An integrated approach to distribute carbonate reservoir properties, using lithofacies and seismic attributes: a case study from SW of Iran

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

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Fault and fracture study by incorporating borehole image logs and supervised neural network applied to the 3D seismic attributes: a case study of pre-salt carbonate reservoir, Santos Basin, Brazil

Petroleum Science and Technology ◽

10.1080/10916466.2021.2025072 ◽

2022 ◽

pp. 1-20

Author(s):

Amir Abbas Babasafari ◽

Guilherme Furlan Chinelatto ◽

Alexandre Campane Vidal

Keyword(s):

Neural Network ◽

Seismic Attributes ◽

Carbonate Reservoir ◽

3D Seismic ◽

Fracture Study ◽

Borehole Image ◽

Borehole Image Logs

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Effects of structural segmentation and faulting on carbonate reservoir properties: A case study from the Central Uplift of the Tarim Basin, China

Marine and Petroleum Geology ◽

10.1016/j.marpetgeo.2015.12.008 ◽

2016 ◽

Vol 71 ◽

pp. 183-197 ◽

Cited By ~ 21

Author(s):

Guanghui Wu ◽

Haijun Yang ◽

Shu He ◽

Shujuan Cao ◽

Xing Liu ◽

...

Keyword(s):

Tarim Basin ◽

Carbonate Reservoir ◽

Reservoir Properties ◽

Central Uplift

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An integrated approach for carbonate reservoir characterization: a case study from the Linguado Field, Campos Basin

Brazilian Journal of Geology ◽

10.1590/2317-4889202020190103 ◽

2020 ◽

Vol 50 (4) ◽

Cited By ~ 1

Author(s):

Wagner Moreira Lupinacci ◽

Livia de Moura Spagnuolo Gomes ◽

Danilo Jotta Ariza Ferreira ◽

Rodrigo Bijani ◽

Antonio Fernando Menezes Freire

Keyword(s):

Reservoir Characterization ◽

Integrated Approach ◽

Carbonate Reservoir ◽

Campos Basin ◽

Carbonate Reservoir Characterization

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Carbonate reservoir rock typing algorithm based on integrated approach (IRT)

PROneft’ Proffessional’no o nefti ◽

10.51890/2587-7399-2021-6-4-62-70 ◽

2021 ◽

Vol 6 (4) ◽

pp. 62-70

Author(s):

Mariia A. Kuntsevich ◽

Sergey V. Kuznetsov ◽

Igor V. Perevozkin

Keyword(s):

Water Saturation ◽

Integrated Approach ◽

Learning Technologies ◽

Carbonate Reservoir ◽

Reservoir Rock ◽

Static Properties ◽

Reservoir Properties ◽

Optimal Method ◽

Rock Types ◽

Rock Typing

The goal of carbonate rock typing is a realistic distribution of well data in a 3D model and the distribution of the corresponding rock types, on which the volume of hydrocarbon reserves and the dynamic characteristics of the flow will depend. Common rock typing approaches for carbonate rocks are based on texture, pore classification, electrofacies, or flow unit localization (FZI) and are often misleading because they based on sedimentation processes or mathematical justification. As a result, the identified rock types may poorly reflect the real distribution of reservoir rock characteristics. Materials and methods. The approach described in the work allows to eliminate such effects by identifying integrated rock types that control the static properties and dynamic behavior of the reservoir, while optimally linking with geological characteristics (diagenetic transformations, sedimentation features, as well as their union effect) and petrophysical characteristics (reservoir properties, relationship between the porosity and permeability, water saturation, radius of pore channels and others). The integrated algorithm consists of 8 steps, allowing the output to obtain rock-types in the maximum possible way connecting together all the characteristics of the rock, available initial information. The first test in the Middle East field confirmed the applicability of this technique. Results. The result of the work was the creation of a software product (certificate of state registration of the computer program “Lucia”, registration number 2021612075 dated 02/11/2021), which allows automating the process of identifying rock types in order to quickly select the most optimal method, as well as the possibility of their integration. As part of the product, machine learning technologies were introduced to predict rock types based on well logs in intervals not covered by coring studies, as well as in wells in which there is no coring.

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