Challenging Myths of Remote Simulation and Monitoring

Over the past decade, numerical simulation and monitoring solutions have rapidly expanded across various industries, including oil production, manufacturing, and automotive. However, implementing and using these solutions imply various technical challenges for engineers, such as processing and analyzing large volumes of data with limited infrastructure.

Given this scenario, companies have to evolve by improving their IT infrastructure or purchasing specialized remote solutions. The typical option has been establishing modern infrastructures and research groups, which can be costly regarding hardware, support, and implementation time. On the other hand, acquiring specialized software services still raises some concerns regarding data security, compatibility with other tools, and the quality of technical support. However, more and more providers offer customized and flexible solutions that adapt to the needs of each company, which reduces costs and implementation times and allows engineers to focus on more critical tasks for the business.

Current Solutions for Simulation and Monitoring Operations

Modern solutions are essential for companies to achieve a new level of innovation and maintain competitiveness in today’s market. Using on-premises systems with High-Performance Computing (HPC) has been a popular option for a long time, but today, companies are also looking to optimize computing resources, reduce costs and work times, as well as minimize energy consumption. To achieve these goals, companies increasingly turn to service models such as SaaS, which offers software accessed through a web browser or mobile application. Additionally, models based on Platform as a Service (PaaS) provide a complete development framework or tools to create, simplify, deploy, and manage applications on virtual hardware (IaaS).

These service models can be further enhanced through large centralized systems, such as the cloud, or distributed systems, such as edge computing. Cloud-based solutions utilize state-of-the-art servers that enable users to perform tasks more efficiently and with greater flexibility, as they can easily scale up or down their computing resources according to the user’s needs. In contrast, edge computing is a relatively new computing paradigm that emphasizes the location of data processing and storage resources at the edge network. This approach enables greater efficiency in data processing and faster response times. As a result, specialized solutions providers can choose between cloud and edge computing approaches to suit their technical needs, depending on the amount of data to be transferred, their security requirements, and the desired level of accessibility.

For example, companies that perform simulation and monitoring operations often prefer to purchase software from third-party providers with a distributed computing platform, who inherently handle the maintenance, continuous updates, and the entire operations that occur in the backend. This approach allows companies to access the software remotely and scale the computational resources automatically while providers ensure enhanced security, flexibility, uptime, and fast response times.

Even more, the innovation of engineering solutions don’t limit to using only PaaS, IaaS, and SaaS but also includes emerging services models such as AI as a Service (AIaaS) and Machine Learning as a Service (MLaaS), which allow solutions providers to integrate artificial intelligence capabilities without the need for significant investments in infrastructure or specialized talent.

Facing Myths of Performing Remote Simulation and Monitoring

Traditionally, simulation and monitoring tasks were believed to be best carried out exclusively at the company’s facilities due to the complexity of the problems, the need for data confidentiality, and the high demand for computational resources.

On the other hand, SaaS providers increasingly invest in research and development to build efficient, reliable, and secure solutions with less computational load without compromising accuracy. However, misconceptions regarding remote solutions still need to be addressed. To reverse these myths, providers deploy their solutions onto a platform configured with several prerequisites, including: 

Data Security

Concerns about data security may arise with remote simulation and monitoring. However, proper security protocols, layer-based networks, and encryption can be implemented to ensure sensitive data safety.


SaaS-based solutions can be more cost-effective than users working on their installations because they reduce the need for expensive on-premises hardware and IT infrastructure and eliminate the need for operators to manage them.


SaaS-based solutions can also be accessed in isolated locations and with reduced space, enabling users to access software from anywhere and anytime.


One advantage of using SaaS solutions is its flexibility in delivering training from anywhere and optimizing the learning process. With traditional on-site training, operators must travel to a specific location, which can be costly and time-consuming.


Thanks to advanced tools and emerging technology, remote solutions can be as accurate as traditional solutions.

Next-Gen Remote Solutions: Harnessing the Power of AI

As technology advances, legacy and modern solutions will eventually be subjected to digital transformation. Many companies are therefore seeking to integrate technologies that not only add new features but also turn them into ‘smart’ solutions capable of optimizing themselves. Implementing intelligent solutions can help companies improve the efficiency of their processes and make more informed and accurate real-time decisions.

Artificial Intelligence (AI) has proven to be especially beneficial as a key technology in almost every industry when integrated, for example, into monitoring and numerical simulation solutions. With its ability to analyze large amounts of data in real-time, including images and other types of information, AI can vastly improve the efficiency and precision in detecting problems for monitoring applications. In the case of numerical simulation, AI is useful for solving complex problems by optimizing and simplifying both mathematical models and algorithms. Furthermore, the integration of AI offers other relevant aspects, such as:

  • Supports better decision making by boosting the processing of real-time data;
  • Reduces the use of computing resources;
  • Enables the next-generation of remote solutions, and
  • Provides an easier path to automation.

Gushr’s Innovative Solutions for Oil and Gas Industry

Gushr AI aims to transform the way businesses operate. By combining the best-in-class technology and service, energy companies can enjoy several benefits, including reduced latency, improved performance, and lower costs. Our simulation and monitoring solutions enable engineers to effectively manage flaring operations using advanced modeling techniques and data analytics, leading to accurate and efficient results.

Let Gushr AI help transform your operations and take your business to the next level.

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