Huda Al-Mutairi
Senior Systems Analyst
Kuwait Oil Company
Huda Al-Mutairi is a Computer Engineer with a Master’s degree in Business Administration (MBA), currently serving as a Senior Systems Analyst at Kuwait Oil Company (KOC) within the Innovation & Technology Group. With a strong background in SCADA systems, digital transformation, and real-time data integration, Huda plays a key role in driving innovation initiatives and enabling digital oilfield solutions across the company. She combines technical expertise with strategic business insight to support the development and deployment of cutting-edge technologies in the oil and gas sector.
Participates in
TECHNICAL PROGRAMME | Energy Technologies
The Energy Transition: The Role of Digitalisation, AI, and Cybersecurity
Forum 23 | Digital Poster Plaza 4
30
April
10:00
12:00
UTC+3
The Kuwait Integrated Digital Field (KwIDF) is an intelligent oil field platform that integrates artificial intelligence (AI), advanced data analytics, and a management-by-exception framework. This platform aligns with Kuwait Oil Company’s (KOC) digitalization strategy by significantly enhancing operational efficiency, sustainability, and resilience in oil production.
KwIDF enables objective detection of production anomalies through comprehensive real-time data (RTD) analytics, facilitating timely interventions and informed decision-making. This abstract highlights four real-world use cases from active oil fields in Kuwait, showcasing how data-driven methodologies delivered operational improvements:
Well-0001 (T1): Identified as producing gas exclusively, deviating from expected liquid output.
Well-0002: Showed significant variance from historical production benchmarks.
Well-0003: Displayed irregular flow measurements, suggesting impaired operations.
Well-0004: Experienced unexplained production decline despite equipment operating within normal parameters.
Targeted interventions based on these insights included zone adjustments, historical production evaluations, workover operations, and real-time decision-making. The platform employed advanced analytical techniques such as K-means clustering for production grouping, statistical process control (SPC) for adaptive benchmarking, principal component analysis (PCA) for managing multivariate data, and linear regression with signal normalization for predictive modeling.
As a result, the platform recovered an estimated 2,000 to 2,700 barrels per day across the four wells, demonstrating substantial operational and economic benefits. Additionally, KwIDF enhanced safety by reducing manual interventions, increased manpower productivity through automation, and promoted operational excellence through predictive decision-making.
Further analysis revealed a broader optimization potential, identifying 308,752 barrels/day as recoverable lost production across 1,258 wells, and 183,414 barrels/day as actionable opportunities across 854 wells.
In summary, these use cases validate how AI-driven tools and analytics within KwIDF have improved production efficiency, enabled timely action, and uncovered untapped potential in mature oil fields.
KwIDF enables objective detection of production anomalies through comprehensive real-time data (RTD) analytics, facilitating timely interventions and informed decision-making. This abstract highlights four real-world use cases from active oil fields in Kuwait, showcasing how data-driven methodologies delivered operational improvements:
Well-0001 (T1): Identified as producing gas exclusively, deviating from expected liquid output.
Well-0002: Showed significant variance from historical production benchmarks.
Well-0003: Displayed irregular flow measurements, suggesting impaired operations.
Well-0004: Experienced unexplained production decline despite equipment operating within normal parameters.
Targeted interventions based on these insights included zone adjustments, historical production evaluations, workover operations, and real-time decision-making. The platform employed advanced analytical techniques such as K-means clustering for production grouping, statistical process control (SPC) for adaptive benchmarking, principal component analysis (PCA) for managing multivariate data, and linear regression with signal normalization for predictive modeling.
As a result, the platform recovered an estimated 2,000 to 2,700 barrels per day across the four wells, demonstrating substantial operational and economic benefits. Additionally, KwIDF enhanced safety by reducing manual interventions, increased manpower productivity through automation, and promoted operational excellence through predictive decision-making.
Further analysis revealed a broader optimization potential, identifying 308,752 barrels/day as recoverable lost production across 1,258 wells, and 183,414 barrels/day as actionable opportunities across 854 wells.
In summary, these use cases validate how AI-driven tools and analytics within KwIDF have improved production efficiency, enabled timely action, and uncovered untapped potential in mature oil fields.
