Rock Typing: An Integrated Reservoir Characterization Tool to Construct a Robust Geological Model in Abu Dhabi Carbonate Oil Field

Salman, Salman Mohamed; Bellah, Samir

doi:10.3997/2214-4609-pdb.170.spe125498

Cited by 8 publications

(4 citation statements)

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“…Reservoir rock typing is a fundamental building block of reservoir characterization and reservoir modeling process. There is a wide approach in defining rock types and it is highly dependent on the type of data available and the data integration [19][20][21][22][23][24][25][26]. Rock layers are generally the same "rock type".…”

Section: Rock Typing Processmentioning

confidence: 99%

“…To integrate the carbonate reservoir characterization and the link between geological and reservoir engineering, reservoir rock-typing methods have been developed and been used in many oil and gas fields, especially the carbonate reservoirs in Middle East [13][14][15][16][17][18][19][20][21][22][23]. Rock typing absorbs the geological and petrophysical connotation which is a process of classifying reservoir into distinct units and each rock type is characterized by same depositional and diagenetic processes and unique physical properties, capillary pressure curves (or J-function) and relative permeability curves [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. As a result, given the rock type sets, the empirical formula for predicting reservoir parameters, such as water saturation, could have been obtained from cored wells and could have been used to calculate the parameters of uncored wells and interwells to lead to an improved reservoir property model.…”

Section: Introductionmentioning

confidence: 99%

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Water saturation modeling using modified J-function constrained by rock typing method in bioclastic limestone

Deng

Guo

Tian

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Combining both geological and petrophysical properties, a reliable rock typing scheme can be achieved. Two steps are included in rock typing. Step 1: rocks are classified into lithofacies based on core observations and thin sections; Step 2: lithofacies are further subdivided into rock types according to petrophysical properties such as MICP (Mercury Injection Capillary Pressure) and K-Phi relationships. By correlating rock types to electrofacies (clusters of log data), we can group the target formation into 12 rock types. Then it is possible to predict the distributions of rock types laterally and vertically using wireline logs. To avoid the defect of the classical J-function saturation model that includes permeability which is quite uncertain especially in carbonate rocks, a modified J-function was created and used in the paper. In this function, water saturation is simply expressed as a function of height above free water level for a specific rock type. Different water saturation models are established for different rock types. Finally, the water saturation model has been successfully constructed and verified to be appropriate.

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Section: Rock Typing Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water saturation modeling using modified J-function constrained by rock typing method in bioclastic limestone

Deng

Guo

Tian

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…MICP is considered as an acceptable approach to determining pore throat in porous media. Previous studies provide various kinds of methods to characterize the pore structure and classify the pore types based on the statistical analysis [ Salman and Bellah , ; Chehrazi et al ., ; Xu and Torres‐Verdín , ; Theologou et al ., ; Oyewole et al ., ] and the fractal analysis of MICP data [ Davis , ; Mandal et al ., ; Buiting and Clerke , ; Wu et al ., ; Ge et al ., ; Lai and Wang , ; Liu et al ., ]. We use the pore radius to segregate pore systems into four sections [ Skalinski et al ., ], as shown in Figure .…”

Section: Algorithm and Applicationmentioning

confidence: 99%

An improvement of the fractal theory and its application in pore structure evaluation and permeability estimation

Fan

Deng

et al. 2016

JGR Solid Earth

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We present an improved fractal model for pore structure evaluation and permeability estimation based on the high pressure mercury porosimetry data. An accumulative fractal equation is introduced to characterize the piecewise nature of the capillary pressure and the mercury saturation. The iterative truncated singular value decomposition algorithm is developed to solve the accumulative fractal equation and obtain the fractal dimension distributions. Furthermore, the fractal dimension distributions and relevant parameters are used to characterize the pore structure and permeability. The results demonstrate that the proposed model provides better characterization of the mercury injection capillary pressure than conventional monofractal theory. In addition, there is a direct relationship between the pore structure types and the fractal dimension spectrums. What is more, the permeability is strongly correlated with the geometric and the arithmetic mean values of fractal dimensions, and the permeability estimated using these new fractal dimension parameters achieve excellent result. The improved model and solution give a fresh perspective of the conventional monofractal theory, which may be applied in many geological and geophysical fields.

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“…Rock type identification is a critical first step in resource exploration and development [ 1 , 2 , 3 ]. This involves a visual examination of the specimens for specific properties that are typically based on color, composition, sedimentary structures, and granularity [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques

Malik

Puasa²,

Lai

2022

Sensors

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The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When such activity is conducted in the field as part of an outcrop study, the sedimentologist is likely to be exposed to challenging conditions such as the weather and their accessibility to the outcrops. This study uses high-resolution photographs which are acquired from a sedimentological study to test an alternative basic multi-rock identification through machine learning. While existing studies have effectively applied deep learning techniques to classify the rock types in field rock images, their approaches only handle a single rock-type classification per image. One study applied deep learning techniques to classify multi-rock types in each image; however, the test was performed on artificially overlaid images of different rock types in a test sample and not of naturally occurring rock surfaces of multiple rock types. To the best of our knowledge, no study has applied semantic segmentation to solve the multi-rock classification problem using digital photographs of multiple rock types. This paper presents the application of two state-of-the-art segmentation models, namely U-Net and LinkNet, to identify multiple rock types in digital photographs by segmenting the sandstone, mudstone, and background classes in a self-collected dataset of 102 images from a field in Brunei Darussalam. Four pre-trained networks, including Resnet34, Inceptionv3, VGG16, and Efficientnetb7 were used as a backbone for both models, and the performances of the individual models and their ensembles were compared. We also investigated the impact of image enhancement and different color representations on the performances of these segmentation models. The experiment results of this study show that among the individual models, LinkNet with Efficientnetb7 as a backbone had the best performance with a mean over intersection (MIoU) value of 0.8135 for all of the classes. While the ensemble of U-Net models (with all four backbones) performed slightly better than the LinkNet with Efficientnetb7 did with an MIoU of 0.8201. When different color representations and image enhancements were explored, the best performance (MIoU = 0.8178) was noticed for the L*a*b* color representation with Efficientnetb7 using U-Net segmentation. For the individual classes of interest (sandstone and mudstone), U-Net with Efficientnetb7 was found to be the best model for the segmentation. Thus, this study presents the potential of semantic segmentation in automating the reservoir characterization process whereby we can extract the patches of interest from the rocks for much deeper study and modeling to be conducted.

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Rock Typing: An Integrated Reservoir Characterization Tool to Construct a Robust Geological Model in Abu Dhabi Carbonate Oil Field

Cited by 8 publications

References 0 publications

Water saturation modeling using modified J-function constrained by rock typing method in bioclastic limestone

Water saturation modeling using modified J-function constrained by rock typing method in bioclastic limestone

An improvement of the fractal theory and its application in pore structure evaluation and permeability estimation

Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques

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