<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The fracture network in hydrocarbon reservoirs plays a major role in reservoir fluid transfer to production wells. Modeling of fracture in fractured reservoir is often done randomly. Modelling is based on image logs and core information. Because the information is available in a small number of wells, the model is not reliable and this problem makes it impossible to predict the correct flow rate and the amount of wells produced. In this study, an algorithm based on primary and secondary data for fracture network modelling in one of the southwest fields of Iran has been presented. The initial data include aperture fracture and fracture density, and secondary data includes petrophysical data, i.e. electrical resistance and resistance logs used to scale-up characteristics of fracture in wells. In this study, we tried to increase the accuracy of modelling by using modelling conditionality on existing and constructed data. Gaussian conditional simulation produces a set of realizations on which non-linear statistics can be readily available. In this way, information was entered into the model in areas where fracture was predicted to exist. Using the turning bands co-simulation method in geostatistic, the fracture characteristics were simulated in wells that were not available. Using the results of the 3D model, the fracture of the reservoir was re-constructed. The results showed that the modelling performed in this study has been able to increase the fracture prediction accuracy and their properties in fracture density by about 9% and in the fracture opening by about 5%.</p>
</abstract>

The fracture network in hydrocarbon reservoirs plays a major role in reservoir fluid transfer to production wells. Modeling of fracture in fractured reservoir is often done randomly. Modelling is based on image logs and core information. Because the information is available in a small number of wells, the model is not reliable and this problem makes it impossible to predict the correct flow rate and the amount of wells produced. In this study, an algorithm based on primary and secondary data for fracture network modelling in one of the southwest fields of Iran has been presented. The initial data include aperture fracture and fracture density, and secondary data includes petrophysical data, i.e. electrical resistance and resistance logs used to scale-up characteristics of fracture in wells. In this study, we tried to increase the accuracy of modelling by using modelling conditionality on existing and constructed data. Gaussian conditional simulation produces a set of realizations on which non-linear statistics can be readily available. In this way, information was entered into the model in areas where fracture was predicted to exist. Using the turning bands co-simulation method in geostatistic, the fracture characteristics were simulated in wells that were not available. Using the results of the 3D model, the fracture of the reservoir was re-constructed. The results showed that the modelling performed in this study has been able to increase the fracture prediction accuracy and their properties in fracture density by about 9% and in the fracture opening by about 5%.

Improvement of Fracture Network Modeling in Fractured Reservoirs Using Conditioning and Geostatistical Method

School of Mining, Petroleum and Geophysic, Engineering

Journal of Applied Engineering Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The fracture network in hydrocarbon reservoirs plays a major role in reservoir fluid transfer to production wells. Modeling of fracture in fractured reservoir...

Improvement of Fracture Network Modeling in Fractured Reservoirs Using Conditioning and Geostatistical Method

Pubblicato online: 11 nov 2020

Pagine: 147 - 154

Ricevuto: 22 ago 2020

Accettato: 01 set 2020

DOI: https://doi.org/10.2478/jaes-2020-0022

Parole chiave
fracture modelling, conditioning, petroleum, geostatistic

© 2020 Seyyedeh Ellieh Moafi Madani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improvement of Fracture Network Modeling in Fractured Reservoirs Using Conditioning and Geostatistical Method

Pubblicato online: 11 nov 2020

Pagine: 147 - 154

Ricevuto: 22 ago 2020

Accettato: 01 set 2020

DOI: https://doi.org/10.2478/jaes-2020-0022

Parole chiavefracture modelling, conditioning, petroleum, geostatistic

© 2020 Seyyedeh Ellieh Moafi Madani et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
fracture modelling, conditioning, petroleum, geostatistic