Oil and Gas AI – ensuring Data and AI readiness for leading company in MEA
Advisory
Oil and Gas
MEA
Dominik Zuk
Co-Founder at Innsyte
Author’s historical experience before founding Innsyte
The oil and gas sector was undergoing a significant digital transformation, with companies striving to become data-driven organizations. A leading Oil and Gas company in the Middle East was navigating this shift, focusing on building modern analytics and AI capabilities. With their ongoing migration to Azure and the successful implementation of SAP HANA, the company has recognized the importance of transforming their data ecosystem. By addressing existing gaps and deploying advanced solutions, they aim to unlock new opportunities in operational efficiency and decision-making through AI and advanced analytics.
Improved data management understanding
Increased IT ecosystem capabilities
Established a long-term Data & AI strategy with use-cases
Problem: ensuring data and strategy readiness for AI in Oil & Gas company
In the journey towards digital transformation, this leading oil and gas company identified several critical gaps in their existing data landscape. These included a lack of a unified data platform, limited capabilities for advanced analytics, and non-standardized cloud management processes. With nine key data sources, including the Historian and multiple SAP modules (SD, HR, SCM, FM, and PM), the company faced challenges in data integration and governance.
To fully harness the power of AI and advanced analytics, they needed a robust data ecosystem that could serve as a “single source of truth,” providing reliable and actionable insights across the organization. This transformation was not just about technology but also about ensuring the right governance structures were in place. Cloud governance, especially with the OSDU (Open Subsurface Data Universe) implementation, was a key area of focus, aiming to establish a secure and compliant environment for AI-driven operations.
The opportunity was clear: by addressing these gaps, the company could significantly enhance its operational efficiency, achieve real-time insights into daily plant performance, and set the stage for future AI and advanced analytics initiatives. This would not only improve decision-making but also drive innovation in key areas such as predictive maintenance, hydrocarbon accounting, and production optimisation.
Discovered Analytics & AI use-cases: ideation at the forefront of plant operations
In the ever-evolving landscape of the oil and gas industry, leveraging AI to optimize operations across downstream, midstream, and upstream activities has become imperative. Through a comprehensive AI ideation process, we identified several high-impact use-cases that address critical challenges in operational efficiency, resource management, and sustainability. These use-cases not only enhance day-to-day operations but also align with the broader goals of achieving cost-effectiveness, environmental stewardship, and long-term strategic growth.
Below is a list of the operational AI use-cases discovered during this process, each designed to drive significant improvements across various aspects of the business.
Below is a list of the operational AI use-cases discovered during this process, each designed to drive significant improvements across various aspects of the business.
| Case name | Problem | Solution | Expected results |
| Analytics and reporting for daily plant Performance | Lack of visibility into daily production performance impedes timely decision-making and operational efficiency. | Implement daily production analysis for plant efficiency, providing real-time insights and reporting. | – Establishes visibility across the entire plant.- Management gains a clear understanding of daily performance.- Enables real-time commentary and decision-making on specific production issues. |
| Hydrocarbon accounting | Unaccounted losses of hydrocarbons during processing lead to inefficiencies and revenue loss. | Implement hydrocarbon accounting practices to monitor and minimize gas loss during processing. | – Accurate tracking of hydrocarbon losses.- Enhanced process efficiency and reduced wastage.- Improved financial accountability and resource management. |
| Drilling performance analysis | Inadequate understanding of drilling speed, pressure, and other variables leads to inefficiencies and higher costs. | Implement real-time drilling performance analysis using WITSML data and dataflow technology. | – Real-time monitoring and adjustment of drilling parameters.- Improved drilling efficiency and reduced operational costs.- Enhanced decision-making based on accurate, real-time data. |
| Predictive maintenance for rotating equipment | Unplanned equipment downtime disrupts production and increases maintenance costs. | Implement predictive maintenance strategies to monitor and preemptively service rotating equipment. | – Reduced unplanned downtime.- Extended equipment lifespan and improved reliability.- Lower maintenance costs through predictive interventions. |
| Predictive failure of alarms based on operational processes | Operational alarms frequently fail, leading to unnoticed issues and safety risks. | Deploy predictive algorithms to monitor and preemptively address potential alarm failures. | – Increased reliability of operational alarms.- Enhanced safety and operational integrity.- Proactive management of potential failures before they occur. |
Solution: Data & AI advisory leading to a long-term strategy
To close the gap between their current state and future goals, the oil and gas company began by exploring AI use cases and assessing data readiness. The first step was building a unified data platform using Azure to consolidate data sources and improve access for reporting and analytics. Alongside this, the DataOps approach was recommended to automate and scale data processes.
