The Use of Hybrid Digital Models and Tracer-Based Dynamic Production Profiling in Intelligent Field Development

Today, the global oil and gas industry needs a solution to process huge data arrays due to a growing number of wells studied and emerging technologies that yield a broader range of information about the geological and technical factors of field development. Therefore, digital analytical tools are required enabling quick analysis of the data on production, well interventions, reservoir pressure, well interference, voidage replacement, and field studies. This paper describes the approaches to data processing and analysis employed during marker-based well logging at several large fields in the Russian Federation. The mentioned technology involves the use of quantum dot marker-reporters as high-precision flow indicators to obtain data on the flow profile and composition in horizontal wells for many years without well interventions. Data analysis was performed using hybrid digital models based on geological and reservoir modeling and a simplified physical reservoir model, involving machine-learning algorithms underlain by neural networks. This platform provides for structured storage of geological and engineering data and enables using dynamic production logging data in stochastic and traditional geological and reservoir modeling. A case study is described to demonstrate how the waterflooding system operation was optimized by applying complex analysis algorithms, generating a notable economic effect.