Case Study on Improving Operational Efficiency in Deepwater Assets Through Physics-Informed Data-Driven Hybrid Modeling
Authors: Mustafa Azreen , Ravuri Meher , Vierkandt Steven , Razi Siti Abdullah , Ismail Wan Akmal Wan , Heah Lianne , Rodriguez Dryonis Pertuso , Sabharwal Varad , Chauhan Prithvi Singh , Sankaran Sathish 
Published: February 2025

Abstract
Optimizing hydrocarbon field production is a multifaceted and demanding process that hinges on precise reservoir understanding and effective data management. Traditional well and reservoir monitoring methods are manual and interpretive and struggle to scale across numerous wells with large data volumes, hindering timely decision-making. Can hybrid models, combining physics and data-driven approaches, be utilized for automated real-time performance monitoring, early detection of anomalies, identification of actionable opportunities, and continuous system optimization?
A digital solution integrating various reservoir and production workflows was deployed to enhance asset operational efficiency. Event detection algorithms for identifying well shut-ins and stable flow periods catalog planned and unplanned occurrences during regular field operations. Shut-in analyses are conducted automatically to derive key well parameters (e.g., productivity index, average reservoir pressure) and reservoir diagnostics using a hybrid model (a fusion of physics-based and data-driven methods). Any performance deviations, such as a decline in productivity, liquid loading, choke erosion, or water/gas breakthrough, are flagged as anomalies, facilitating exception-based surveillance. Well, steady-state detection algorithms update inflows in response to changes in operational points. The integrated production system, including wells and pipeline networks, is continuously calibrated and leveraged for production optimization and short-term business forecasting by assessing multiple scenarios.
This paper outlines the application of the integrated hybrid modeling system to shallow water assets. The system enabled rapid evaluation and identification of several valuable opportunities for fields with significant reservoir uncertainties. Event detection algorithms improved model calibration data density for shut-in analysis by over 200%. An unsupervised learning method for predicting choke erosion was also validated through hindcasting, potentially preventing costly choke washouts with early intervention. Self-calibrating models facilitated ongoing, enhanced scenario optimization for integrated asset management, leading to a 10-15% increase in field production. Model quality improved as calibration frequency shifted from manual updates twice a year to automated weekly updates. To enhance the efficiency of the platform a virtual metering pilot was designed to address challenges related to measurements in the field. Monthly well reviews were also facilitated by the platform.
The field application demonstrates the efficiency of the integrated hybrid modeling system in automating performance tracking, early anomaly detection, opportunity identification, and continuous optimization. It enhances asset operational efficiency, maximizes production, and reduces costs by preventing expensive issues like choke washouts through early intervention. This solution exemplifies the next generation of hybrid modeling tools, enabling continuous asset optimization, and augmenting engineers’ capabilities by merging data-driven insights with physical understanding.