Importance of Dynamic Data in Detecting Reservoir Anomalies, A Case Study

Keyvan Miladi; Javad Mahdavi Kalatehno; Fatemeh Yousefmarzi; Ramin Eivazi

doi:10.52716/jprs.v16i1.1186

Authors

Keyvan Miladi Pasargad Energy Development Company (PEDC), Tehran, Iran.
Javad Mahdavi Kalatehno Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran.
Fatemeh Yousefmarzi Well Services of Iran (Schlumberger Methods), Tehran, Iran.
Ramin Eivazi Pasargad Energy Development Company (PEDC), Tehran, Iran.

DOI:

https://doi.org/10.52716/jprs.v16i1.1186

Keywords:

Static Data, Dynamic Data, Fluid Typing, Fluid Profiling, Static and Dynamic data integration, Fluid Saturation, Well Development Plan, Core Data, Well Logging, Well Testing

Abstract

The exploitation of oil reservoirs is a critical activity in the energy industry, contributing significantly to global energy needs. Accurate assessment of reservoir properties and fluid characteristics is vital for effective operations in oil and gas reservoirs. In order to reach this principle, static and dynamic data are integrated to provide a comprehensive understanding of reservoir behavior. However, it is possible for these data not to yield the same results; in which scenario selecting correct and accurate data is essential. This study tries to reveal the importance and essentiality of dynamic data. A case study alongside its numerous data is referenced to support the importance of data integration, demonstrating its effectiveness in improving reservoir understanding, fluid typing, and reservoir connectivity assessment. By analyzing the real case study, studying the results of static and dynamic data, and investigating their impact on the well’s development path, the undeniable need for dynamic data and their priority is demonstrated. More importantly, this study presents a comprehensive diagnostic workflow for reconciling conflicts between static and dynamic data in highly heterogeneous reservoirs. The workflow consists of systematic data quality control, cross-validation of independent measurements, and extended fluid analysis (LFA) to resolve discrepancies. The novelty of this work lies in documenting the step-by-step approach for diagnosing and resolving data conflicts, rather than only reiterating the established principle of dynamic data priority.

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