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Facies (Rocktype) Modeling Using Inverse Static Model Process from Porosity Distribution, Case from Baturaja Formation

Abstract

Reservoir characterizations are intended to transfer geological and geophysical data into digital data, so that the model can reflect the best subsurface condition. Several state of the art geostatistical techniques have recently been used to construct a full-field, three dimensional (3D) models. Constructing reservoir models has become a significant step in resource development as reservoir modeling provides a spot to integrate and compile all available data and geologic concept. The successful application of these reservoir models is used to calculating reserves and input for further process (simulation). Pyrite Structure is located in the South Sumatera Basin that produces gas and condensate from limestone of Baturaja Formation that has complex and heterogeneous geological and petrophysical characteristics, and exhibit complex porosity-permeability system. This structure provides geophysical and geological data such as acoustic impedance (AI) attributes, log data, and core data as well. An acoustic impedance attribute is commonly used as the trend for the reservoir modeling. The problem rises since the AI trend at this structure cannot directly represent the facies trend. Hence, the inverse modeling process used to determine facies (rock type). Since AI data has high correlation with porosity and consistent with geological concept, this modeling use inverse modeling process to determine facies (rock type) from porosity model. First approach is to distribute porosity model guided by variogram analysis and AI. Second, after porosity log distributed, probability map from rock type-1, rock type-2 and rock type-3 generated from porosity model. Then the trend modeling for each rock type made and used to guide facies model distribution. Furthermore, the facies models along with the AI trend are used to create the new porosity distribution model. This porosity model and the permeability-porosity logarithmic correlation equation are used to make the permeability distribution model. At last, by using J-Function and consider the water saturation is the function of depth and rock quality, the water saturation model can be also created using the facies model and constrained by gas-water contact. Those reservoir properties models provide better depiction of their trend that is fit with AI trend. Those models also can reduce the hesitation in calculating hydrocarbon volumetric