Leveraging Edge AI to Overcome Challenges in Traditional AI for Upstream Oil and Gas Industry

Artificial Intelligence (AI) has the potential to transform the upstream oil and gas (O&G) industry by improving operational efficiency, optimizing production processes, and enabling better, data-driven decision-making. However, traditional AI systems face significant barriers that have hindered widespread adoption in this sector. In subsurface operations, where conditions are often unpredictable, significant challenges need to be addressed. Integrating data from different sources can be complex, reliance on cloud computing adds another layer of difficulty, and cybersecurity threats are a constant concern. These issues make AI models less reliable, leading to inaccurate outcomes and limits their usefulness in critical real-time situations.

This article explores the potential of Edge AI as an alternative solution to traditional AI approaches. Unlike centralized cloud-based AI, Edge AI processes data locally at its source, reducing latency, minimizing reliance on cloud infrastructure, and enhancing data privacy and security. Edge AI’s ability to deliver real-time insights with lower bandwidth demands is particularly advantageous for remote upstream operations where connectivity is often unreliable. The analysis presented in this article highlights how Edge AI can overcome the challenges faced by traditional AI and support more efficient, secure, and resilient decision-making in subsurface exploration and production. Ultimately, adopting Edge AI can provide the upstream O&G industry with the agility needed to make informed decisions quickly, reduce operational risks, and fully capitalize on the value of advanced data analytics.

Introduction

The O&G business is the world's primary energy source, with a very complex process for production and distribution (Elijah et al., 2021).? The O&G industry involves complex industrial operations focused on three main sectors: upstream, midstream, and downstream. The upstream sector is the first phase in the life cycle of O&G, which involves exploration and development, drilling and well completion, production and optimization, reservoir engineering, and control center operations. Fig. 1 illustrates the O&G sector (Elijah et al., 2021).

Artificial Intelligence (AI) has the potential to significantly improve operational efficiency and decision-making in the upstream O&G industry. However, adopting traditional AI systems comes with several hurdles, such as difficulties with data integration, reliance on cloud computing, and cybersecurity concerns. These challenges are particularly relevant in subsurface exploration and production, where unreliable data and the high latency of cloud-based solutions often reduce the effectiveness of AI models.

Edge AI has emerged as a promising solution to these problems. By processing data locally, closer to its source, Edge AI enables real-time analysis, minimizes the need for constant cloud connectivity, and enhances data privacy. This decentralized approach offers a more efficient and practical alternative to traditional AI systems, making it especially valuable in remote and data-sensitive operations like those in the upstream O&G sector.

Problem Statement

While some O&G companies are jump-starting their AI initiatives by investing aggressively in startups and R&D, several challenges in data integration, data availability, cloud computing, and cybersecurity are preventing them from massively and rapidly implementing AI in the exploration and production of O&G (Koroteev et al., 2021). AI tools need good quality data of a suitable volume to be trained and work properly in the operational mode (Koroteev et al., 2021). When the data is applied blindly, especially when a sufficient amount of data about the problem does not exist, or when the modeled system is not stable during the period covered by the model, algorithmic bias could become a risk (Balaji et al., 2018). According to Fan et al. (2014), the significant challenges surrounding the rise of big data in AI include the high dimensionality of data, which leads to erroneous correlations, noise, meaningless clustering, and incidental homogeneity. Additionally, there are significant issues related to the high computational time, instability in software, and the high infrastructural costs associated with these computational demands and data storage. Another critical challenge is the heterogeneity and experimental variation resulting from biases due to multiple distributed sources of data matrices. Cloud computing involves using on-demand services such as servers, storage, networking, software, and intelligence (Elijah et al., 2021). Although cloud computing offers several advantages, certain limitations have been identified, which include degradation of quality-of-service due to delays in time-sensitive applications (Elijah et al., 2021), bandwidth issues, and significant uncertainty in predictive models due to the complex and unpredictable nature of subsurface environments. Cyber-attack threats concern the upstream industry to submit confidential or restricted data through the internet to the cloud servers for analysis. In 2016, the O&G production operation ranked highest in cyber vulnerability in upstream operations (Balaji et al., 2018).

Research Analysis

The O&G industry is one of the significant sensor-based industries, with many data-collecting sensors installed downhole and on the surface. Companies also monitor their assets closely to calculate their reservoir production and predict their reservoirs' future performance. An example of the application of wireless pipeline monitoring in the upstream sector is shown in Fig. 3. As a result, the petroleum industry has to deal with considerably large volumes of structured and unstructured data from various sources (Balaji et al., 2018). More and more companies are realizing that they can utilize this available data to better optimize their overall performance in different areas, such as increasing the production capacity of the reservoir, forecasting extreme events, or simulating fluid flow (Balaji et al., 2018). However, replacing or maintaining sensors such as downhole temperature or pressure sensors to generate the data required for data-driven analytics could be expensive (Balaji et al., 2018). Additionally, geoscientists and engineers usually practice in offices with totally different environments compared to the field. Operators must make quick and safe decisions in these environments based on their past experiences. Therefore, data-driven AI technology must meet standards of robustness and reliability to be accepted and applied by operators (Balaji et al., 2018).

