Enabling a New Era of Artificial Intelligence Modelling System. A Novel Approach to Optimize Production and Efficiency in the Greater Burgan
Authors: Al-Shemmari Y. , Al-Watyan A. , Al-Jabal K. , Freire J. L. , Sridhar G. , Tordecilla J. , Harkness C. , Johnson L. , Mooney K. 
Published: November 2024

The Kuwait Oil Company supergiant Greater Burgan Field has been producing billions of commercial volumes since 1946 from the primary clastic sandstone Burgan and Wara reservoirs and from the secondary Burgan Marrat, Magwa Marrat and Burgan Minagish carbonate reservoirs. Currently, the South and East Kuwait (S&EK) directorate stands as the largest and most crucial asset within Kuwait. S&EK produces roughly 1.5 million barrels of oil daily from about 2,000 operational wells that belong to 15 gathering centers. To sustain and increase production rates, intensive drilling and workover campaigns are essential including daily well interventions that involve an extensive range of activities and responsibilities to ensure the efficient performance of the asset. This includes overseeing a high volume of wells and operations, each of which requires meticulous attention to ensure optimal performance and output. Given the scale and complexity of these operations, decision-making processes related to production optimization and surveillance become inherently time-consuming. The magnitude of wells and the interconnectivity of pipelines necessitate thorough analysis, evaluation, and strategic planning before implementing any changes or improvements.

Production engineers rely on physics models to support operational decisions because these models can predict and simulate complex processes, enabling the assessment of various scenarios and their potential outcomes. However, creating models is considered a meticulous and demanding activity. In addition, in the asset the vast number of active wells, and the need to constantly troubleshoot and optimization operations, urge the need of a lightning-fast solution that provide confident information and insights prior making decisions.

This paper presents an agile and agnostic solution built on the interaction of production engineering knowledge, simulation technologies, and pioneering Physics-informed Artificial Intelligence (PiAI) to enable near real-time operational decision-making.

A science-infused AI framework was leveraged to create intelligence surrogate models, trained from data sets generated from physics-based models. To build the data set key inputs (reservoir/wellhead pressure, frequency, gas to oil ratio, water cut and productivity index) were sensitized based on real field operational ranges. An integrated fit-for-purpose production platform was developed using business intelligence tools was integrated with production and artificial lift data-ecosystems to allow simultaneous monitoring of well behavior and artificial-lift health. The platform maximized information to support decision-making in conducting an optimization, troubleshooting or debottleneck job. Embedding AI-based modelling component in the platform is a step change in efficiency and the way of working, moving from a static to an accurate, live and science-based solution.

The solution was deployed for both single wells and production network models covering all reservoir and production complexity in the asset (under natural depletion, waterflooding, artificial lift and natural flow wells). The integration of real production data obtained in the field with physics-principles within this framework enhances predictive capabilities. The accuracy of the PiAI surrogates models in terms of flow rates and pressures in the production system reach 97and 98% respectively in comparison to detail physics simulations.

The solution demonstrates the practical implementation of our approach and its ability to enhance overall efficiency to identify and increase production rates. Moreover, the solution is engineered to scale seamlessly to large networks, accommodating the complexities inherent in the production system.

The novel PiAI approach leveraged in the production platform provides quick predictions, making them suitable for near-real-time applications where immediate results are essential. Field development team can proactively seek, rank, and select high-impact production optimization opportunities in short period of time. Expected production gain can be derived accurately before executing the job maximizing efficiency and minimizing risk. The results unlock the true potential of the reservoir and maximum well completion capacity to balance the production outtake.