The accuracy of seismic data is lower than that of well data, but the breadth and extent of this data set is very large-in other words, covering a greater area of the region-which is its key advantage (Russell et al. For example, 3D seismic data reveal the acoustic properties of a reservoir covering a continuous and numerically large part of the field (Ogiesoba 2010 Van Riel 2000). On the other hand, seismic data contain extensive information about the rock and fluid conditions in the ground (Maity and Aminzadeh 2012). Although this type of data has higher resolution than seismic data, it relates to a small part of the reservoir or the well environment, and considering the complexities of the geology, errors will occur in generalizing the data to the whole reservoir (Somasundaram et al. Information such as porosity, p-wave velocity, shale volume, water saturation, permeability, lithology, and production zones can be obtained from the processing and interpretation of well logs (Gholami and Ansari 2017). One of the most important tools for reservoir evaluation and description of reservoir parameters is well log data (Hosseini et al. Well logs and seismic exploration data are commonly used for the evaluation and exploration of hydrocarbon resources (Bahmaei and Hosseini 2019). The results also show that although estimation accuracy is increased significantly with the use of the geostatistical approach, this method requires that a sufficient number of well logs, representing all the fields under investigation, be provided in order to improve the geological model obtained by the multi-attribute and neural network methods. The results clearly show the superiority of neural networks compared with the other methods in estimating the reservoir parameter. In this paper, we apply these methods on the available data for an oil field in southwest Iran to obtain the porosity in a total reservoir cube, and these methods are then compared with one another. Therefore, by determining the reservoir properties and correctly estimating these parameters, optimization can be performed with fewer wells, and the costs of exploration and production are reduced. To do this, there are several methods including multiple linear regression, neural networks, and geostatistical methods. And we are looking for the parameter estimation in the whole reservoir. It is available the desired parameter (such as porosity) of the number of wells in the reservoir and seismic cube. Therefore, we can combine these two types of data to obtain reservoir parameters such as porosity and saturation. Conventionally, seismic data have been used to delineate reservoir structure however, seismic data can be used for reservoir characterization such as porosity. However, the vertical resolution of seismic data is poor compared with that of well data.
Three-dimensional seismic data, on the other hand, can provide more detailed reservoir characterization between wells.
Well data such as logs typically provide sufficient vertical resolution but leave a large space between the wells.
The most commonly used data for reservoir description are well and seismic data.
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