Reservoir Modelling PDF - 4th Year

Reservoir Modelling 4th Year 3- Facies Model: 3-1 Introducrion: The three-dimensional distribution of bodies of rock and sediments with different sedimentological properties and associated properties is controlled to varying degrees by t...

Reservoir Modelling 4th Year 3- Facies Model: 3-1 Introducrion: The three-dimensional distribution of bodies of rock and sediments with different sedimentological properties and associated properties is controlled to varying degrees by the depositional history of the strata of interest. Primary (depositional) variations in sediment textures and fabrics are modified by diagenetic processes, such as compaction, dissolution, and cement precipitation. The facies: is defined as a body of sedimentary rock with specified characteristics, which may include lithology and rock properties. Reservoirs are commonly be as siliciclastic or carbonate. Siliciclastic reservoirs are dominated by eroded and transported rock detritus, while carbonate reservoirs are dominated by carbonate materials that are grown in place and/or transported to the basin. Facies Model: A facies model captures the reservoir variability based on the sedimentological analysis of the core and wireline data, combined into a conceptual model of the reservoir depositional environment (Fig.40). Facies Modeling: is a means of distributing discrete facies throughout the model grid. 49 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.  Reservoir Modelling 4th Year Fig.40 Facies Modelling The main reason to build a facies model is to condition the subsequent property model; each facies should have a porosity and permeability distribution that is different from the other facies. Electrofacies: the set of log responses which characterizes a bed and permits this to be distinguished from others (Fig. 41) 50 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.  Reservoir Modelling 4th Year Fig.41 : Simplified lithology determination from wireline logs Depending on the distribution of facies, there are different methods are used in facies modelling. These methods fall into two categories: Pixel-Based and Object-Based Modelling Methods. Pixel-based models are built using correlation methods based on the variogram. It is more suitable for carbonate environments as the facies may have a more random pattern. Object-based modelling allows to build realistic 51 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.  Reservoir Modelling 4th Year representations of large-scale geological units such as channels, dunes, sand bars and reefs. 3-2-1 Pixel-Based Methods: Pixel-based models are built using correlation methods based on the variogram: a measure of spatial variation of a property in three orientations, vertical, and maximum and minimum horizontal directions. The variogram captures the relationship between the difference in value between pairs of data points, and the distance separating those two points. Numerically, this is expressed as the averaged squared differences between the pairs of data in the data set, given by the empirical variogram function, which is most simply expressed as: Where Zi and Zj are pairs of points in the dataset For convenience we generally use the semivariogram function: The semivariogram function can be calculated for all pairs of points in a data set, whether or not they are regularly spaced, and can therefore be used to describe the relationship between data points from, for example, irregulary scattered wells. The results of variogram calculations can be represented graphically to establish the relationship between the separation distance (known as the lag) and the average γ value for pairs of points which are that distance apart (Fig.42). 52 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.  Reservoir Modelling 4th Year Figure 42: The raw data for a variogram model Generally, γ increases as a function of separation distance. Finding a trend line through the points on a semivariogram plot yields a semivariogram model (Fig. 43). A semivariogram model has 3 defining features:  The Sill, The value that the semivariogram model attains at the range (the value on the y-axis) is called the sill.  The Range, The distance where the model first flattens out is known as the range.  The Nugget, which is the extrapolated γ value at zero separation 53 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.  Reservoir Modelling 4th Year Fig.43: A semivariogram model fitted to the points in the figure above. 3-2-2 Object-Based Modelling: OBM is a facies modeling technique that accounts for geometry of geological objects. The geometry of facies bodies is analyzed using sedimentary principles and field data, and then it is characterized by probabilistic distributions. Users describe these probabilistic distributions using statistical parameters, such as minimum, mean and maximum values. Depending on the shapes of facies bodies, such as channels and bars, OBM uses some predefined mathematical functions to approximate the facies body shapes. 54 Dr. Maha M. Al-Dabag Petroleum and Mining Engineering/ Mosul Unv.