Editor’s comment: This article is part of a series of short “tutorial-like” notes styled to mentor users of digital well logs in becoming confident practitioners of petrophysics.
At the close of Shaly-Sand Tutorial Part 2, I implied, or sort-of promised that this final part would provide some guidance or at least guidelines on the application of my favorite models (equations, transforms, or any of your personal preference names) used to calculate water saturation from a combination of electrical conductivity (i.e., inverse of resistivity), total porosity, formation water salinity, fitting parameters based on rock types (m*, n*), and of course, formation temperature. I need not remind you that all of these properties/parameters listed above can and do vary rapidly with depth (vertically) and transversely (horizontally) across a given reservoir.
I once requested a second core in a large reservoir which was turned down by the Division Engineering Manager using the following reason: “Easy, I am really doing you a favor because if the second core and its analyses are different than the first, you will spend a lot of time understanding the differences, and I know you will be back with a request for a third core to help in this investigation.” This fellow turned a deaf ear to all arguments that I fully expected that each core would be different and only through such sorts of studies could we ever improve our ability to predict recovery efficiency and sweep. “Anyway, that’s not the job assignment of a petrophysical engineer. Leave that job to the experts.” To me it was obvious that this manager did not know that he did not know the influence that geology and petrophysics played in selecting the correct earth model to simulate with mathematical models.
Seleznev, Nikita (Schlumberger-Doll Research Center) | Hou, Chang-Yu (Schlumberger-Doll Research Center) | Freed, Denise (Schlumberger-Doll Research Center) | Habashy, Tarek (Schlumberger-Doll Research Center) | Feng, Ling (Schlumberger-Doll Research Center) | Fellah, Kamilla (Schlumberger-Doll Research Center) | Xu, Guangping (Schlumberger-Doll Research Center and Sandia National Laboratories) | Nadeev, Alexander (Schlumberger Reservoir Laboratories)
Electromagnetic (EM) formation evaluation currently relies on low-frequency resistivity and high-frequency dielectric measurements that are typically not interpreted jointly. In consideration that formation EM responses in different frequency ranges are controlled by different physical phenomena, analysis of a wideband EM response can provide new and complementary sensitivities to formation petrophysical parameters.
We established a wideband rock model to describe the dielectric response of well-sorted clean sandstones in the spectral induced polarization (SIP) frequency range and the dielectric-dispersion frequency range. The model is based on a differential effective-medium approach that accounts for both the Maxwell-Wagner interfacial polarization related to the rock texture and the electric double-layer polarization due to the presence of charged grains. We aim to use a minimal number of parameters in our model to capture the essential dielectric properties in the frequency ranges of interest.
The SIP and dielectric-dispersion spectra were measured on a collection of quarried clean sandstones saturated with brines providing wideband core data. We analyzed these wideband data by applying the rock model simultaneously to the SIP and dielectric spectra. The joint wideband data inversion enabled the estimation of five formation parameters: water-filled porosity, water salinity, cation exchange capacity, dominant grain size, and cementation exponent. The ability to invert for this broad set of formation parameters provides a comprehensive characterization that is unattainable with currently practiced methods. Moreover, when the modeled and measured responses are compatible, the joint wideband inversion of SIP and dielectric-dispersion spectra potentially eliminates interpretation uncertainties if some parameters are independently provided as input.
Application of nanoparticles in the subsurface typically requires the use of surface coatings to maintain stability in dispersion and to provide particular functionality. However, the presence of surface coatings may hinder or mask properties of the bare nanoparticle cores, which may be a concern in nuclear magnetic resonance (NMR) applications. In this study, we used different amounts of 3-aminopropyltriethoxysilane (APTES) coating on Fe3O4 magnetic nanoparticles (A-MNPs). We measured the longitudinal relaxation time (T1) values of those A-MNPs suspensions, and computed and compared the surface relaxivities of A-MNPs with different amounts of APTES coating. Our results showed that when the mass percentage of APTES coating increased from 1.60 to 4.22 wt%, the A-MNPs’ surface relaxivity decreased by 26.1%. To determine the surface relaxation mechanism(s), we also used various volume fractions of D2O to dilute A-MNP dispersions to two concentrations: 0.01 and 0.002 g/L Fe. In the final mixtures, the volume fractions of D2O were fixed as 0-, 30-, 50-, and 70-vol%. The NMR measurements indicated that, at relatively high Fe concentration (0.01 g/L), electron-proton interaction dominates surface relaxation, and the hydrogen atoms in the APTES did not significantly alter the surface relaxation mechanism of the nanoparticles. At a lower Fe concentration (0.002 g/L), proton-proton relaxation, due to the APTES, also played a role in the overall relaxation mechanism on nanoparticle surfaces. A-MNPs with more APTES coating showed lower apparent surface relaxivities with higher D2O volume fractions in the mixture, indicating a greater amount of proton-proton relaxation on the nanoparticle surfaces.
