Geomechanical Engineering Research Articles

Rock Mass Classification of Chalk, a UK Perspective

AbstractInvestigating the geomechanical engineering behaviour of Chalk is challenging since it is a generally soft rock formation and presents in cases a different behaviour from other competent rocks. Additionally, Chalk is widespread throughout the United Kingdom and other parts of the world. Thus, an in-depth understanding of Chalk is paramount when considered in engineering projects. In this research work, the geomechanical behaviour of Northern Province Chalk is examined. This study mainly involves characterising two significant units of Northern Province Chalk, namely Flamborough and Burnham Chalk, through intensive fieldwork and laboratory testing. Multiple scanline surveys are conducted along the cliffs of Flamborough to characterise the jointed nature of the Chalk. The samples collected are assessed in the lab to determine the physical properties of the intact Chalk. Moreover, the Chalk mass properties are compared to those estimated from empirical classification systems. Finally, the limitations of applying rock mass classification systems to Chalk have also been highlighted.

Review on Constitutive Model for Simulation of Weak Rock Mass

Understanding the behavior of weak rock masses is important for predicting the stability of structures under different loading conditions. Traditional models such as the generalized Hoek–Brown and Coulomb weak plane are widely used; however, they often fail to capture the nonlinear and irreversible behavior of weak rock masses. This study offers a comprehensive overview of a critical analysis of constitutive models’ strengths and limitations for simulating weak rock masses. By comparing traditional and advanced novel approaches such as the strength degradation of rock (SDR) masses and continuous damage mechanics (CDM), this investigation shows that the new advanced methods significantly enhance the quality and accuracy of simulations. Moreover, SDR models address the limitations of classical plasticity models by incorporating nonlinear stress paths and irreversible stress changes, while CDM offers detailed insights into microstructural defect progression. These advancements allow for more accurate and practical predictions of long-term stability in geomechanical engineering tailored to specific requirements of each project.

A New Correlation for Estimating Static Elastic Properties of Sandstone Formations: Case Study from Rumaila Oilfield

Evaluating the elastic characteristics of reservoir's rocks is crucial for the oil and gas sector to reduce risks throughout drilling and production operations. Elastic parameters are essential for prediction of wellbore stability, estimation of in-situ stresses, optimization of drilling performance and design of hydraulic fracturing. Depending on the type of formation, Elastic parameter is varied dramatically. Thus, a precise way to identify these characteristics is needed. Inaccurate assessment of elastic characteristics can result in excessive expenditure and inappropriate field development plans. Static elastic modulus (Est) can be determined through laboratory testing. Although laboratory measurement is the most precise approach to determine the elastic modulus, it requires a continuous core sample to acquire a complete profile of the formation's elastic characteristics. However, just a portion of the well is typically sampled for cores. On the other hand, wireline log data is applied to determine the dynamic modulus (Edyn), allowing for continuous assessment of elastic properties. Numerous characteristics of the rock, including consolidation, pore pressure and lithology affect the dynamic modulus. Thus, it needs to be converted to Static elastic modulus by empirical formulae. Several empirical equations have been proposed to convert dynamic Young's modulus to static Elastic modulus. Nevertheless, the validity of these relationships is restricted and exclusive to certain areas. This research seeks to establish a relationship between Dynamic modulus and static Elastic modulus for sandstone formation of Rumaila oil field. The suggested correlation provides accurate predictions of the static elastic modulus for the complete sandstone segment. The newly created correlation performed better than the previous correlations in predicting static elastic modulus with average absolute error of 8.69 %, and correlation coefficient of 0.9848. The newly established empirical correlation can assist geo-mechanical engineers in estimating continuous profile of the sandstone formation's static modulus.

HYSTERESIS OF IMPLICIT EQUATIONS IN HYPOPLASTICITY FOR SOIL MATERIALS WITH GRANULAR HARDNESS DEGRADATION

We study a hypoplastic model for soil and granular materials stemming from geomechanical engineering which further incorporates effects of degradation of the granular hardness, therefore allowing for the description of environmental weathering. The governing system is described by a nonlinear system of transcendental-differential equations for stress and strain rate, which is investigated with respect to its long-time dynamic. Under deviatoric stress control, two different solutions of the underlying, implicit differential equations are constructed analytically. The spherical components of stress and strain rate converge asymptotically to an attractor and lead to the sparsification of material states. Whereas under cyclic loading-unloading carried out in a numerical simulation, finite ratcheting of the deviatoric strain rate is observed in the form of a square spiral.

