\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Manning is a Principal Scientist (Rank Grade 9) in the Coasts & Oceans Group at HR Wallingford (UK) and has over 23 years of scientific research experience (in both industry and academia) examining natural turbulent flow dynamics, fine-grained sediment transport processes, and assessing how these interact, (including both field studies and controlled laboratory flume simulations). Andrew also lectures in Coastal & Shelf Physical Oceanography at the University of Plymouth (UK). Internationally, Andrew has been appointed Visiting / Guest / Adjunct Professor at five Universities (Hull, UK; Delaware, USA; Florida, USA; Stanford, USA; TU Delft, Netherlands), and is a highly published and world-renowned scientist in the field of depositional sedimentary flocculation processes. Andrew has contributed to more than 100 peer-reviewed publications in marine science, of which more than 60 have been published in international scientific journals, plus over 180 articles in refereed international conference proceedings, and currently has an H-index of 24. He supervises graduates, postgraduates and doctoral students focusing on a range of research topics in marine science. 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These numerical simulations require input parameters such as relative permeabilities, capillary pressures, and other rock and fluid porosity versus permeability trends. These parameters are typically derived from Special Core Analysis (SCAL) tests. Core analysis laboratories have traditionally provided SCAL through experiments conducted on core plugs. Depending on a number of variables, SCAL experiments can take a year or longer to complete and often are not carried out at reservoir conditions with live reservoir fluids. Digital Rock Physics (DRP) investigates and calculates the physical and fluid flow properties of porous rocks. In this approach, high-resolution images of the rock’s pores and mineral grains are obtained and processed, and the rock properties are evaluated by numerical simulation at the pore scale.
Comparisons between the rock properties obtained by DRP studies and those obtained by other means (laboratory SCAL tests, wireline logs, well tests, etc.) are important to validate this new technology and use the results it provides with confidence. This article shares a comparative study of DRP and laboratory SCAL evaluations of carbonate reservoir cores.
This technology is a breakthrough for oil and gas companies that need large volumes of accurate results faster than the current SCAL labs can normally deliver. The oil and gas companies can use this information as input to numerical reservoir simulators, fracture design programs, analytic analysis of PTA, etc. which will improve reserve forecasts, rate forecasts, well placement and completion designs. It can also help with evaluating option for improved oil recovery with sensitivity analysis of various options considering the actual pore scale rock fabric of each reservoir zone. Significant investment savings can also be realized using good DRP derived data compared with the conventional laboratory SCAL tests.
The main objective is to provide petrophysical and multiphase flow properties, calculated from 3D digital X-ray micro-tomographic images of the selected reservoir core samples. The simulations have been conducted on sub-samples (micro plugs) and then upscaled to cores plug scale for direct comparison with experimental data. Typically the whole core are imaged on dimensions of 11 – 16.5 cm with a resolution of 500 microns, while the core plugs are imaged from to 2 – 4 cm with resolutions of 12 – 19 microns. The micro plugs have dimensions from 1 – 5 mm with resolutions of 0.3 – 5 microns and with Nano-CT one can look at rocks of 50 – 300 microns with resolutions from 50 – 300 nm. In DRP process, the results of these increasingly smaller and smaller investigations are then integrated by an upscaling, either by steady state or geometric methods. Hence, rock properties are computed from Nano and micro scale to plug scale to a whole core scale.
Absolute permeability can be computed using Lattice-Boltzmann simulations, calculation of Formation Resistivity Factor is based on a solution of the Laplace equation with charge conservation (the equations were solved using a random walk algorithm) and elastic properties were calculated by the finite element method. Primary drainage and waterflood capillary pressure and relative permeabilities are determined from multi-phase flow simulations on the pore network representation of the 3D rock model. Flow simulation input parameters were set according to expected wettability conditions.
