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Reservoir characterization, especially during early stages of reservoir life, is very uncertain, due to the scarcity of data. Reservoir connectivity and permeability evaluation is of great importance in reservoir characterization. The conventional approach to addressing this is computationally very expensive and time consuming. Therefore, there is a great incentive to produce much simpler alternative methods. In this paper, we use a statistical approach called the percolation theory, which considers a hypothesis wherein the reservoir can be split into either permeable (i.e. sand/fracture) or impermeable flow units (i.e. shale/matrix), and assumes that the connectivity of permeability... 

Reservoir characterization, especially during early stages of reservoir life, is very uncertain, due to the scarcity of data. Reservoir connectivity and permeability evaluation is of great importance in reservoir characterization. The conventional approach to addressing this is computationally very expensive and time consuming. Therefore, there is a great incentive to produce much simpler alternative methods. In this paper, we use a statistical approach called the percolation theory, which considers a hypothesis wherein the reservoir can be split into either permeable (i.e. sand/fracture) or impermeable flow units (i.e. shale/matrix), and assumes that the connectivity of permeability... 

The construction of a facies model could be employed as a conditional data for any property simulation that results in a more reliable reservoir characterization in further steps. In this study, an Iranian gas reservoir with six wells was studied to determine the 3D reservoir facies model. Fifteen reservoir facies were first detected along one of the wells with detailed core and thin section descriptions. Due to the significant difference between the core and log data resolution, facies were clustered into four major groups regarding the digenetic processes and petrophysical lithofacies properties (permeability and porosity). The lithofacies specification effect on petrophysical properties... 

Accurate knowledge of pure hydrocarbon and natural gas viscosity is essential for reliable reservoir characterization and simulation as well as economic design of natural gas processing and transport units. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. When there is no available experimental data for the required composition, pressure, and temperature conditions, the use of predictive methods becomes important. In this communication, a novel approach was proposed to develop for prediction of viscosity of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide,... 

The authors studied the efficiency and accuracy of neural network model for prediction of permeability as a key parameter in reservoir characterization. So, some multilayer perceptron (MLP) neural network models with different learning algorithms of Levenberg-Margnardt, back propagation, improved back propagation (IBP), and quick propagation with three layers and different node numbers (3, 4, 5, 6, 7) in the middle layer have been presented. These models have been obtained by 630 permeability data from one of offshore reservoirs located in Saudi Arabia. The accuracy of models was studied by comparing the obtained results of each model with experimental data. So, the neural network with IBP... 

This paper presents a new combined method for accurate upscaling of two-phase displacements in highly heterogeneous reservoirs. The method has the capability to retain its high performance for various flow regimes, from viscous to gravity dominant displacements, without the need for further modifications and computational steps. Two different grids are incorporated for simulation. The grid on fine scale is used to recognize the complicated physics of flow which depends on dominated driving forces and their interaction with heterogeneity. However, to achieve a fast simulation, the global flow calculation is performed on the coarse scale grid using upscaled equivalent properties. The... 

Immiscible gas injection is one of the most common enhanced oil recovery methods used under various reservoir conditions. In this work, the immiscible recycle gas injection, as an EOR scenario for improving recovery efficiency in one of the south-west Iranian oil reservoirs, is simulated by a commercial simulator, ECLIPSE. The reservoir fluid is light oil, with an API of 43. The oil bearing formations are carbonate, and so a dual porosity/dual permeability behavior was chosen for better representation of the fracture system. Different sensitivity analyses with respect to several parameters like the number and location of injection/production wells, production/injection rate, completion... 

Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural... 

In fractured reservoirs, a large variation of permeability due to the presence of fractures leads to changes in the production mechanism compared to conventional reservoirs. Hence, an appropriate model with the ability to describe the reservoir properly can provide a more confident prediction of its future performance. One of the features of a representative model is the number and height of the matrix blocks. The determination of these two parameters is one of the decisive steps in the calculation of an accurate amount of oil production from these reservoirs. In fact, matrix height shows its effect as a gravity force, which is one of the driving mechanisms. If the matrix height is less than... 

Fractures identification is essential during exploration, drilling and well completion of naturally fractured reservoirs since they have a significant impact on flow contribution. There are different methods to characterize these systems based on formation properties and fluid flow behaviour such as logging and testing. Pressure-transient testing has long been recognized as a reservoir characterization tool. Although welltest analysis is a recommended technique for fracture evaluation, but its use is still not well understood. Analysis of pressure transient data provides dynamic reservoir properties such as average permeability, fracture storativity and fracture conductivity.An infusion of... 

Advanced reservoir characterization methods can yield geological models at a very fine resolution, containing 1011–1018 cells, while the common reservoir simulators can only handle much lower numbers of cells due to computer hardware limitations. The process of coarsening a fine-scale model to a simulation model is known as upscaling. Predicting the accuracy of simulation results over an upscaled grid with respect to the fine grid is highly important, as it can yield the optimum upscaling process. In this paper, permeability-based and velocity-based a priori error estimation techniques are proposed by introducing image processing-based comparison methods in the context of upscaling. The... 

During transient tests, both pressure and temperature are changed depending on downhole flow rate. In gas producing wells, Joule-Thomson cooling and frictional heating effects are the main dynamic factors causing flowing bottomhole temperature to differ from the static formation temperature at that depth. When a gas well is shut in, JT cooling effect is vanished and this causes a sharp increase in sandface temperature. As effect of wellbore storage ends, wellbore temperature gradually cools down due to heat conduction with near wellbore region. This paper demonstrates a new technique for using temperature transient data in gas wells in order to determine end of wellbore storage. Also, effect... 

Computer aided history matching techniques are increasingly playing a role in reservoir characterization. This article describes the implementation of a differential evolution optimization algorithm to carry out reservoir characterization by conditioning the reservoir simulation model to production data (history matching). We enhanced the differential evolution algorithm and developed the modified differential evolution optimization method with random localization. The proposed technique is simple-structured, robust, and computationally efficient. We also investigated the convergence characteristics of the algorithm in some synthetic oil reservoirs. In addition, the proposed method is... 

Enhancing oil extraction from oil sands with a hydraulic fracturing technique has been widely used in practice. Due to the complexity of the actual process, modelling of hydraulic fracturing is far behind its application. Reproducing the effects of high pore pressure and high temperature, combined with, complex stress changes in the oil sand reservoir, requires a comprehensive numerical model which is capable of simulating the fracturing phenomenon. To capture all of these aspects in the problem, three partial differential equations, i.e., equilibrium, flow, and heat transfer, should be solved simultaneously in a fully implicit (coupled) manner. A fully coupled thermo-hydro-mechanical...