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|Title: ||Stochastic modelling of the reservoir lithological and petrophysical attributes. A case study of the Middle East carbonate reservoir|
|Authors: ||Kravets, Svetlana|
|Advisor: ||Almeida, José|
|Keywords: ||Carbonate reservoir|
|Issue Date: ||2012|
|Publisher: ||Faculdade de Ciências e Tecnologia|
|Abstract: ||Carbonate reservoirs represent the significant part of oil and gas production. They produce about 50% of hydrocarbons globally. In order to provide the rational exploitation of deposits in carbonate reservoirs it is necessary to ensure accurate prediction and effectively overcome the technical barriers that occur in a complex carbonate formations. The main rules for successful project are to develop and apply reservoir characteristics, to predict performance and productivity, effectively manage diagenesis to optimize production and maximize recovery through reservoir simulation technology. The great development of digital modelling technologies gives the opportunities to solve these problems.
Generation of models of carbonate reservoir rocks by simulating the results of the geological processes involved is very complicated. Mainly because the rock may have undergone several phases of diagenetic processes that might have modified or even completely overprinted texture and fabrics of the original carbonate rock. In spite of this problem, a modelling technique, originally developed for sandstones, has successfully been extended for the 3D modeling of carbonate reservoir rocks. The input data to the modelling is obtained from the geophysical data and logging. In the present work, the virtual pore scale models of carbonates were produced by simulating the results of the geological processes.
The implemented methodology was divided into two main steps. The first stage was a Lithoclasses Modelling. The 3D stochastic geological model of the lithology was produced by the Sequential Indicator Simulation (SIS) algorithm. The second stage was an attribute modelling. The main properties such as porosity and permeability were computed according to the lithoclasses via Direct Sequential Simulation (DSS) algorithm with local histograms. The comparison of the two data sets showed high convergence for the main calculated properties. In the final stage of the work the geobody analysis was conducted. This type of the connectivity analysis performed the geometry of geological facies, trends for property distribution and permeability barriers.|
|Description: ||Dissertação para obtenção do Grau de Mestre em Engenharia Geológica (Georrecursos)|
|Appears in Collections:||FCT: DCT - Dissertações de Mestrado|
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