With superior magnetic properties, nanoscale dimensions and nontoxic characteristics, iron oxide nanoparticles are of high interest in nanoscience and nanotechnology. As superparamagnetic nanoparticles (MNPs), Fe3O4 nanoparticles have been widely applied in biomedical areas, such as targeted drug delivery (Chertok et al., 2008), tissue repair (Jordan et al., 2001) and magnetic resonance imaging (MRI) techniques (Babes et al., 1999).
Craddock, Paul R. (Schlumberger-Doll Research Center) | Mossé, Laurent (Schlumberger) | Bernhardt, Carolina (YPF S.A.) | Ortiz, Alberto C. (YPF S.A.) | Tomassini, Federico Gonzalez (YPF S.A.) | Pirie, Iain C. (Schlumberger) | Saldungaray, Pablo (Schlumberger) | Pomerantz, Andrew E. (Schlumberger-Doll Research Center)
The determination of accurate density porosity requires an accurate matrix density. This determination is challenging in organic-rich shale using downhole logs because of the presence of insoluble sedimentary organic matter (“kerogen”) that is part of the solid matrix but has log-response characteristics more similar to those of pore fluids. Methods other than logs used to determine shale matrix density from drill core or cuttings, such as gas pycnometry, rely on remote laboratory services and may not be representative due to microcracks. This study describes a novel approach to quantify porosity in organic-rich shale by the integration of fast wellsite measurements of cuttings and a bulk-density log. The cuttings analysis uses diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), which provides an accurate estimate of matrix density explicitly including kerogen as part of the matrix. Matrix density is then used to estimate porosity per the well-known density-porosity relationship. The method is enabled because the DRIFTS analysis explicitly solves for both minerals and kerogen as components of the shale matrix, separate from that of pore fluids. It is demonstrated that kerogen density is a critical parameter in the determination of matrix density, and its determination is an integral part of the DRIFTS interpretation. Kerogen density is not measurable using traditional logging methods and is otherwise only obtainable using time-consuming laboratory procedures. The DRIFTS technique is advantageous because it requires minimal sample preparation and footprint, can be run at the wellsite, is rapid enough to keep pace with typical drilling rates, and can be performed in oil- and water-based muds. The method can be run in horizontal wells because cuttings are always available, and because the combined depth-resolution of the cuttings and the logs is typically much finer than the lateral interval over which formation properties vary. The integration of cuttings and logs provides a depth-by-depth estimate of shale porosity at the wellsite that is otherwise not obtainable from either basic logs or cuttings individually.
While permeability modeling follows a well-established approach in converting laboratory properties to subsurface conditions, ambiguity remains over the approach to be followed by laboratory-acquired capillary pressures (under ambient conditions, like most mercury injection capillary pressures (MICP) measurements). One approach (developed by the earlier work of Juhasz) recommends that capillary pressures be stress corrected (prior to modeling) according to a correlation. Another approach suggests the saturation-height model (SHM) be built with ambient measurements that when supplied with corrected properties (porosity and permeability) would generate in-situ saturations.
The effect of stress correction applied to porosity and permeability data (as part of routine core analysis (RCA) is not easily compared against the capillary pressure correction, potentially leading to inconsistencies.
The work presented here uses a recent methodology that aims at ensuring consistency between permeability and SHMs to provide guidance on the best approach to be followed in the process of building a SHM. The MICP or SHM carries an intrinsic permeability that can be compared to the permeability model. The results show that significant inconsistency can occur between the porosity-permeability data (a reliable, well-controlled and measurable property under stress) on one hand, and the MICP-/SHM-inferred permeability on the other.
The conclusion is that the most robust dataset for preparing the SHM is under the same conditions under which the MICP and capillary pressure (Pc) data have been acquired. When these data have been acquired under ambient conditions and the resulting model has stressed porosity and permeability as inputs, the SHM will predict the correct stressed entry pressures. The findings are validated against a dataset where the capillary pressures acquired under both ambient and stress conditions.
Saturation-height models (SHM) combined with fundamental rock properties (porosity and permeability) are the basis for a realistic reservoir model. In contrast to porosity and (single-phase) permeability that are rock properties, SHMs are the result of fluid-rock interaction.
In this study, experiments were done on samples from the Marcellus, Woodford, and Eagle Ford shales. The experiments showed that samples from these formations were grossly water-wet, mixed-wet and oil-wet, respectively. The correlation of average wettability index with total organic carbon (TOC) showed that 5 wt% is the critical TOC content required to achieve connectivity and generate oil-wet pathways. Similarly, correlation of average wettability index with clay content showed that <10 wt% clay, samples are oil-wet and >65 wt%, they are predominantly water-wet, and between 10 and 65 wt% clay content, samples exhibited mixed wettability. The threshold values of 5 wt% TOC and 10 wt% clays represent the same volumetric fraction (~10%) of the rock. The figure of 10% can be thought of as percolation threshold for connectivity in shale rocks.
