| Autor: |
Osama A. Abdelhamid, Shu J. Zhang, Aymen A. Alramadhan, Marko Maucec, Saad A. Al-Garni, Fabio Ravanelli, Stig Lyngra |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
Day 1 Mon, September 26, 2016. |
| DOI: |
10.2118/181325-ms |
| Popis: |
This paper presents an ensemble-based computer Assisted History Matching (AHM) of a real life carbonate oil field. The field-level reservoir pressures were matched with a fine-scale Dual-Porosity DualPermeability (DPDP) model spanning a long production history under primarily peripheral water injection pressure support. The well-level AHM workflow presented was validated with a DPDP high-resolution sector model of a fracture dominated carbonate reservoir. This sector model was ~17 million active grid cells with no application of simulation grid upscaling. The AHM workflow integrates probabilistic Bayesian inference using Ensemble Smoother with Multiple Data Assimilation (ES-MDA), which simultaneously assimilates the data and generates maximum a-posteriori updates of reservoir model parameters in a variance- minimizing update scheme. A detailed uncertainty matrix was built with ensemble of sensitivity scenarios, based on varying free water level, corresponding matrix porosity and the initial water saturation combined with geostatistical realizations of dynamic permeability derived from dynamic PLT logs and fracture characterization, where the varied parameters were the variogram attributes in terms of correlation length and geometric anisotropy. Five data assimilation iterations with ES-MDA method were required to achieve acceptable convergence and minimization of objective function, defined as a joint misfit of well-level static pressures and watercut for the key producing wells. Practical DPDP model simulation times were achieved through utilization of Massive Parallel Processing technology. This study presents the first ensemble-based approach to integrated reservoir modeling for a mature oil field with the objective to deliver geologically-constrained history matched models with better predictive value for production optimization and forecasting. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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