The first section outlines the basic DRP based results on reservoir properties determined on complex carbonates from giant Middle East reservoirs and compared with similar measurements performed in SCAL laboratories. Section 3 outlines possible details in the calculation process for each of the many reservoir parameters that can be calculated using DRP, while section 4 illustrates snapshots of multi-phase flow results on complex carbonates from the same giant Middle East reservoirs. Capillary pressure, cementation exponent (m), saturation exponent (n) for both primary drainage and imbibition, water-oil and gas-oil relative permeability and elastic properties of carbonates were calculated from DRP. Very good agreement is obtained between DRP derived properties and available experimental data for the studied data set. The results obtained for porosity, absolute permeability, formation resistivity factor, cementation and saturation exponent are shown in Figure 1. through 5. Calculations of elastic properties have been performed on all reconstructed samples. The elastic parameters Vs and Vp are reported in figure 6 and compared to available literature data.
In order to meet project objectives the workflow illustrated in Figure 7. was implemented. High resolution (19 μm/voxel) micro-CT images of core plugs were first recorded in order to identify rock types and their distribution within the core plugs, and to select locations for thin sections and micro-plugs.
Simulated vs. experimental permeability
Simulated vs. experimental porosity
Porosity-FRF correlation
Porosity-Cementation exponent correlation
Porosity-Saturation exponent correlation
Porosity-Vs and Vp correlation
Digital rock analyses on carbonate samples
Thin sections were then prepared and analysed for each core plug. Micro-plugs were drilled and high resolution (8.0 – 0.3 μm/voxel) micro-CT images were recorded, processed and analysed for each micro-plug, generating 3D digital rock models.
Micro-plug properties have been calculated directly on the digital rock (grid calculations) or on network representations of the pore space (network based calculations):
Grid based reservoir core properties
Total porosity
Absolute permeability kabs
Formation Resistivity Factor FRF and corresponding cementation exponent m
Elastic moduli assuming isotropy (bulk modulus K, shear modulus μ, Young’s modulus E, Poisson’s ratio υ and Lamé’s parameter λ) and corresponding acoustic velocities (P-wave velocity Vp and shear-wave velocity Vs)
Network based flow relations
Capillary pressure Pc as a function of water saturation Sw
Relative permeability kr as a function of water saturation Sw
Resistivity Index RI as a function of water saturation Sw and corresponding saturation exponent n for both primary drainage and imbibition cycle
Capillary pressure and relative permeability curves were established for the following flow processes:
2-phase flow
Oil/water primary drainage to initial water saturation Swi
Water/oil imbibition to residual oil saturation Sorw
3-phase flow
Gas/oil drainage at initial water saturation Swi
Resistivity index curves and corresponding saturation exponents were established for water/oil primary drainage and imbibition.
The last step of the workflow is to upscale micro-plug properties (volumes in the mm3 range) to core plug properties (volumes ranging from ~ 40 to100 cm3). Rock types were identified and micro-plugs representing these rock types were selected. Each core plug voxel was then assigned to a given rock type or pore space. Corresponding micro-plug and pore fluid properties were used as input to the calculation of upscaled properties.
The following properties were upscaled:
Petrophysical properties
Total porosity
Absolute permeability kabs
Formation Resistivity Factor FRF
Cementation Exponent m
Flow properties
Capillary pressure Pc as a function of water saturation Sw
Relative permeability kr as a function of water saturation Sw
Resistivity Index RI as a function of water saturation Sw
Capillary pressure, relative permeability and resistivity index curves were generated for the micro-plug flow processes listed on previous page.
The principle of microfocus X-ray Computed Tomography (mCT) is based on Beer’s law, i.e. the intensity of X-rays is attenuated when passing through physical objects. The attenuated X-rays are captured by a detector to compose a projection image. A series of projection images from different angles (0 to 360°) are collected by rotating the object around its axis. These projection images are processed to generate 2D mCT slices, which subsequently are input date to construct 3D images of the objects. Each volume-element in these 3D images corresponds to one voxel. The voxel value of the recorded image is proportional to the attenuation coefficient, which is mainly a function of the density and the effective atomic number Z of the constituents of the object (Alvarez and Macovski, 1976; Pullan et al., 1981).
The petrographic study of thin sections cut from the core plugs is an important part of the workflow. It allows describing and examining the heterogeneity of the core plugs on the micro- to macro-scale. Important features are the identification of micro-facies, the microscopic fabric in each facies type, and the mineralogy of the rock - including diagenetic features (cementation and secondary porosity). The first step in studying the facies associations is the analysis of the initial core plug mCT images (at 19 micron/voxel resolution) to identify the spatial distribution of the various facies, which is the basis for selecting where to cut the slab for thin section preparation, and also to select sites for drilling sub-plugs.