An equivalent thermo-hydro-mechanical model for gas migration in saturated rocks

Gas transport process in saturated rocks is gaining increasing attention in the environmental/energy geomechanical engineering fields, e.g., shale gas exploitation, carbon dioxide sequestration and nuclear waste disposal, etc., as it is closely associated with gas production rate, sealing capability of caprocks. To investigate gas migration behavior through saturated rocks which induces microcracking inside the material, an equivalent thermo-hydro-mechanical (THM) model is developed that considers coupled behavior of fractures. The fractured rock material is considered as the equivalent continuum, which consists of isotropic porous matrix and parallel fracture set. The mathematical formulations are constructed within the thermodynamic framework, where the THM constitutive relations are proposed. Compared to the model without considering fracture behavior, the equivalent model improves the calculation accuracy of about 60% against laboratory experiments (e.g., observed gas flow rate), which proves the model capability. Furthermore, the sensitivity analysis shows that highly-pressurized gas induced microcracking has significant influence on the coupled process, which greatly contributes to the observed gas flow phenomenon.

A stacked generalisation methodology for estimating the uniaxial compressive strength of rocks

Uniaxial compressive strength (UCS) has become a highly essential strength parameter in the mining, civil and geomechanical industries. Estimating the exact value of the strength of rock has become a matter of great concern in real life. Despite this, there have been many works to indirectly/directly estimate the UCS of rocks. This study introduces a novel stacked generalisation methodology for estimating the UCS of rocks in geomechanics. In this study, generalised regression neural network (GRNN), radial basis function neural network (RBFNN), and random forest regression (RF) were used as the base learners and the multivariate adaptive regression spline (MARS) functioned as the meta-learner for the proposed stacking method. The proposed 3-Base learner stack model exhibited dominance over single applied AI methods of GRNN, RBFNN, and RF when confirmed with similar datasets by employing performance metrics like the Nash–Sutcliffe Efficiency Index (NSEI), Root Mean Squared Error (RMSE), Performance Index (PI), Scatter Index (SI) and Bayesian Information Criterion (BIC). The proposed 3-Base learner stack model scored the least RMSE, PI, and SI scores of 1.02775, 0.50691, and 0.00788 respectively for the testing datasets. In addition, it also produced the utmost NSEI value of 0.99969 and the least BIC value of 16.456 as likened to other competing models (GRNN, RBFNN and RF), reaffirming its power in forecasting the UCS of rocks in geomechanical engineering.

The Evaluation of Rock Mass Characteristics against Seepage for Sustainable Infrastructure Development

The determination of rock seepage characteristics is a complex phenomenon due to the variability, discontinuities, and formation age of rocks. The available literature on rock mechanics covers empirical relationships and approaches for the estimation of seepage characteristics from the rock mass parameters. In this study, an area comprising of infrastructure such as a water reservoir, embankments, roads, etc., constructed on mix rock mass formations was selected. The field and laboratory tests’ geo-mechanical data for the study area were evaluated. The data obtained from the field geo-mechanical engineering tests like Rock Quality Designation (RQD), Rock Core Recovery, Lugeon, etc., were analyzed. The data retrieved from the geological and geotechnical laboratory tests such as petrography, uniaxial compression, Hoek shear, elastic modulus, etc., were also evaluated. Rock mass was characterized based on petrographic and RQD, and was found in the hybrid formation of igneous, metamorphic, and sedimentary deposits. Seepage analysis in the study area was also carried out based on adit and piezometric data (installed in accordance with the mining technology guidelines), using Seep W Finite Element Method (FEM). The seepage observed in adits were compared with seepage calculated from Seep W. The trend of simulated flux was also presented against K ratio. Seepage quantities for different ranges of K ratio were plotted to evaluate interdependency between seepage and K ratio. Correlations of RQD were developed with hydraulic conductivity “k” for igneous, metamorphic, and sedimentary rocks for quick assessment of seepage characteristics of rock mass by RQD. These correlations and seepage related evaluations will be beneficial for the characterization of rock mass in relation to seepage for sustainable infrastructure development.