General Findings

Edge computing offers a distributed approach for processing data, controlling functions, and storing high bandwidth content closer to devices rather than a remote network. This helps to mitigate network delays and low latency associated with centralized cloud computing. The edge computing devices can be local or localized data centers (Elijah et al., 2021). As a result of the low fault-tolerant process involved in oil extractions, the need to process data collected from intelligent oil fields in real-time makes edge computing a suitable candidate. However, some challenges that need to be overcome in deploying edge computing are the resource-constrained nature of edge nodes and the difficulty of configuration and maintenance in remote areas (Elijah et al., 2021).

Edge AI represents a further shift from edge computing, integrating AI to enhance the processing capabilities at the network's edge. This integration further reduces latency and alleviates the bandwidth demand on central servers while providing additional benefits, such as enhanced privacy due to distributed approaches for Machine Learning (ML), like federated learning, and improved resilience due to local autonomy and decentralized control. Edge AI has applications in various domains, including smart cities, health care, autonomous driving, and industrial automation, where low latency and local data processing are critical. This trend is further augmented by the increasing prevalence of 5G networks, which offer the high-speed connectivity necessary for edge intelligence applications (Meuser et al., 2024). Fig. 4 illustrates the shift from a centralized, cloud-based use of AI to Edge AI solutions.

Strength Identification Relative to Disruption

Edge AI allows model training and inference directly at the edge, either in a collaborative form through direct interaction between edge devices or using local edge servers close to these devices. A notable trend is the emergence of distributed ML techniques for training and inference of AI models across multiple edge devices while preserving data security and privacy (Meuser et al., 2024). These models can be split into several submodels to perform inference of large AI models at the edge without compressing them via pruning or quantization. This allows for their distributed and collaborative execution on multiple, possibly heterogeneous, edge devices. Finally, hierarchical inference has been proposed where the interplay between larger and smaller neural network structures is leveraged toward accuracy, energy efficiency, and latency in edge-based inference scenarios (Meuser et al., 2024).

Weakness Identification Relative to Disruption

Despite its promise and potential, Edge AI can face significant challenges in large-scale deployment, including energy optimization, trustworthiness, and ethical issues. As an essential goal of sustainability, the energy consumption of Edge AI needs to be optimized. Concerning trustworthiness, Edge AI benefits from its closeness to the end devices. However, due to the distributed deployment with deep insights into a personal context, Edge AI services' safety and perceived trustworthiness are raising concerns among the stakeholders (Ding et al., 2022). In addition, if the accuracy of the AI model executed at the edge needs to be improved, it might be necessary to offload the task to the cloud. The cloud can provide high accuracy and send the results back to the edge (Ding et al., 2022).

Why Is This an Opportunity?

Edge AI offers the upstream industry a significant opportunity by allowing faster, more efficient decision-making in subsurface operations, reducing costs, and enhancing safety in operations. It opens doors for more sustainable energy production by optimizing resource extraction in real-time.

Why Is It a Threat?

Edge AI could potentially disrupt traditional data management and IT infrastructures. Companies that fail to adopt Edge AI may fall behind in operational efficiency, potentially losing competitive advantage in exploration and production.

How Edge AI Solves the Problem of Traditional AI Adoption?

Many of the challenges that have hindered the adoption of traditional AI in the upstream O&G industry can be effectively addressed by Edge AI. By processing data locally, Edge AI reduces the latency associated with cloud-based systems, crucial for real-time drilling and decision-making in well-monitoring operations. This local processing also lessens the need to transfer large amounts of data, resolving bandwidth issues in remote locations. Edge AI's ability to handle and analyze evolving data on-site leads to more accurate and timely predictions in subsurface exploration- where data is often incomplete or inconsistent. Processing data on-site as it evolves provides more accurate and timely predictions, improving overall decision-making in these unpredictable environments.

Additionally, local processing enhances data privacy and security by reducing the exposure of sensitive information to external networks, mitigating the risk of cyberattacks. Edge AI also lowers traditional cloud-based AI's infrastructure and computational costs, as it relies on smaller, localized devices for analysis. This makes AI adoption more cost-effective. Furthermore, by integrating data at the source, Edge AI streamlines the handling of diverse datasets, ensuring that insights are more relevant and actionable in specific operational contexts.

Further Areas of Research to Consider

Further research is needed to explore the integration of Edge AI with other emerging technologies like IoT sensors, 5G connectivity, or digital twins to further improve subsurface AI applications. Future research could also focus on improving Edge AI’s hardware to meet the computational demands of subsurface models.

Conclusion

Edge AI provides a practical solution to the challenges that have hindered the adoption of traditional AI in the upstream O&G industry, especially in subsurface exploration and production. By processing data locally, Edge AI significantly reduces latency and lightens the load on bandwidth, both of which are crucial for making fast decisions in remote and unpredictable environments. Moreover, Edge AI enhances data security by minimizing reliance on cloud infrastructure, directly addressing a key concern in the industry. Its ability to manage complex data in real time and function effectively in remote locations makes it particularly well-suited to the unique demands of upstream operations. However, the successful deployment of Edge AI will require ongoing investment in infrastructure, research, and collaboration to address challenges such as resource constraints and hardware capabilities. Overall, Edge AI has the potential to revolutionize the industry by enabling faster, more accurate, and more secure AI-driven decisions, paving the way for more efficient and sustainable exploration and production practices.

References

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