All samples are classified according to Dunham (1962) and Embry & Klovan (1971) limestone Classification. Several rock types are described with special attention to the grain types in terms of size, shape and sorting, because these parameters are influencing the flow properties. One of the main purposes of the microscopic analysis is to describe the porosity of the sample. The different pore types are identified using the Choquette and Pray (1970) porosity classification scheme. Furthermore, the high resolution images of the thin sections are used to distinguish between carbonate cements and micrite, in addition to identifying the main minerals (calcite or dolomite). Drilling sites for micro-plugs are also decided based on the characteristic microfacies types of the sample. The mCT scanning resolution of the micro plugs is chosen in accordance with the microscopic analysis, with particular emphasis on micrite, pore sizes and cementation features.
Scanned mCT images of plugs are cropped into the largest possible undisturbed rectangular volume avoiding cracks, uneven edges and gradients towards the outer surface of the sample. Cropped images are scaled to a voxel size representing approximately the size of the corresponding micro-plug images. A noise reduction filter is applied.
Scanned images of micro-plugs are cropped to a volume of 12003 grid cells to avoid gradients at the side of the image and to allow further processing (size limitations of applied software).
Each image element carries an 8-bit signal (256 grey values) corresponding to the X-ray attenuation experienced within its volume. A voxel completely filled by empty pore space (air) data is approaching black (0,
An example of a grey value histogram is shown in Figure 8. The histogram itself is shown as blue diamonds and is displayed on logarithmic scale (to the left). The other two data sets (pink and yellow) show the first and second order differentials of the grey value counts.
Example grey value histogram.
The following porosities can then be calculated:
where
This analysis underestimates porosity and permeability. Therefore, a practical threshold needs to be found to decide up until which micro-porosity value voxels are considered pore. For this purpose, the grey value (GVp) is found where:
This is illustrated in Figure 9. Thus, the total porosity of the image is calculated according to:
where the second term gives the amount of micro-porosity extracted from the image. Note that the micro-porosity values for individual grey values are the same in eq. 5 as in eq. 2.
Practical pore threshold.
The grey values GVp and GV2 give the segmentation thresholds for the micro-plug images. Three images are prepared according to this segmentation method:
representing
representing
representing a coarser image where
Image 1 is segmented into a pore network representation (see Bakke and Øren, 1997, Øren and Bakke, 2002, 2003) which is used to calculate capillary pressure, relative permeability, resistivity index, and saturation exponent. Image 2 is used to calculate porosity, permeability, formation resistivity factor, and cementation exponent. Image 3 is used in a steady-state upscaling routine to include properties calculated for generic models for micritic material representing the micro-porosity.
The scaled image of the whole core plug is segmented in the same way to identify фres as vugs. However, the remaining matrix of the core plug is segmented into pure solid and 1-3 porosity classes according to the segmentation of the respective micro-plugs extracted from the core plug.
3.5.1.1. Porosity
Three different porosities are reported: total porosity (
where
3.5.1.2. Absolute permeability
A Lattice-Boltzmann method is applied to solve Stokes’ equation in the uniform grid model. Flow is driven either by a constant body force or a constant pressure gradient through the model. Permeability is calculated in three orthogonal directions separately. Sides perpendicular to flow directions are closed during each directional calculation (no-flow boundary conditions). In this study, absolute permeability is calculated using a constant body force because this setting delivers more accurate results when model resolution is sufficient and porosity is relatively high. Averages of three directional calculations are reported for each model realization. For further details see Øren and Bakke (2002).
