Enhancing Waterflooding Performance Using a combined Data Driven and Physical Modeling Approach

Capacitance Resistance Models (CRM) are increasingly recognized for their role in managing waterflooding and offering a streamlined subsurface representation for operational optimization. Since their introduction two decades ago, CRMs have evolved to address multi-phase flow and, with the integration of machine learning algorithms, have been extended to applications in gas, gas-alternating-water injection, and other enhanced oil recovery mechanisms. In this paper, we introduce a novel data-driven formulation that reduces the number of optimization parameters by 50% compared to traditional CRM approaches. This advancement not only enhances deployment efficiency in fields with numerous wells but also significantly accelerates CRM runtime. We validated our new formulation using several reproducible subsurface numerical simulation models that have been previously employed in CRM studies. Our validation also included tests on more compressible fluid systems (such as gas injection) and scenarios with significant changes in oil-water mobility. CRMs provide critical insights into well-to-well correlations in flooded reservoirs and are typically used for production optimization. However, this often requires the separation of production phases into water, gas, and hydrocarbon liquids, which is typically achieved using additional analytical or data-driven models in conjunction with CRM. In response, we present a novel method that integrates CRM with a pattern balancing algorithm, enabling optimization based on multiple objectives such as reservoir fill-up, sweep efficiency, and water injection effectiveness. Our extended formulation not only improves the accuracy of CRM models—evidenced by increases in R2 values from 0.6 to 0.95 in some cases—but also provides more precise subsurface insights, reducing the risk of misinterpreting reservoir trends as false well correlations. Finally, we explore the complementary role of production data-driven models alongside more detailed numerical reservoir simulation models.

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Enhancing Waterflood Efficiency Through Integrated Workflows and Advanced Analytics

Gerges,

Ahmed,

Belayouni

et al. 2024

Adipec

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Optimizing waterfloods requires a comprehensive approach that combines production-centric solutions with a focus on the subsurface reservoir. Holistic waterflood management requires a broad framework to cover all decision-making situations: operational, tactical, and strategic. In this paper, we seek to demonstrate a solution that combines physics-based streamlines flow dynamics together with advanced analytics to optimize short-term operational and tactical decision making. Our approach integrates physics-based streamline data with well production and injection rates to estimate fluid flow dynamics. This enables comprehensive analysis of well performance, facilitating identification of potential workover candidates for optimization. We create and manage workover strategies, estimating their effects for effective interventions. Utilizing opportunity screening logic, the solution identifies opportunities for production/injection adjustments or injector conversions. Automated history match analysis refines these opportunities, focusing on high-quality matches. Integration with reservoir management guidelines and an automated economics engine prioritizes actionable opportunities. Flexible data ingestion ensures updated insights for enhanced decision-making. The solution underwent successful piloting in two large ADNOC reservoirs with extensive waterflooding and thousands of wells. It generated numerous opportunities, enhancing understanding of injection-production dynamics. Notably, it provided actionable insights for short-term optimization, including injection / production increase / decrease and well conversions / shutdown. Serving as a decision support model, it offered alternatives and recommendations, optimized for operational and tactical decisions via comprehensive dashboards incorporating business KPIs. This hybrid model, combining physics-based results with data-driven analytics, maximizes dynamic reservoir model utilization for short-term optimization and robust decision-making. Integrated with traditional petroleum engineering insights, it streamlines workflows and bridges subsurface and surface integration gaps, facilitating more efficient operations and resource utilization. This paper introduces a novel approach to waterflood optimization, merging physics-based streamlines with advanced analytics for short-term operational and tactical decision-making. By integrating production-centric solutions with subsurface reservoir focus, it offers a holistic framework to address operational and tactical decision-making scenarios. This innovative methodology enhances traditional waterflood management practices, providing practicing engineers with a comprehensive toolset to optimize reservoir performance efficiently.

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Enhancing Waterflooding Performance Using a combined Data Driven and Physical Modeling Approach

Cited by 3 publications

References 17 publications

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Preliminary experimental study of low salinity effect on organic polar compounds desorption in Hassi Messaoud field-Algeria

A Novel Data-Driven Approach for Capacitance Resistance Models in Multi-Phase Flow Systems

Enhancing Waterflood Efficiency Through Integrated Workflows and Advanced Analytics

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