Application of CRM for Production and Remaining Oil Reserves Reservoir Allocation In Mature West Siberian Waterflood Field
- A. Gladkov (Modeling Technology Center JSC) | R. Sakhibgareev (Modeling Technology Center JSC) | D. Salimov (Modeling Technology Center JSC) | A. Skiba (Modeling Technology Center JSC) | O. Drozdov (Modeling Technology Center JSC)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 16-18 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 5 Reservoir Desciption & Dynamics, 7 Management and Information, 5.4 Improved and Enhanced Recovery, 5.5.8 History Matching, 5.5 Reservoir Simulation, 4.1 Processing Systems and Design, 7.6 Information Management and Systems, 4 Facilities Design, Construction and Operation, 4.1.2 Separation and Treating, 5.4.1 Waterflooding, 7.6.4 Data Mining
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A Capacity-Resistance Model was matched with production and injection data. Oil and water production splits and remaining reserves were calculated per reservoir unit. The results were validated with history matched streamline and conventional simulation model.
A Capacity-Resistance Model is based on material balance and signal processing of resistor-capacitor circuits where each circuit is represented by injector-producer pair per reservoir. The calibrated model is then combined with fractional flow model fit that is used to forecast water cut. CRM is based on injection and production data and injection splits. Based on this case study an engineer would have access to equations and methods that describe CRM predictive model and comparison with simulation models.
The relative simplicity of CRM model allows to use all available production and injection data in mature West-Siberian waterflood assets with large number of wells and to save time for decision-making on injection redistribution, infill wells, conformance treatment. CRM can be applied before simulation model is built and later the CRM results can be used in history match.
In this paper, we would like to analyze connectivity factors fij of CRM model and see how well the model matches the history.
|File Size||1 MB||Number of Pages||10|