3.5.1.3. Formation resistivity factor
The steady state electrical conductivity, or formation resistivity factor (
subject to the boundary condition
The effective directional conductivities σ
Formation factor and cementation exponent were approximately 10-15% greater than experimental data. The
3.5.1.4. Elastic moduli
The finite element method described by Garboczi and Day (1995) has been implemented for calculation of elastic properties. The method uses a variational formulation of the linear elastic equations and finds the solution by minimizing the elastic energy using a fast conjugate-gradient method. The results are valid for quasi-static conditions or at frequencies which are sufficiently low such that the included pore pressures are in equilibrium throughout the pore space (Arns
The effective bulk and shear moduli are computed assuming isotropic linear elastic behavior. The Vp and Vs are subsequently calculated using the simulated effective elastic moduli and the effective density according to:
Inputs to the calculations are:
A three-dimensional representation of the rock microstructure (a digital rock sample)
Density
Density
Acoustic measurements are generally performed on samples with volumes ranging from 40 to 100 cm3. Digital rock samples are significantly smaller, generally in the mm3 range. Care must therefore be taken when laboratory measurements and pore-scale derived properties are compared, due to the scale difference. Laboratory samples and plugs for μCT imaging are not only of different volumes, they are also generally sampled at different locations. Due to the spatial variability, it is recommended to compare trends when laboratory measurements are compared with pore-scale derived properties. This is illustrated in Figure 10 for the P-wave velocity. A digital sample with 12.9% porosity has been made. However, samples tested in the laboratory do not have porosities close to this value. By sub-sampling the original digital sample, a relative broad porosity range is obtained (from 9.9 to 16.3%). The pore-scale derived velocity – porosity trend is now overlapping with the measurements performed in the laboratory, and a comparison can be made.
Fontainebleau sandstone.
Cracks may reduce the acoustic velocities significantly. Sub-resolution cracks are not incorporated in the processed mCT images and the corresponding pore-scale derived velocities are therefore overestimated for materials containing such cracks.
Cracks are closed during loading. Acoustic measurements performed at elevated stress levels are consequently expected to approach pore-scale derived velocities. Derzhi and Kalam (2011) compared acoustic measurements at different stress levels with pore-scale derived velocities. Their results are shown in Figure 11. Note that this assumes that the pore space and rock framework deforms without large micro-structural changes such as pore collapse, grain rotation and grain crushing.
P-wave velocity and stress for carbonate samples.
Frequency
Pore-scale derived elastic properties represent properties in the low frequency limit (f → 0). Measurements in the laboratory are generally performed in the ultrasonic range (1 Hz to 4 kHz). Acoustic velocities are independent of frequency for dry materials, while an increase with increasing frequency has been observed for fluid saturated rocks. Pore-scale derived properties are therefore expected to be comparable to laboratory measurements for dry rocks – and lower for fluid saturated rocks.
3.5.1.5. NMR
NMR is simulated as a diffusion process using a random walk algorithm to solve the diffusion equations (Øren, Antonsen, Rueslåtten and Bakke, 2002). The T2 responses at Sw = 1.0 were simulated on one sample from the field A. The results are shown as T2 decay and T2 distribution curves in figures 14 and 15. The surface relaxation strength was kept at 1.65x10-5 m/sec for all minerals. The inter echo time was 200µsec at a background magnetic field gradient of 0.2 G/m, the bulk water T2 was 0.3 sec, and the diffusion constant was 2x10-9 m2/sec. The decay curves are transformed into T2 distributions using 50 exponential functions (assuming the same has been done for the lab data).
The reconstructed rock models were simplified into pore network models. Crucial geometrical and topological properties were retained, while the data volume was reduced to allow timely computation (Øren and Bakke, 2003). In pore network modelling, local capillary equilibrium and the Young–Laplace equation are used to determine multiphase fluid configurations for any pressure difference between phases for pores of different shape and with different fluid/solid contact angles. The pressure in one of the phases is allowed to increase and a succession of equilibrium fluid configurations are computed in the network. Then, empirical expressions for the hydraulic conductance of each phase in each pore and throat are used to define the flow of each phase in terms of pressure differences between pores. Conservation of mass is invoked to find the pressure throughout the network, assuming that all the fluid interfaces are fixed in place. From this the relationship between flow rate and pressure gradient can be found and hence macroscopic properties, such as absolute and relative permeabilities, can be determined (Øren and Bakke, 2002).
The following oil-water displacements were simulated:
Primary drainage
Imbibition at a given wettability preference
For each displacement process, capillary pressure and relative permeability curves were calculated. Resistivity index with the corresponding n-exponent was calculated after primary drainage.
