Integrated Reservoir Geological Modeling and Quantitative Uncertainty Appraisement of Models

Li Gu¹, Chunliang Huo², Chunming Zhao², Guangyi Hu¹, Song Liu², and Qinghong Yang^2
1Research center, CNOOC, Beijing, China
²Tianjin Branch, CNOOC, Tianjin, China

Taking Lower Minghuazhen Formation, Neocene, B Oilfield in Bohaiwan Basin as an example, the paper discusses a method of reservoir modeling incorporating geological data, logs and seismic data and a method of quantitative uncertainty appraisement of models.

The reservoir of B Oilfield belongs to a distal delta system and is featured by distributary channels in delta front. The complicated distribution of channel sand requires facies-constrained modeling method. Reasonable division of modeling units, geological knowledge base and the corporation of seismic data are the key of geological modeling. Division of modeling units emphasizes the reasonable vertical division and horizontal correlation of reservoir. The corporation of 3D-seismic data with well data yields geological knowledge base, including quantitative description of microfiches, flow lines of channels, probability function of facies, and connection probability of sand bodies in each unit. Object-based simulation method is applied in facies modeling.

To appraise uncertainty and optimum seeking of models is important after geological modeling. Uncertainty mainly lies in the geological knowledge and in the parameters of reserves calculation. Thus, experiment designing was used to single out a set of 9 geological scenarios to describe all likelihoods. OOIP and drained OOIP, respectively reflecting the scale and the connectivity of the reservoir, were used as the quantitative indexes. Based on the 9 realizations of the 9 modeling scenarios, the multinomial response surfaces of the main uncertainty factors with OOIP and drained OOIP were built up respectively, which were used as the substitute for modeling. Then the 3P probabilistic reserves were calculated by Monte Carlo method. A set of realizations was selected out by optimum seeking for numerical reservoir simulation, which covered the risks of the reservoir.

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