Summary Conditional stochastic modeling is becoming an established method for geological modeling. With this process, both seismic and well data are honored in all of the equiprobable realizations which are generated. These realizations provide descriptions of the interwell regions of the reservoir. The model used for reservoir characterization of a fluvial reservoir, includes both a description of reservoir heterogeneities at the facies level and the smaller scale level of variable, petrophysical properties within facies types. The Statfjord formation in the field in study is quite complicated and heterogeneous with high contrasts in permeabilities. Little information exists for the field in study. Four exploration wells have been drilled, one of these wells in the main fault block which is modelled in this study. The paper presents the marked point process which is used for the description of channel sands and overbank floodplain shales. The channel sands contain other internal facies with contrasting petrophysical properties. These internal facies are modelled within the channels. The permeability variation; are modelled for each facies type using a Gaussian random field. At both the facies and permeability stages of modeling, the stochastic model honors all the known data from wells, seismics, and sedimentological knowledged gained by the geologists from neighboring fields, outcrop studies, and general sedimentological principles. Permeability data, modelled in a very fine grid, is converted to an effective permeability in a coarser corner point geometry grid used in numerical reservoir simulation. The production profiles resulting from geological realizations carried forward to the reservoir simulator are compared with profiles obtained from a conventional model of the same structure. The uncertainty originating from the unknown facies architecture and permeability distribution is evident with analysis of the water production profile. Introduction The oil and gas fields which have been the aim of exploitation in the past in the North Sea have often been large compared to the present field development issues concerning smaller, geologically complex fields. Due to the high costs of drilling, production and transport, higher precision in performance estimation and qualification of uncertainties in production profiles are critical at all stages of development and exploitation. The probabilistic approach of stochastic modeling allows for generation of a range of reservoir models describing the reservoir architecture between the well penetrations. This offers the geologists and reservoir engineers a powerful tool which can better the quality of reservoir description from the traditional well-to-well correlation models. Recent papers of Clemetsen et al, Alabert and Massonnat, Guerillot et al, and Falt et al, describe several of the models which have been recently developed.
The petroleum industry is focusing on improved reservoir characterization. Decisions concerning development and depletion of hydrocarbon reservoirs must be made while giving consideration to the uncertainties of the formation involved. This requires combining geological and engineering data to develop a detailed reservoir model.Geostatistics and stochastic modeling techniques have emerged as promising approaches for integrating all relevant information and describing heterogeneous reservoirs. By use of stochastic techniques to generate a range of equiprobable reservoir descriptions, the uncertainty in the important reservoir parameters can be quantified. This quantification, together with the enhanced understanding of the reservoir characteristics given by stochastic reservoir modeling and visualization, provides an essential basis for making informed field-development decisions. This paper presents an integrated approach for stochastic reservoir evaluation. The presented approach has been implemented in the software system STORM.
High resolution sequence stratigraphical methods have been used in the detailed geological modeling of an element of the Ness formation of a North Sea field. This updated geological description has been used as input to the stochastic geological model MOHERES.14 realizations of facies architecture and petrophysical properties have been generated.The stochastically generated realizations have been scaled up to a refined element of an existing deterministic, history matched reservoir simulation model for the full Upper Brent reservoir. The upscaled versions of the realizations have been connected to the full field model, and reservoir simulations have been performed to compare twelve years of production history with simulated results for well production, RFT-and PLT-data. Six of the realizations gave good to very good results when compared to measured production data. These realizations were used for simulation of the future production performance to the year 2010, making it possible to estimate its uncertainty.
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