Each saturation and relative permeability value corresponds to a capillary equilibrium state. In all the simulations, it is assumed that capillary forces dominate. This is a good approximation for capillary numbers Nca < 1e-6. This, however, does not necessarily mean that it is the most efficient displacement possible. In certain cases, viscous and/or gravity forces can dominate and result in higher (or lower) displacement or sweep efficiency.
3.5.2.1. Relative permeability
Simulated relative permeability data are fitted to the empirical LET expression (Lomeland, Ebeltoft and Thomas, 2005). For water/oil displacements the LET equations become:
where
3.5.2.2. Capillary pressure
A detailed account of the methods used to calculate capillary pressure as a function of
The calculated capillary pressures can be expressed in terms of the dimensionless Leverett
where
where
3.5.2.3. Water saturation
All reported
where
3.5.2.4. Resistivity Index and saturation exponent, n
The resistivity index is calculated from capillary dominated two-phase flow simulations on the pore network representation of the 3D rock image. The basis for simulating capillary dominated displacements is the correct distribution of the fluids in the pore space. For two-phase flow, the equilibrium fluid distribution is governed by wettability and capillary pressure and can be found by applying the Young-Laplace equation for any imposed pressure difference between the phases. A clear and comprehensive discussion of all the mathematical details, including the effects of wettability, involved in the simulations can be found in Øren et al., 1998, and Øren and Bakke, 2003).
The current
where
where the subscript
The conductance of a pore element
where
The resistivity index is computed similarly. At various stages of the displacement (i.e. different
The
Effective properties of the core samples are determined using steady state scale up methods. The CT scan of the core plug is gridded according to the observed geometrical distribution of the different rock types or porosity contributors. Each grid cell is then populated with properties calculated on the pore scale images of the individual rock types. The following properties are assigned to each grid cell; porosity, absolute permeability tensor (
Single phase up-scaling is done by assuming steady state linear flow across the model. The single phase pressure equations are set up assuming material balance and Darcy’s law
The pressure equation is solved using a finite difference formulation. From the solution one can calculate the average velocity and the effective permeability using Darcy’s law. By performing the calculations in the three orthogonal directions, we can compute the effective or up-scaled permeability tensor for the core sample. The effective formation resistivity factor is computed in a similar manner by replacing pressure with voltage, flow with current, and permeability with electrical conductivity. The up-scaled
Effective two-phase properties (i.e. capillary pressure, relative permeability, and
Select a capillary pressure (
Using the
Calculate the average water saturation using pore volume weighting
From the
Perform two separate single phase steady state simulations, one for the oil and one for the water, to calculate the effective phase permeability
Divide the phase permeability with the effective absolute permeability to obtain the effective relative permeability
Repeat these steps with different
The resistivity index curve is generated in a similar manner by replacing phase permeability in the water phase calculations with electrical conductivity. The effective
The overall uncertainty in up-scaled properties varies from sample to sample. Uncertainties may be introduced in the following steps:
How representative are identified rock types and their corresponding distribution? In other words; how representative are the digitized model of the core samples?
How representative are μCT models of the rock micro-structure? The main uncertainty is related to size (REV) and image segmentation where the spatial distribution of pore-space and rock minerals is set.
An uncertainty of ± 2% related to the accuracy of pressure solvers.
Uncertainty related to whether all relevant physics are included in the calculations or not.
Wettability is an input parameter to the simulations. The uncertainty in wettability is the main source of uncertainty for both two- and three-phase flow simulations.
In this section, we present some novel results of validation of multi-phase flow SCAL results using Digital Rock Physics. The reservoir cores comprise complex carbonates from giant producing reservoirs in Middle East. Figure 12 show the comparisons of water-oil capillary pressure (Pc) measured in a SCAL laboratory at reservoir temperature and net over burden pressure using a Porous Plate and MICP trims from the same cores corrected to the reservoir conditions. The DRP data were acquired from the cores after the tests were completed on the Porous Plate and core thoroughly cleaned for final SCAL reference measurements. Both limestone and dolomite samples show excellent similarity of DRP derived data with the laboratory evaluations. Figure 13 confirms the validity of such measurements on different sets of core samples comprising the same reservoir rock type (RT), provided the rock typing is valid and captures the key formation properties of rock and fluids.
