Real-Time Production Logs Surveillance: Automated Workflow Technology Allows Synthetic Sensing of Oilfield Downhole Production

Authors
Mustafa Bawazir (Schlumberger) | Mohammed Dallag (Schlumberger) | Sourabh Shukla (Schlumberger)
DOI
https://doi.org/10.2118/196390-MS
Document ID
SPE-196390-MS
Publisher
Society of Petroleum Engineers
Source
SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, 29-31 October, Bali, Indonesia
Publication Date
2019
Document Type
Conference Paper
Language
English
ISBN
978-1-61399-647-8
Copyright
2019. Society of Petroleum Engineers
Keywords
Surveillance, Reservoir Management, Production , Workflow , Methods, Synthetic Time-Lapse , Data Analytics , Dynamic , Workover, Well Intervention , Reservoir Characterization , Production Logging, Automated Workflow , Time-Lapse , Flow Profile
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Abstract

One goal for oil fields of the future is acquiring continuous and on-demand data as required for field and reservoir management. Synthetic time-lapse production methods are becoming a way of providing this information at and away from wells. Time-lapse production log data acquired over oil fields is used to monitor water sweep in the reservoir. Production logs provide a direct measure of the fluid flowing downhole and detect the unwanted fluid entries. In field applications, this advanced scanning of fluid profiling successfully derisked several infill well locations and identified new workover candidates and drilling opportunities in the fields.

Synthetic time-lapse production logging is a useful complement to understanding reservoir heterogeneity and complexity through tailored synthetic and real data integration. A computer-based workflow has been developed to automate the downhole production flow profile. Production performance of the well is assessed, considering the dynamic time-lapse logging data. A synthetic flow profile is constructed to show the change in water production signature, and the well is further examined if it undergoes remedial actions. Reservoir characterization is a continuous process during the life of the oil field. As new data are available, the model is updated and contains more details. The incorporation of all data allows increased accuracy and reduced uncertainty in characterizing the reservoir.

The proposed methodology requires the acquisition of dynamic production logging data to establish a solid workflow and validate the model. Uncertainty can be eliminated with the acquisition of additional production logs. Recommendations for improvement of the current well condition can be made to reduce the well water cut and improve oil production from the well. Consequently, well classification and candidate selection for workover can be achieved. The results of this work demonstrate the strength of applying multidisciplinary team efforts to develop automated workflows that are relevant to reservoir and production engineers who deal with complex reservoirs with numerous wells.

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