Seismic inversion as a reliable technique to anticipating of porosity and facies delineation, a case study on Asmari Formation in Hendijan field, southwest part of Iran

Abstract

Porosity and facies are two main properties of rock which control the reservoir quality and have significant role in petroleum exploration and production. Well and seismic data are the most prevalent information for reservoir characterization. Well information such as logs prepare adequate vertical resolution but leave a large distance between the wells. In comparison, three-dimensional seismic data can prepare more detailed reservoir characterization in the inter-well space. Generally, seismic data are an efficient tool for identification of reservoir structure; however, such data usable in reservoir characterization. Therefore, these two types of information were incorporated in order to obtain reservoir properties including porosity and facies in the study area. Using Multimin algorithm, petrophysical analysis was carried out for estimation of reservoir porosity. Then, an accurate post-stack inversion was accomplished to obtain the acoustic impedance volume. The results showed that the Ghar sandstone is characterized by a lower acoustic impedance compared to the high acoustic impedance Asmari Formation. Because of a relationship between acoustic impedance and reservoir properties (i.e., porosity), porosity cube calculation was performed by artificial neural network method which is a popular approach for parameter estimation in petroleum exploration. The consequences showed a good agreement between log based and seismic inversion-derived porosity. The inversion results and well logs cross-plots analyses illustrated that the Ghar member considered as a high quality zone with porosity 22 to 32 percent and the Asmari dolomite shows a low quality interval characters with porosity 1 to 6 percent. The findings of this study can help for better understanding of reservoir quality (especially porous Ghar member delineation) by lithology discrimination in the analysis of identification reservoirs and finding productive well location in Hendijan field.

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

“…Generally, regression analysis is performed using a linear (direct or inverse) correlation to establish a statistical relationship. These relationships are utilized to identify the distribution of the reservoir properties such as porosity (Abdolahi et al, 2022).…”

“…Acoustic impedance (AI) inversion is described as a key tool for the identification of the reservoir's porosity and lithofacies distribution (Avadhani et al, 2006;Mahgoub et al, 2017;Abdolahi et al, 2022). The post-stack seismic inversion is a cheap, rapid, and effective method for investigating exploration wells that are distant from each other (Abdolahi et al, 2022).…”

“…Acoustic impedance (AI) inversion is described as a key tool for the identification of the reservoir's porosity and lithofacies distribution (Avadhani et al, 2006;Mahgoub et al, 2017;Abdolahi et al, 2022). The post-stack seismic inversion is a cheap, rapid, and effective method for investigating exploration wells that are distant from each other (Abdolahi et al, 2022). Seismic inversion aims to turn seismic reflection data into a layer rock characteristic that accurately characterizes reservoir quality with high-resolution , thus enabling the geoscientists to improve reservoir estimates of porosity and facies distinction (Ashraf et al, 2020b).…”

Appraisal of reservoir porosity using a machine learning approach: A study from the Eocene‐Miocene interval of Upper Assam Basin, NE, India

“…It acts as a crucial tool for accurately describing the characteristics of a reservoir (e.g. porosity, saturation and lithofacies) (Abdolahi et al, 2022; Ashraf et al, 2022; Avadhani et al, 2006; Mahgoub et al, 2017; Xu et al, 2009). Hence, usage of this attribute, in particular, becomes more reliable for defining the reservoir property than using a set of multiple attributes such as instantaneous phase, frequency, amplitude, energy, variance and bandwidth.…”

Integrated geological data, 3D post‐stack seismic inversion, depositional modelling and geostatistical modelling towards a better prediction of reservoir property distribution for near‐field exploration: A case study from the eastern Sirt Basin, Libya

“…The geostatistical analysis is often used to establish a statistical relationship between the reservoir petrophysical parameters and the seismic attributes (reflectivity and inversion attributes) (Haas and Dubrule, 1994;Qiang et al, 2020). These relationships are generally used to identify the distribution of reservoir properties such as porosity (Abdolahi et al, 2022).…”