Workshops helped identify cloud governance gaps, leading to a new framework for secure and compliant data management. With this foundation, the company focused on AI use cases like plant performance monitoring and predictive maintenance. To ensure long-term success, adopting a CI/CD approach was suggested to streamline data deployments and maintain high standards across their AI solutions.
Workshops helped identify cloud governance gaps, leading to a new framework for secure and compliant data management. With this foundation, the company focused on AI use cases like plant performance monitoring and predictive maintenance. To ensure long-term success, adopting a CI/CD approach was suggested to streamline data deployments and maintain high standards across their AI solutions.
Overall, the solution provided a comprehensive roadmap for the company’s AI transformation, aligning their data management, cloud governance, and advanced analytics initiatives under a unified strategy. This long-term vision not only addressed immediate needs but also positioned the company to continuously innovate and improve in the years to come.
Impact: long-term vision for Oil and Gas company with Reasonable AI use-cases and implementation roadmap
The impact of this strategy and plan for AI transformation was profound, setting the stage for a data-driven future in the oil and gas sector. By establishing a unified data ecosystem, the company would significantly improve its data management capabilities, enabling real-time insights and better decision-making across the organization. The implementation of Azure facilitated seamless data integration, providing a single source of truth that could be relied upon for accurate and timely information.
The advanced analytics capabilities were planned to be acquired through PoC engagements. For example, the daily plant performance analysis was expected real-time visibility into key metrics, allowing managers to make informed decisions that optimized production processes. Predictive maintenance use cases was planned to prevent equipment failures, reducing downtime and improving operational efficiency.
Ultimately, the long-term vision established through this AI ideation process positioned the company as a leader in the oil and gas sector, demonstrating how reasonable AI use-cases could drive significant business value. The implementation roadmap provided clear guidance on the steps needed to achieve this vision, ensuring that the company could navigate the complexities of AI transformation with confidence.
“Through this Data & AI transformation plan, we have not only established a robust data foundation blueprint but also established a long-term vision that will guide us in unlocking the full potential of AI in our operations. The journey we embarked on with AI ideation has equipped us with the tools, processes, and strategies needed to lead in the digital age of Oil and Gas.”
Dominik Zuk, CFOO at Innsyte
Lessons learnt from Advisory in building data & analytics strategy
The journey of digitalising capabilities management for the customer representseda significant leap forward in HR analytics. By embracing a data-driven approach to re-skilling and talent management, the company has not only addressed immediate operational challenges but also laid the groundwork for sustainable growth and employee development. This initiative showcases the immense potential of HR analytics in unlocking employee potential and driving organisational success in the digital age.
Prioritise business use-cases
Start with high-impact business use-cases to demonstrate the value of AI and build momentum for broader adoption across the organization. We focused slightly too much on the technical readiness.
Modernize legacy systems
The transformation highlighted the need to modernize outdated systems to fully leverage AI and advanced analytics capabilities as client faced challenges to unified ecosystem.
OSDU framework
Oil and Gas sector has dedicated and open data platform framework. It is crucial to be aware and know the framework in Data readiness assessments.
Ensure early and continuous stakeholder alignment
Involving key stakeholders from the start is crucial for successful AI integration. This ensures clear communication, quicker decision-making, and addresses concerns proactively.
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