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Technical Papers

Harnessing Machine Learning and Artificial Intelligence in Remote Upstream EOR Projects

In the era of digital transformation, the integration of Machine Learning (ML) and Artificial Intelligence (AI) is revolutionizing the landscape of Enhanced Oil Recovery (EOR) projects in the upstream oil and gas sector. Particularly in challenging environments such as remote desert locations, these technologies can significantly optimize project execution. The stewardship principle of PMBOK’s 7th Edition emphasizes the efficient use of resources, which can be greatly enhanced through predictive analytics and optimization algorithms of ML and AI. These technologies can aid in logistical planning, enabling optimal use of scarce resources such as water and challenging road networks.

• Predictive Analytics: AI and ML can predict future scenarios, helping project teams make more informed decisions and optimize resources.

• Optimization Algorithms: These tools can determine the most efficient ways to use available resources, minimizing waste and maximizing productivity.

A strong, competent team is crucial for any project, more so when dealing with intricate technologies like ML and AI. Effective stakeholder engagement is also critical, as the introduction of these technologies might raise concerns that need to be addressed transparently and proactively. AI and ML can also facilitate a systems thinking approach by modeling the complex interactions between geology, technology, and market dynamics, and predicting the potential outcomes of various scenarios.

• Team Competence: A team well-versed in AI and ML is crucial for implementing these technologies effectively.

• Stakeholder Engagement: Transparent communication about the benefits and potential risks of these technologies can help address stakeholder concerns.

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