Implementation of the Non-Associated Elastoplastic MSDPu Model in FLAC3D and Application for Stress Analysis of Backfilled Stopes

The multiaxial Mises-Schleicher and Drucker-Prager unified (MSDPu) criterion has been shown to exhibit several specific features compared to other yield and failure criteria, including a nonlinear mean stress dependency, influence of the Lode angle, use of independent uniaxial compressive and tensile strength values and absence of an apex (singularity) on the envelope surface in the negative stress quadrant. However, MSDPu has been seldom used in practice to solve geotechnical and geomechanical engineering problems mainly because it had not yet been fully implemented into three-dimensional (3D) numerical codes. To fill this gap, a 3D elastoplastic MSDPu formulation is developed and implemented into FLAC3D. The proposed MSDPu elastic-perfectly plastic (EPP) constitutive model is then validated against existing analytical solutions developed for calculating the stress and displacement distributions around cylindrical openings. The FLAC3D MSDPu-EPP model is then applied to evaluate the vertical and horizontal stress distributions in a three-dimensional vertical backfilled stope. The numerical results obtained with the MSDPu-EPP model are compared with those obtained with the Mohr-Coulomb EPP model, to highlight key features of the new formulation.

2019 Canadian Geotechnical Colloquium: Mitigating a fatal flaw in modern geomechanics: understanding uncertainty, applying model calibration, and defying the hubris in numerical modelling

This paper has been written to achieve two objectives. The first objective is to provide a discussion of the practical limitations of numerical modelling in the field of geomechanical engineering. Too many discussions of numerical methods in geomechanical engineering are centred on the impressive ability of numerical tools to conduct complex and sophisticated analyses with relative ease and efficiency. Practitioners need to have grounded conversations on numerical modelling regarding the reality that geomechanical designs are often data-limited with high degrees of uncertainty. When data limits and uncertainty are overlooked, geomechanical engineers are at risk of introducing unforeseen fatal flaws into engineering design. The second objective is to provide “how to” guidelines for model calibration using a variety of data types to qualify and quantify ground reaction. Model calibration is truly the only means to reduce numerical uncertainties. Formal training in numerical modelling is often focused on software utilization and computational methods; however, there are few opportunities for formal training on how to calibrate a model for practical engineering applications. This paper provides guidelines for calibration methods and procedures.

A new look into the prediction of static Young's modulus and unconfined compressive strength of carbonate using artificial intelligence tools

Accurate estimation of rock elastic and failure parameters plays a vital role in petroleum, civil and geotechnical engineering applications. During drilling operations, continuous logs of rock elastic and failure parameters are considered very helpful in optimizing geomechanical earth models. Commonly, rock elastic and failure parameters are estimated using well logs and empirical correlations. These are calibrated with rock mechanics laboratory experiments conducted on core samples. However, since these samples are expensive to get and time-consuming to test, artificial intelligence (AI) models based on available petrophysical well logs such as bulk density, compressional wave and shear wave travel times are utilized to predict the static Young's modulus ( E static ) and the unconfined compressive strength (UCS) – with an emphasis on carbonate rocks. We present two AI techniques in this study: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The dataset used in this study contains 120 data points obtained from a Middle Eastern carbonate reservoir from which we develop an empirically correlated ANN model to predict E static and an ANFIS model to predict the UCS. A comparison between the UCS, predicted by the proposed ANFIS model, and the published correlations show that the ANFIS model predicted the UCS with less error and with a high coefficient of determination. The error obtained from the ANFIS model was 4.5%, while other correlations resulted in up to 30% error on a published dataset. On the basis of the results obtained, we can say that the developed models will help geomechanical engineers to predict E static and the UCS using well logs without the need to measure them in the laboratory. Thematic collection: This article is part of the Naturally Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/naturally-fractured-reservoirs

Investigating the post-mining subsidence and the long-term stability of old mining excavations: case of Cow Pasture Limestone Mine, West Midlands, UK

Assessing the long-term stability of abandoned mine workings is a challenging problem in geo-mechanical engineering. There are several factors contributing to the long-term behaviour of excavations. Creep and the gradual deterioration of rocks are, in particular, among the dominant factors affecting the long-term stability of abandoned mine workings. In this paper, the mechanism of post-mining subsidence over old room-and-pillar mine workings is investigated. A case history of post-mining subsidence over Cow Pasture abandoned limestone mine in the West Midlands of the UK is presented. The geological and geotechnical characteristics of the mining site are explained. Empirical and analytical approaches are then used to study the mechanisms of post-mining subsidence. Outcomes of the analyses show that the bearing capacity failure of the roof strata and the gradual punching of pillars into the moisture-sensitive roof layers have been the key mechanisms initiating failure and leading to the formation of post-mining subsidence in the Cow Pasture Mine.

