Plunger Lift Optimization.

Overview

Optimization of Plunger Lift Systems

Over time, gas wells can experience a phenomenon known as liquid loading, where the accumulation of liquid becomes substantial enough to completely seal the tubing, leading to a cessation of gas production. If left unaddressed, this issue can permanently render the well unproductive.

Plunger Lift, a form of artificial lift, utilizes gas pressure to remove the accumulated liquid from the well’s bottom. Plunger Lift systems have gained widespread popularity due to their cost-effectiveness and efficiency. The typical operation of a Plunger Lift involves a continuous cycle that is repeated to lift the accumulated slug. Presently, plunger cycles are managed using trial-based trigger systems and manual inspections, resulting in suboptimal operation.

Maximize Gas Production

Minimize Equipment Damage and Downtime

Next to Zero Emissions

Reduce Labour Costs

 

Our Monitoring Platform: Standalone, in the Cloud, or Embedded within Existing Metering and Monitoring Hardware!
Leveraging IoT and Cutting Edge AI Frameworks for Smarter, Cleaner, and Efficient Gas Wells

 

Benefits

  • Maximize Gas Production
    Through the utilization of our AI trigger, we can accurately predict the impact of liquid loading on gas flow rate, resulting in optimized plunger cycles. Compared to standard trial-based triggers, our AI-trigger achieves a remarkable 20% increase in gas production.

  • Minimize Equipment Damage and Downtime
    Equipment failure and damage often stem from the velocity of the traveling plunger. Swiftly moving plungers tend to cause more harm. With our AI trigger system, we effectively eliminate 90% of undesired velocity cycles, leading to an impressive 88% reduction in costs associated with plunger breakages and resulting downtimes.

  • Nearly Eliminate Scope-1 Emissions
    Venting, which is typically employed to facilitate plunger arrival at the surface, contributes to Scope-1 emissions. However, our AI trigger system significantly diminishes the occurrence of slow velocity cycles, thereby minimizing the need for venting. As a result, Scope-1 emissions are drastically reduced by 98%.

  • Decrease Labor Costs
    By implementing our automated monitoring and trigger system, labor costs can be reduced by up to 66%. Experience the benefits of streamlined operations and increased efficiency while optimizing your budget.

Workflow

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Optimizing Plunger Life

Plunger Lift systems are extensively utilized in gas wells due to their cost-effectiveness and similarity in lifting accumulated liquid slugs at the well’s bottom. Various plunger types are available from different vendors.

The selection of an appropriate plunger for a specific well depends on multiple factors, including internal well conditions, path geometry for the plunger’s travel, gas amount and pressure, chemical conditions, and more. It is crucial to make an informed decision when choosing the plunger type and manufacturer for a particular well. Failing to do so can result in a significant increase in plunger breakages and a reduction in plunger lifespan, leading to revenue loss in the form of maintenance and downtime costs.

Optimize plunger performance and minimize replacements with our Data & AI-driven solution. Our solution recommends the most suitable plunger type and manufacturer based on the specific conditions within the well.

Benefits

  • Assists in determining the most suitable plunger type for a specific well, reducing the risk of premature wear and frequent replacements.

  • Identifies optimal conditions to maximize the lifespan of plungers.

  • Our AI-powered solution leverages historical data from wells to predict when a plunger is approaching its breaking point, enabling timely replacement.

Workflow

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AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.

Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.

In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.