Scanning electron microscope (SEM) imaging done on representative samples (one per formation) was used to quantitatively assess the fraction of different pore types. The fractions of different pore types were in agreement with the observations from the macroscopic imbibition experiments. For instance, oil-wet Eagle Ford samples had a higher fraction of organic pores (22.5%) while water-wet Marcellus samples had a higher fraction of inorganic pores (40%). The samples from all the three shales had a high fraction of mixed-wet pores (Marcellus 57%, Eagle Ford 69%, and Woodford 68%). This knowledge of fractions of different pore types can be instrumental in modeling connectivity pathways.
Garcia, Artur Posenato (The University of Texas at Austin) | Jagadisan, Archana (The University of Texas at Austin) | Rostami, Ameneh (The University of Texas at Austin) | Heidari, Zoya (The University of Texas at Austin)
The importance of clay-network conductivity in resistivity-based saturation assessment has been well recognized over the years. The existing shaly sand models are oversimplified by assuming that the clays are present in the rock predominantly as laminated, dispersed, or structural. This assumption, however, is not reliable in many clay-rich formations because, in nature, clay minerals can have complex spatial distributions. Furthermore, the conventional shaly sand resistivity models, such as Waxman-Smits, dual-water, and Simandoux, do not take into account spatial distribution and connectivity of the clay network. Spatial distribution of the clay network can significantly affect resistivity of clay-rich formations and oversimplifying this distribution can lead to huge uncertainties in estimates of water saturation. In this paper, we introduce a new resistivity-based model that quantitatively takes into account the actual clay-network geometry and distribution and type of clay minerals. Reliable incorporation of spatial distribution of the clay network (i.e., not limited to extreme cases of dispersed, layered, and structural) improves reserves evaluation in clay-rich formations with complex clay network structure.
The new resistivity model incorporates directional pore-network connectivity of each conductive component of the rock that forms a percolating network. The directional connectivity is calculated as a function of the volume fractions and rock-fabric features, such as the directional tortuosity and constriction factor of each rock component. The aforementioned rock-fabric features are quantitatively evaluated from three-dimensional (3D) pore-scale images. We scan core samples from clay-rich formations using a high-resolution microcomputed tomography (CT) scanner. We apply a semianalytical streamline model to estimate the network connectivity and tortuosity of the conductive components from the 3D segmented images, which will be inputs to the introduced model.
We successfully applied the introduced model to several synthetic rock samples as well as to actual clay-rich rock samples, including a shaly sand formation and a mudrock. The electrical conductivity estimates from numerical simulations were in agreement with those estimated from the new model. Comparison of the results against conventional methods showed that saturation estimates were relatively improved by at least 50% in more than 50% of the samples after quantitatively taking into account spatial distribution of the clay network. The outcomes of this paper are promising for successful application of the introduced model for improved in-situ assessment of hydrocarbon saturation through assimilating the impacts of rock fabric and spatial distribution of clay networks on electrical resistivity measurements.
Freed, Denise E. (Schlumberger-Doll Research) | Seleznev, Nikita (Schlumberger-Doll Research) | Hou, Chang-Yu (Schlumberger-Doll Research) | Fellah, Kamilla (Schlumberger-Doll Research) | Little, Jeffrey (Schlumberger Data Services) | Dumy, Gabriel (ESPCI Paris) | Sen, Pabitra
Determining hydrocarbon content from conventional resistivity measurements in freshwater shaly sands formations is challenging because the resistivity depends on the clay formation’s cation exchange capacity (CEC). Because the CEC value depends on clay type and can vary significantly, continuous and direct logging of the formation CEC will benefit resistivity log interpretation in shaly sands. Multifrequency dielectric measurements are sensitive to the CEC of the formation, as well as to the water-filled porosity, water salinity, and texture. We introduce a new physics-based model for the dielectric response of shaly sands in the frequency range of 20 MHz to 1 GHz. To take into account the effect of the CEC, we derive the polarization of the clay particle’s double layer from first principles. The new model uses a minimal number of parameters to describe the essential macroscopic properties of shaly sands and reduces back to a widely accepted dielectric model when the effect of the formation CEC on the dielectric response is negligible.
To validate the model, we present inversion results for dielectric measurements from both core and log data. The new model provides reliable inversion results for the CEC in freshwater formations, water-filled porosity, salinity, and a water-phase tortuosity exponent, which, for fully water-saturated rocks, is analogous to the Archie m parameter. In addition, the low-frequency invaded zone resistivity Rxo can also be predicted based on the inverted parameters.