Water-oil Pc (Porous Plate): DRP vs lab on same core sample
Figures 14 and 15 show for the first time in industry that laboratory NMR T2 and MICP measurements done on carbonate rock types can also be captured using DRP based simulations on the same cores with distinctly different pore geometries. The robustness of DRP in capturing NMR T2 based pore bodies and MICP based pore throat distributions have far reaching consequences. This shows that DRP in essence can be used confidently to quantify pore body and pore throat distributions, and therefore the 3D pore geometry is representative of the specific core sample and pore network topology. In using DRP effectively, it is recommended that one compares and validates measured NMR T2 and MICP prior to detailed simulations to quantify various two-phase and three-phase flow properties through such reservoir rocks.
Figures 16 and 17 demonstrate example DRP based validations with respect to water-oil relative permeabilities conducted at full reservoir conditions (reservoir temperature, reservoir pressure and live fluids) on other complex carbonates, including highly permeable vuggy samples. The imbibition displacements were conducted under steady state conditions at SCAL laboratories and QC’ed thoroughly with respect to production, pressure profiles and insitu saturation data, and the corresponding numerically simulated measrements. The DRP data were acquired on cores comprising each of the composites tested. It is interesting to note that when plug DRP data are compared with composite laboratory measurements there is some scatter and divergence for each reservoir rock type. However, the divergences are significantly minimized when the DRP plugs used are digitally butted to represent the composite used in the laboratory tests. DRP captures the full reservoir condition multi-phase flow data very well, and in some cases even show the experimental artefacts of the SCAL measurements. The validity of tehse tests were confirmed on 14 different reservoir rock types comprising different formations.
Water-oil Pc (Porous Plate): DRP vs lab in different core samples, but same RRT
NMR T2 distribution and MICP pore throat distribution, DRP vs Lab – vuggy core
NMR T2 distribution and MICP pore throat distribution, DRP vs Lab – tight core
Validating water-oil kr of low permeability composite samples: RRT 6 (10-25 mD)
Validating water-oil kr of high permeability composite samples: RRT 8 (350-560 mD)
ADCO and ADNOC Management are acknowledged for their permission to publish these novel Digital Rock Physics based SCAL results.
Numerical Rocks (Norway) is acknowledged for providing the detailed drafts relating to the procedures adopted in example DRP computations and the robust measurements presented in this chapter.
Ingrain Inc (Houston and Abu Dhabi) are acknowledged for introducing the author to the various challenges ahead, and the uncertainties in current DRP based dvelopments.
Digital Core (Australia) is gratefully remembered in first introducing the concept of DRP to Middle East, and involving ADCO in one of the first Joint Industy Projects offered to industry.
iRocks (Beijing and London) are acknowledged for many stimulating discussions relating to the state-of-the-art.
Finally, one must remember the ADCO DRP team for the interest generated and the numerous insights to imaging, segmentation, data evolution and their impact on different Petrophysical, SCAL and elastic properties. I thank my son, AbdAllah, for helping me in getting this draft ready despite the very busy schedules of August 2012.
The external examination of the corpse is a procedure that can provide information on the examination of the body when the identity is unknown, provides guidance on cause of death, unnatural, or unexplained manner of death, and determines conditions, for example, time of death [1].
The external examination of the body must be accurate and must be performed by trained people with many years of experience in the field, as sometimes medical work is combined with the forensic work.
During the external examination, definitive signs of death (temperature, lividity, rigor, or advanced postmortem changes) should be considered. In the procedure, all areas of the naked body should be analyzed and photographed, and all visual evidence and findings, such as scars, traumatic changes, tattoos, deformities, syringe marks, should be reported [2].
Unnatural deaths are those with external influence, due to physical aggression, accident, homicide, poisoning, suicide, and in those death, the external examination is very important, because it can provide information about the cause, with indicators on the body such as conjunctival hemorrhages, livor mortis color, signs of injury, among others [1].
For these reasons, the external examination of the corpse is of great importance, as it allows to:
To provide the elements of identification.
Plan the steps to be followed in an autopsy, for which it is necessary to determine the autopsy technique to obtain results according to the needs of the case.
Document any pathological findings from the outset.
Support a medico-legal case if a full autopsy is not possible (which has happened due to health regulations in the context of the COVID-19 pandemic).
