Maham Nadeem
Student
Heriot-Watt University Dubai Campus
Motivated 3rd-year Electrical Engineering student at Heriot-Watt Universityactively pursuing an internship opportunity to apply and expand my technicalknowledge in a real-world environment. With strong communication, problem-solving, and time management skills, I have gained practical experience inprogramming through the Praxis Programming course, where I honed my ability todevelop solutions for complex engineering problems. Eager to contribute to adynamic team, I am excited to leverage my academic background while learningfrom industry profess
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
In mature reservoirs, the identification and placement of infill wells with high productivity indices (PI) is critical for maximizing recovery and extending field life. Traditional methods of infill well planning rely heavily on reservoir simulation, geostatistical mapping, and expert judgment, which can be time-consuming and may overlook complex, nonlinear relationships between subsurface properties and well performance. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer transformative potential to enhance infill well planning by leveraging large volumes of historical and real-time reservoir data to identify optimal drilling locations and predict well productivity with greater accuracy.
This paper presents a novel AI/ML-driven workflow for designing high-productivity infill wells by integrating multi-source data, including static (geological, petrophysical) and dynamic (production, pressure, injection) datasets. The workflow begins with a thorough data pre-processing and feature engineering phase, followed by supervised ML modeling techniques such as gradient boosting, random forest, and deep neural networks to predict productivity index (PI) and other key performance indicators (KPIs). Spatial correlation techniques and unsupervised learning methods, such as clustering and self-organizing maps (SOMs), are then used to identify underdeveloped sweet spots and optimize well placement within the reservoir.
A case study from a mature carbonate field in the Middle East demonstrates the practical application and benefits of this AI/ML-based approach. Historical production data from over 150 wells were used to train and validate the model, achieving a PI prediction accuracy of over 85% when compared to actual results. The AI-recommended well locations not only avoided areas of interference and pressure depletion but also targeted zones with higher remaining hydrocarbons and better reservoir quality, leading to a 25% improvement in average PI compared to traditionally planned infill wells.
The results underscore the potential of AI and ML to significantly improve the efficiency and success rate of infill drilling campaigns. By automating pattern recognition and decision-making based on vast and complex datasets, these technologies can reduce planning cycles, enhance reservoir understanding, and ultimately improve economic outcomes. This study advocates for a paradigm shift in well planning strategies, where AI/ML complements domain expertise to achieve optimized field development in a cost-effective and timely manner.
This paper presents a novel AI/ML-driven workflow for designing high-productivity infill wells by integrating multi-source data, including static (geological, petrophysical) and dynamic (production, pressure, injection) datasets. The workflow begins with a thorough data pre-processing and feature engineering phase, followed by supervised ML modeling techniques such as gradient boosting, random forest, and deep neural networks to predict productivity index (PI) and other key performance indicators (KPIs). Spatial correlation techniques and unsupervised learning methods, such as clustering and self-organizing maps (SOMs), are then used to identify underdeveloped sweet spots and optimize well placement within the reservoir.
A case study from a mature carbonate field in the Middle East demonstrates the practical application and benefits of this AI/ML-based approach. Historical production data from over 150 wells were used to train and validate the model, achieving a PI prediction accuracy of over 85% when compared to actual results. The AI-recommended well locations not only avoided areas of interference and pressure depletion but also targeted zones with higher remaining hydrocarbons and better reservoir quality, leading to a 25% improvement in average PI compared to traditionally planned infill wells.
The results underscore the potential of AI and ML to significantly improve the efficiency and success rate of infill drilling campaigns. By automating pattern recognition and decision-making based on vast and complex datasets, these technologies can reduce planning cycles, enhance reservoir understanding, and ultimately improve economic outcomes. This study advocates for a paradigm shift in well planning strategies, where AI/ML complements domain expertise to achieve optimized field development in a cost-effective and timely manner.
