Abstract

Abstract The generating and usage of conceptual geological models become an integral part of reservoir characterisation studies, because they represent geologist understanding of the reservoir. With understanding of uncertainty associated with the conceptual models, their usage could be expanded to accomplish critical task such as inplace hydrocarbon (IPH) estimation. This paper presents a recommended workflow for building a conceptual geological model for a fluvial system and discusses the different uncertainties associated with it. Afterward, Its uses this workflow to build a conceptual geological model for a gas reservoir in the Cooper Basin, South Australia and estimates the IPH through stochastic modeling method. Building a fully integrated conceptual geological model involves input data of different scales such as core data, well logs and analogues, and each input has its own uncertainty. The process of building a conceptual model is made up of different steps such as core description and depositional facies identifications. Uncertainty cannot be eliminated, but it can be reduced. It cannot be eliminated because the accurate description of the reservoir is not known and will not be known precisely, but using available data will help in narrowing down the possible choices. Inplace hydrocarbon estimates using stochastic modeling method was preformed for a gas reservoir in the Cooper Basin. There were two stochastic porosity models built using the same input data, but one model was based on a conceptual model and the other was not. The results showed that the use of conceptual models has given higher oil and gas estimates. This is due to the mechanism of the porosity simulation based on geological data. The porosity is simulated independently in each facies object in each reservoir interval. Another advantage of using the conceptual model is improvements in the continuity of porosity. Without the facies objects, porosity continuity will be dependent of semivariograms and values at wells. In the absence of any well data, the continuity will be estimated for the semivariogram parameters namely range and direction.

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