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Abstract

This paper introduces the bases for the design of next-generation automated workflows to implement advanced assisted history-matching (AHM) techniques. The paper presents procedures for geostatistical modeling, high-end dynamic flow simulation modeling, and the use of streamline tracing and visualization to generate a basic (fundamental) model for AHM. The accuracy of the base model is essential because this model is the starting point of the AHM process; therefore, the quality of the AHM process is dependent on the base model. The geomodel benefits from a combination of multiple lithotype proportion mapping (LPM) and plurigaussian simulation (PGS), which successfully represents complex, carbonate depositional settings with eight lithofacies and high-permeability channels. By honoring geostatistical variograms and core-log constraints, a reservoir model is generated with 1.4 million cells. The LPM indicated that 108 layers are sufficient to describe the vertical resolution of lithofacies in the reservoir. A three-dimensional (3D), three-phase, black-oil single-porosity numerical simulation model was developed. The dynamic model has three-phase relative permeability normalization that computes the effects of parameterizing rock type and permeability distribution in the static model. The model is complex, as it has 16 equilibrium regions and two pressure volume temperature (PVT) regions. The simulation model includes 49 wells in 5 waterflood patterns to match 50 years of production, 12 years of injection, and 8 years of forecasting. The model was optimized for minimum simulation time. The base case was used for a) closed-loop, multilevel probabilistic history matching with parameterization of geostatistical and reservoir-dynamic properties and b) dynamic model ranking (DMR) and uncertainty quantification based on predicted oil recovery factor (ORF). This workflow was implemented at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. It generates faster and more accurate history matching updates, produces a high-resolution reservoir model with no upscaling, and calculates waterflood indicators, including voidage replacement, water injector efficiency, producer well allocations, sweep efficiencies, and recovery factors.

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/content/papers/10.3997/2214-4609-pdb.395.IPTC-17518-MS
2014-01-19
2024-03-28
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.395.IPTC-17518-MS
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