Transforming Production Management and Forecasting for Oil and Gas Assets with an Automated, Multilevel Digital Solution

Abstract Production forecasting and management in oil and gas operations traditionally rely on manual, Excel-based workflows that pose significant challenges in accuracy, efficiency, and integration of operational activities. This paper presents an innovative web-based digital solution that revolutionizes production management by automating forecasting processes while incorporating drilling, workover, and field-level technological interventions across multiple organizational levels. The solution, developed using the Dash framework within the Dataiku platform, implements an enhanced decline curve analysis (DCA) model that dynamically adjusts to various operational activities. The system automatically calculates decline rates and initial production profiles for both existing and planned wells, integrating these rates and profiles with baseline production forecasts. A key innovation of the system is the solution's ability to handle quarterly plan updates, allowing for modifications in activity timing, well specifications, and technological implementations while maintaining data integrity and tracking capabilities. Implementation results demonstrate significant improvements in forecasting accuracy and operational efficiency. The automated workflow eliminates manual errors, reduces forecasting time, and provides real-time visibility into production trends from individual wells to enterprise-wide assets. The solution's hierarchical approach enables comprehensive tracking of planned vs. actual production, facilitating improved decision-making and resource allocation. Users can seamlessly update operational plans, visualize production impacts, and generate forecasts at multiple organizational levels. The system's innovative features include automatic detection of new wells, correction of historical production data based on field-level reports, and dynamic adjustment of decline rates considering various operational scenarios. The solution also provides robust quality control mechanisms, identifying data inconsistencies and allowing users to correct anomalies efficiently. This comprehensive approach to production management represents a significant advancement over traditional DCA-based methods, offering a new standard for integrated production forecasting in the petroleum industry.

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