Implementation of Physics-Based Data-Driven Models With a Commercial Simulator
- Guotong Ren (U. of Tulsa) | Jincong He (Chevron Corporation) | Zhenzhen Wang (Chevron Corporation) | Rami M. Younis (U. of Tulsa) | Xian-Huan Wen (Chevron Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Simulation Conference, 10-11 April, Galveston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.4.6 Thermal Methods, 2 Well completion, 5.6 Formation Evaluation & Management, 5.1.5 Geologic Modeling, 5 Reservoir Desciption & Dynamics, 5.5 Reservoir Simulation, 5.4.1 Waterflooding, 2.1.3 Completion Equipment, 5.4 Improved and Enhanced Recovery, 2.2 Installation and Completion Operations, 5.5.8 History Matching, 5.6.9 Production Forecasting
- Proxy model, Steam flooding, Water flooding
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The use of full-physics models in close-loop reservoir management can be computationally prohibitive as a large number of simulation runs are required for history matching and optimization. In this paper we propose the use of a physics-based data-driven model to accelerate reservoir management and we describe how it could be implemented with a commercial simulator.
In the proposed model, the reservoir is modeled as a network of 1D flow paths connecting perforations at different wells. These flow paths are discretized and the properties at each grid block along each flow path are derived from history matching of production data. To simulate flow in this network model through a commercial simulator with all the physics, an equivalent 2D Cartesian model is set up in which each row corresponds to one of the 1D flow paths. Finally, the history matching is performed with ensemble smoother with multiple data assimilation (ESMDA).
The proposed network model is tested on both waterflood and steamflood problems. It is demonstrated that the proposed model matches with well-level production history (including pressure and phase flow rate) well. The calibrated ensemble from ESMDA also provided a satisfactory probabilistic forecast of future production that almost always envelops the true solutions. This indicates that the proposed model, after calibrated with production data, is accurate enough for production forecast and optimization. In addition, the use of commercial simulator in the network model provided flexibility to account for complex physics, as demonstrated by the successfully application to the steamflood problem. Compared with traditional workflow that goes through the full cycle of geological modelling, history matching and probabilistic forecasting, the proposed network model only requires production data and can be built within hours. The resulted network model also runs much faster than a full-physics as it typically has much less grid blocks. We expect the proposed method to be most useful for mature fields when abundant of production data is available.
As far as we know, this is first time a physics-based data-driven model is implemented with a commercial simulator. The use of commercial simulator makes it easy to extend the model for complex reservoir such as thermal or compositional reservoirs.
|File Size||1 MB||Number of Pages||20|