoring, allowing operators to track key performance metrics and other relevant data in real time. This includes data on plunger lift speed and pressure, fluid flow rates and temperature, among others. It also has model training capabilities, allowing it to learn from data and improve its performance over time. This could involve using machine learning algorithms to automatically detect patterns and trends in the data and make predictions about future behavior.

Figure 4: Flow diagram of artificial intelligence-assisted plunger lift system. Courtesy: Qult Technologies

This system is designed to provide operators with the tools and insights they need to optimize plunger lift operations, reduce emissions and improve efficiency. It is flexible and adaptable, able to work with different plunger lift systems and changing conditions and to optimize plunger lift operations in real time.

Its benefits go beyond just reducing emissions. By improving the efficiency of the system, AI can also help to reduce costs and increase profitability for oil and gas production facilities. By identifying and addressing inefficiencies, AI can help to lower operating costs and increase the bottom line. The use of AI in plunger lift optimization is just one example of the many ways that the technology is being applied in the oil and gas industry to reduce emissions and improve sustainability. Other applications include the use of AI in predictive maintenance and optimization of production processes.

The adoption of AI in the oil and gas industry is helping to drive progress toward a more sustainable future. As technology continues to evolve, it will likely play an increasingly important role in the industry’s efforts to reduce its environmental impact.

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