Sankey predictive flow analysis for minimizing deferred production: A digital transformation approach. Mature oil fields in Northwest Peru.

Abstract

This research addresses the low productivity in mature fields of northwestern Peru, caused by manual and fragmented management that generates high deferred production and late diagnoses. The objective is to propose a Digital Transformation model using Sankey Predictive Flow Analysis to integrate operational data, optimize decisions, and maximize hydrocarbon recovery. The methodology is applied, non-experimental, and descriptive, with a qualitative-quantitative approach. Industry 4.0 technologies such as IoT, SCADA, and Pump Controllers (POCs) were integrated. The Sankey Diagram was used for the predictive visualization of fluid balances (oil, gas, and water), validating the data architecture and information flow between Operational Technology (OT) and Information Technology (IT) under international standards. The results highlight a reduction in response latency from 9 days to just 1 (an 88% improvement). This minimizes deferred production, achieving a recovery rate of between 2% and 8% of the volume. Furthermore, efficiency was optimized by reducing OPEX by between 3% and 5%, achieving a return on investment exceeding 20%. It is concluded that Sankey Predictive Analytics constitutes the cornerstone of operational visibility for systemic management from well to sale. Beyond the investment in sensors, Hui et al. assert that digitalization is establishing itself as the new operational standard; indispensable for the sustainability of the oil industry on the northern coast of Peru.