Dielectric logging tools have been used for over 30 years to distinguish fresh water from oil. The large contrast between the water permittivity and the oil and rock-matrix permittivity makes it possible to determine the water-filled porosity in situations where it would be difficult to do so from resistivity logs. These situations include freshwater formations and formations where the water salinity, the cementation exponent, or the saturation exponent is unknown.
Dernaika, Moustafa (Ingrain, Halliburton) | Al Mansoori, Maisoon (ADNOC Onshore) | Singh, Maniesh (ADNOC Onshore) | Al Dayyani, Taha (ADNOC Onshore) | Kalam, Zubair (ADNOC Onshore) | Bhakta, Ritesh (Formerly with Ingrain, Halliburton) | Koronfol, Safouh (Ingrain, Halliburton) | Uddin, Yasir Naseer (Ingrain, Halliburton)
Most carbonate reservoirs are characterized by multiple-porosity systems that impart petrophysical heterogeneity to the gross reservoir interval. This heterogeneity complicates the task of reservoir description and thus necessitates the establishment of accurate and detailed understanding of the geological heterogeneities and their impact on petrophysics and reservoir engineering.
One of the fundamental input parameters into reservoir models is permeability. The challenge would be to select appropriate samples that represent reservoir heterogeneity for accurate acquisition of vertical to horizontal permeability Kv/Kh data.
In an unpublished work, thousands of plug permeability measurements were performed to obtain Kv/Kh ratios across a large carbonate field in the Middle East. The results were largely influenced by reservoir heterogeneity and yielded large Kv/Kh ratios (greater than unity). Such data would need to be acquired on the same rock volumes for proper Kv/Kh ratios.
In this work, permeability measurements were investigated using digital and conventional techniques to determine the effect of heterogeneity. Detailed thin-section descriptions and mercury injection capillary pressure (MICP) tests were used to understand the different rock types. Advanced three-dimensional (3D) X-ray CT imaging was acquired at multiple scales for detailed digital rock characterization. Permeability was computed directly on the 3D images by the lattice Boltzmann methodology and upscaled to the plug and whole-core levels. Permeability varied largely among different scales/locations and was clearly linked to complex geological features in the rock samples. Integration of the CT images and thin-section photomicrographs provided geological variation in 3D and showed that permeability was influenced by macroscale heterogeneity that may only be examined through multiscale imaging. Larger volume samples were vital in capturing the reservoir heterogeneity, which gave proper Kv/Kh ratios less than unity. Our understanding of the comparisons among different scales will be crucial for upscaling laboratory-measured properties to grid-block scale in reservoir geological models.
The probabilistic neural network (PNN) is functional in recognizing complex patterns without doing any pretraining of source data. However, for some data clusters, independence and colinearity characteristics of the variables in learning samples can seriously distort the window lengths of their probability density distributions, then leading to the incorrect or totally wrong calculated probability values and final recognition results. In view of such drawbacks, an improved PNN that incorporates two techniques of mean impact value (MIV) and correlation analysis is proposed in order to perfect the original PNN’s calculation mechanism by removing those interference and colinear variables from the source data. The data used to validate the method are from two wells in the Iara oilfield. Recognition accuracies of the improved network in four experiments are, 74.05%, 71.7%, 83.02% and 88.24%, respectively, each of which is the highest accuracy. The validation results demonstrate that the new network has the capability of recognizing complex carbonate lithofacies and the results are reliable enough to serve as the reference data for other geological efforts, such as analyzing sedimentary process and building a sequence framework.
In geological resarch, lithofacies identification is generally viewed as an important basic step because the results can provide remarkable revelations to other geological areas of study, such as analyzing sedimentary cycles, establishing sequence frameworks and constructing sedimentation models. For a particular well, in order to continuously obtain its lithofacies information, collecting rich source data in terms of lithology, electrical and petrophysical properties at each depth is essential, thus almost all the relevant identification methods are realized by processing logs (Baldwin et al., 1990; Carrasquilla et al., 2008). Crossplots are a classic tool used to predict lithofacies. The crossplot axes represent two well-log types that have significance in lithofacies classification, such as natural gamma ray (GR) and acoustic log (AC) logs, in the case of distinguishing sand and shale. With the advantage of crossplots, the lithofacies of noncored intervals can be predicted in accordance with the identification principles discovered from the data analysis of cored intervals (Busch et al., 1987; Dubois et al., 2007; Gifford and Agah, 2010; Grana et al., 2012). Nonetheless, crossplots methods require that each lithofacies has distinct characteristics on all the analyzed logs, which could not be recognized when dealing with complex lithofacies identification. As such, two other methods, statistics and neural networks, are rapidly being developed in place of the crossplot method (Tang and White, 2008; Insua et al., 2015).