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Tengiz is an isolated Paleozoic carbonate build-up located in the Pricaspian Basin, Republic of Kazakhstan. The reservoir contains over 26 Billion Barrels OOIP and is one of the world's deepest supergiant fields. The introduction of petrophysical rock types (PRT) and pore type classification has significantly improved oil in place estimation and re...
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The present work describes and evaluates the reservoir quality of the sandstone of the Nubia Formation at the Gebel Abu Hasswa outcrop in southwest Sinai, Egypt. Hydraulic flow unit (HFU) and electrical flow unit (EFU) concepts are implied to achieve this purpose. The Paleozoic section made up of four formations has been studied. The oldest is Arab...
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... 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, 2009; Chehrazi et al., 2011; Xu and Torres-Verdín, 2013; Theologou et al., 2015; Oyewole et al., 2016] and the fractal analysis of MICP data [Davis, 1989; Mandal et al., 2006; Buiting and Clerke, 2013; Wu et al., 2014; Ge et al., 2015b; Lai and Wang, 2015; Liu et al., 2015]. We use the pore radius to segregate pore systems into four sections [Skalinski et al., 2009], as shown in Figure 5. The first section denoted as the nanopores have pore radius smaller than 0.01 μm. ...
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 mono fractal theory. In addition, there is a direct relationship between the pore structure types and the fractal dimension spectrums. What's 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 mono fractal theory, which may be applied in many geological and geophysical fields.
... Actually, classification algorithms have more commonly been used in conjunction with clustering methods to accomplish the task of predicting rock types or lithofacies from core and log data (Ferraretti, Gamberoni, & Lamma, 2012; Nashawi & Malallah, 2009; Skalinski, Kenter, & Jenkins, 2009). Once the rock type / lithofacies is identified, permeability and other petrophysical parameters can be more reliably estimated in a further step, for instance, training a specific regression model for each rock type. ...
... Once the rock type / lithofacies is identified, permeability and other petrophysical parameters can be more reliably estimated in a further step, for instance, training a specific regression model for each rock type. The work of Skalinski et al. (2009) proposes a workflow that encompasses the following steps. First, a non-supervised clustering analysis of T 2 data is performed using the SOM algorithm (Self Organizing Maps). ...
... We also intend to apply the proposed methodology to investigate the potential of performing automatic lithology classification of carbonates through the analysis of the T 2 responses produced by each rock type. In addition, we leave as future work to investigate the effectiveness of combining NMR with other kinds of methods, as done in Skalinski et al. (2009). Finally, although we obtained convincing results, it is necessary to apply our methodology to field data in order to investigate if these classification algorithms will also perform effectively. ...
The accurate permeability mapping, even with the aid of modern borehole geophysics methods, is still a big challenge on the reservoir management framework. One concern within the petrophysics community is that rock permeability value predicted by well logging should not be considered as absolute, mainly for carbonates, but a relative index for identifying more permeable zones. Therefore, in this paper a permeability classification methodology, based exclusively on 1H NMR (Nuclear Magnetic Resonance) relaxation data, was evaluated for the first time as an alternative to the prediction of permeability as a continuous variable. To pursue this, a side-by-side comparison of different data mining techniques for the permeability classification task was performed using a petrophysical dataset with 78 rock samples from six different carbonate reservoirs. The effectiveness of six classification algorithms (k-NN, Naïve Bayes, C4.5, SMO, Random Forest and Multilayer Perceptron) was evaluated to predict the rock permeability class according to the following ranges: low (<1 mD), fair (1–10 mD), good (10–100 mD) and excellent (>100 mD). Discretization and feature selection strategies were also employed as preprocessing steps in order to improve the classification accuracy. For the studied dataset, the results demonstrated that the Random Forest and SMO strategies delivered the best classification performance among the selected classifiers. The computational experiments also evidenced that our approach led to more accurate predictions when compared with two methods widely adopted by the petroleum industry (Kenyon and Timur–Coates models).
... However, the pore radii to define partitioning cutoffs for pore classes are not consistent between authors. For example, microporosity was defined as pore throats below 0.1 (Chekani & Kharrat 2009), 0.2 (Porras & Campos 2001), 0.3 (Marzouk et al. 1998; Skalinski et al. 2009), 0.5 (Arfi et al. 2006 porositons) corresponding to the entry pressure, which were used as rock typing classification thresholds. Lønøy (2006) defined rock types by estimation of conventional pore size and texture from porosity distributions, but a significant mismatch is observed when comparing resulting rock types with porositon-based classification on the same dataset (O. ...
... However, the distribution in the resulting models is generally guided by depositional trends, distributed within a sequence stratigraphic framework (SSF), but without any clear explanation as to what extent diagenetic modification controls the pore throat distributions and rock types. Several studies (Francesconi et al. 2009; Skalinski et al. 2009) reported the lack of a significant relationship between lithofacies and petrophysical properties and defined rock types based on diagenetic features. Rock type population in the latter model was guided by conceptual diagenetic trends and multiple point statistics (MPS) pixel-based modelling. ...
... (c) The PRT4–PRT6 definition was derived from clustering NMR T 2 distributions, and combining them with the effective porosity scale (30 clusters or bins of relaxation times) and cross-plotted against porosity. (d) PRT4– PRT6 correspond to an increasing degree of the corrosion and represent the reservoir quality rock (modified after Skalinski et al. 2009). and conductive fractures from BHI and FMI, were confirmed by well tests and production logs. ...
Carbonate rock typing provides a vehicle to propagate petrophysical properties through association with geological attributes and, therefore, is critical for distributing reservoir properties, such as permeability and water saturation, in the reservoir model. The conventional approaches to rock typing have significant gaps in incorporating diagenetic processes, transferring rock types from core to log domain, accounting for fractures and using appropriate methodology to realistically distribute rock types in the static reservoir model. The workflow proposed in this paper addresses these issues in a comprehensive way by determination of petrophysical rock types (PRTs), which control static properties and dynamic behaviour of the reservoir, while optimally linking to geological attributes (depositional and diagenetic) and their spatial interrelationships and trends. This approach is novel for the fact that it: (1) integrates geological processes, petrophysics and Earth modelling aspects of rock typing; (2) integrates core and log scales; and (3) provides a flexible 'road map' from core to 3D model for variable data scenarios that can be updated with progressive changes in data quality and quantity during the life cycle of an asset. This paper introduces the rationale behind this workflow, and demonstrates its workings and agility through deployment in two large carbonate fields.
Permeability prediction in carbonate rock reservoirs is a very complex task in reservoir characterization due to the heterogeneity of carbonate reservoir rock. Thus, accurate predicting for permeability needs a good understanding of reservoir heterogeneity. The best method to do this is by classifying reservoir rock into groups that is latterly and vertically similar in petrophysical properties to give a unique relationship between porosity and permeability. Cluster Analysis method is used to classify the rock types of tertiary reservoir in Khabaz oil field, to understand the heterogeneity of reservoir by analyses conventional well logs data, four rock types were identified; each group is proportional to a unique hydraulic flow unit which is a reservoir layer that is horzantally and vertically same flow and bedding characteristics. Core data also classified into four groups by Flow Zone Indicator (FZI) method and permeability equations determined for each group with a high (R²) value than making a relationship for all data. The four permeability equations were linked with four rock type to predict permeability in un - cored wells and intervals by using porosity data from logs, either permeability predicted by using the classical method. the comparison between two approaches, the highest correlation coefficient is given by FZI method, so integrating Rock types with FZI method gives the best technique to predict permeability in heterogynous carbonate reservoirs.