Provide evidence in cases of allegations of lack of timely and adequate obstetric care.
Document cases of neglected of elderly, disabled adults, and young children in the care of third parties.
In addition to fingerprints, there are other elements accessible for external examination that can be valuable in determining or also confirming the identification of the deceased, such as dental features and tattoos [3]. In the case of dental features, age, sex, habits, cultural characteristics can be determined (Figure 1) in addition to the identification of the individual by comparison with dental records [4]. In the case of tattoos, it is important as a complement for identification (Figure 2), as it can provide information for relatives or a tour of tattoo shops which can narrow down the search field [5].
Denture with missing pieces and poor care conditions.
Oversized and eye-catching tattoo.
Before performing an autopsy, it is ideal is to obtain as much information as possible, such as the place where the body or remains were discovered, the circumstances of death, the postmortem interval, the history of previous illnesses, and whether it was a witnessed death.
But if this is not possible, elements of external examination may be useful to:
Take extreme precautions in the use of personal protective equipment if infection is suspected.
Establish where the incisions will be placed, so as not to compromise internal structures (Figure 3).
Establish before starting the autopsy procedure, which complementary tests may be necessary, to have the appropriate containers (e.g., Petri dishes with culture media, fixatives for electron microscopy studies) or to establish coordination with other laboratories, in cases that require quickly processing.
The existence of entry points for skin infection, such as pressure ulcers (bed sores), suggests that sepsis may be present (Figure 4). Staining for microorganisms such as Gram stain and Lactophenol Blue needs to be considered [6, 7].
Large umbilical hernia, with a median infra-umbilical laparotomy scar. The autopsy incision should be lateralised to visualize the underlying structures without interrupting them.
Decubitus ulcer (pressure or bed sores). It is a starting point for skin sepsis.
Many external signs in the ocular conjunctiva, mucosa of the lips, teeth, ears, or skin, among others, may suggest the underlying disease, or be a key in differential diagnoses.
Internal examination findings only have an impact on the clinicopathological picture, causing or contributed to death.
Alterations in skin color, focal such as petechiae or ecchymosis (Figures 5 and 6) or diffuse such as jaundice, the presence of edema (Figure 7), suggest from external examination of the body that it will be necessary to study certain organs and systems, both macroscopically and through complementary tests, like imaging, histopathology, forensic histopathology, or molecular biology.
Petechiae on the chest and left flank. Thrombocytopenic purpura.
Extensive ecchymoses on the abdomen, pubic and genital region. Coagulopathy by anticoagulants.
Jaundice and generalized edema. Stillborn with Rh incompatibility and isoimmunization.
An example of this is how histology and forensic histopathology can be of great use in the diagnosis of skin alterations, which can give us an understanding of lesions or postmortem changes in the structure of the skin [8].
In congestive heart failure and myocardial infarction, vasodilation and congestion are observed, especially in the mucous membranes and conjunctivae, due to their transparency (Figure 8).
Conjunctival congestion. Acute myocardial infarction.
When pallor is severe, massive hemorrhage must be presumed. Occasionally, the source of bleeding is an external injury, as well as the gastrointestinal or respiratory tract (Figure 9).
Conjunctival pallor. Cutting wound.
In response to the COVID-19 pandemic, many countries have adapted their regulations on the examination of the deceased, reducing and eventually banning clinical autopsies [9, 10].
Forensic services, for their part, have restricted forensic autopsies, subjecting them to autopsy in case of a negative PCR for COVID-19, or even leaving the decision to prosecutors who do not usually handle technical criteria.
Therefore, forensic autopsies of potential cases and especially of confirmed cases, especially those without signs of violence, should be kept to a minimum and performed only when necessary, and internal examination of the body should be carried out only when necessary [11].
As it is necessary to have a cause of death to initiate a legal process and bring the case to justice, we can use the alternatives at our disposal to support the decision whether to do an autopsy and take certain samples.
In cases of sudden death, there is usually no history, so an autopsy and other complementary tests will be necessary, such as histopathology.
Deaths caused by trauma, asphyxia, and poisoning are classified as violent. A thorough external examination may be sufficient to document a case of violent death, for example:
In cases of traumatic death, where the offending element is often external to the body, it is essential to document the type, size, location, and relationship between external injuries. And non-invasive imaging tests may be useful.
If the presence of cyanosis suggests asphyxia, manual asphyxia and all lessons accompanied by constriction of the neck should be documented by photographs of the respective groove, fingerprints, nail stigmata, lividity studies, and lividity arrangement in cases of incomplete suspension.
Only if there are doubts about submersion asphyxia, it is necessary to prove the presence of a foreign body in the airway, which may require an internal examination.
In the case of poisoning, it should be borne in mind that for analysis and demonstration, it is necessary to isolate the toxic substance from the tissues or fluids of the corpse, for which it is essential to take a good sample. Although toxic substances produce what is called “asphyxia,” visible changes on external examination may be of value: Lividity of colors, other purplish spots, miosis, increased facial congestion are characteristic of some poisonings.
In cases of gunshot wounds, it is very important to determine the distance of the shot, the angle of the shot, the position in which the victim was when the projectile hit, and the entry and exit orifices (Figures 10 and 11).
Close-range shot, with concentric equimotic-erosive halo and gunshot residue encrustation on its periphery.
Long distance shot, no residue, with eccentric equimotic-erosive halo.
In cases of asphyxia due to aspiration of gastric contents, in addition to cyanosis, the cause of death may be evident on external examination (Figures 12 and 13).
Patient found dead at home. Morbid obesity with complicated umbilical hernia, and intestinal volvulus.
The same patient with severe cyanosis. Foamy hemorrhagic coming out of the mouth and nose.
Autopsy of newborns can provide information to physicians and families about the cause of death and the accuracy of the antemortem clinical diagnosis [12].
Some women have given birth to a newborn. These deaths are attributed to excessive delay in obstetric care and lack of control of the fetoplacental unit. It is essential to record the external features of the stillbirth body to establish the approximate date of death in utero and the gestational age [13, 14].
Routine external examination includes body measurements (at least: body weight, crown-rump length, crown-heel length, foot length, occipitofrontal circumference).
Detailed external examination, including nutritional status/soft tissue and muscle volume; the presence of edema (localized/generalized), pallor, meconium staining, jaundice or the presence of trauma, location of thoracic drains and vascular cannulae, and other iatrogenic lesions (Figures 14 and 15) [15].
Premature stillbirth. With skin. Sloughing.
Term newborn. Fully developed pinna.
The report should include a description of the external morphology specifically mentioning fontanelles, eyes, ears, nose, choanal patency, palatal fusion, spine, extremities, fingers, palmar creases, external genitalia, anal patency, umbilical cord [15]. It is used to diagnose, why some fetuses die in the prenatal period due incompatible life malformations (Figures 16–18) [16].
Premature and macerated fetus with large omphalocele.
Term newborn, with multiple head and body malformations.
Term newborn, with foot malformations.
Examination of the ovarian adnexa may also clarify the causes of death, such as large retroplacental clots (premature detachment of the placenta), opaque ovarian membranes (indicating ovarian infection), or true knots in the umbilical cord (Figure 19) [17, 18].
Malformation of the foot secondary to oligohydramnios. This condition is associated with polycystic kidney or renal agenesis.
Malnutrition, soiling, bed sores and colonization by insects on living persons [19, 20] are located on the conjunctivae, ulcers, genitalia, or other wounds (Figure 20). These are the elements that in a judicial process that allow proving the crime of abandonment or neglect of vulnerable people by their relatives or caregivers. An example of poor care is best illustrated by the diagnosis of marasmus and cachexia. These diseases were frequently diagnosed. They were only rarely cited as a cause of death [21].
Elderly adult male, living in a nursing home. Malnutrition, dirt, and dermatophytosis.
Despite the evolution of imaging techniques, the postmortem examination has maintained a key role in the clinical and forensic analysis. To obtain reliable information on the types of death and to allow a better understanding of the phenomenon, it is useful to study the results of clinical and forensic autopsies that start with the external examination of the corpse.
The search for and documentation of seemingly small details in the external examination of corpses help to resolve difficult situations surrounding medico-legal deaths, such as the identification of undocumented victims, the cause and manner of death, the postmortem interval, differential diagnoses of the cause of death, or the regulation of not performing complete autopsies during health crises.
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