Core log integration: a hybrid intelligent data-driven solution to improve elastic parameter prediction

Current oil prices and global financial situations underline the need for the best engineering practices to recover remaining hydrocarbons. A good understanding of the elastic behavior of the reservoir rock is extremely imperative in avoiding the severe well drilling problems such as wellbore in-stability, differential sticking, kicks, and many more. Therefore, it is plausible to have a good estimation of the rock elastic behavior for successful well operations. This study presents a generalized empirical model to predict static Poisson’s ratio of the carbonate rocks. Petrophysical well logs were used as inputs, and the laboratory measured static Poisson’s ratio was used as an output. Three supervised artificial intelligence (AI) techniques were used, viz. artificial neural network (ANN), support vectors regression, and adaptive network-based fuzzy interference system. An extensive prediction comparison was made between these three AI techniques. Based on the lowest average absolute percentage error (AAPE) and highest coefficient of determination (R2), the ANN model proposed to be the best model to predict static Poisson’s ratio. To transform black box nature of AI model into a white box, ANN-based empirical correlation is also developed to predict the static Poisson’s ratio. Comparison of the developed empirical correlation with previously established approaches to find static Poisson’s ratio on an unseen published dataset revealed that the equation of ANN can predict the static Poisson’s ratio with implicitly less AAPE and with high R2 value. The proposed model with the empirical correlation can assist geo-mechanical engineers to predict the static Poisson’s ratio in the absence of core data. The novelty of the new equation is that it can be used without the need of any AI software.

Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Static Poisson's ratio (νstatic) is a key factor in determine the in-situ stresses in the reservoir section. νstatic is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. νstatic is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. νstatic can be measured in the lab using a real reservoir cores. The laboratory measurements of νstatic will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting νstatic for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the νstatic. The developed artificial neural network (ANN) model yielded more accurate results for estimating νstatic based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for νstatic gave a coefficient of determination (R2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of νstatic without the need for coring and laboratory work and hence will reduce the overall cost of the well.

Development of New Mathematical Model for Compressional and Shear Sonic Times from Wireline Log Data Using Artificial Intelligence Neural Networks (White Box)

Compressional (P-wave) and shear (S-wave) velocities are used to estimate the dynamic geomechanical properties including: Poisson’s ratio, Young’s modulus, and Lame parameters. These parameters are mainly used in estimating the static properties of the formation rocks as well as the in situ stresses. The sonic logs are not always available, epically for old wellbores. Also, in several occasions when the sonic logs are available, missing sections found in the well logs might affect the analysis results. To the authors’ knowledge, there is no single straightforward correlation that can be used to accurately estimate both P- and S-wave travel times directly from the well log data. Most of the existing correlations use the P-wave velocity to measure the S-wave velocity. The main purpose of this study is to develop accurate and simple empirical models using wireline log data (bulk density, gamma ray, and neutron porosity) to predict the sonic travel times (P-wave and S-wave). These wireline logs are slandered wireline log data that are commonly recorded in most of the wells. Three robust artificial intelligence techniques, namely: support vector machine (SVM), artificial neural network (ANN), and adaptive neurofuzzy interference systems (ANFIS), were employed and compared based on their prediction performance. Ultimately, using the weights and biases of optimized ANN model, a simple generalized empirical correlation is derived that can be used without the need of costly commercial software’s to run the AI models. The obtained results showed that ANN, ANFIS, and SVM can be used to estimate P-wave and S-wave travel times. ANN outperformed the ANFIS and SVM by yielding the lowest average absolute percentage error (AAPE) and the highest coefficient of determination $$\left( R^{2}\right) $$ for predicting P-wave and S-wave travel times. ANN model could predict the P-wave and S-wave travel times from wireline log data with high accuracy giving $$R^{2}$$ of 0.98 when compared to actual field data. In addition, the developed empirical correlations prediction completely matched the ANNs prediction. The AAPE of the predicted P and S-waves travel times was less than 5%. The developed correlations are very accurate and can help geomechanical engineers to determine the dynamic geomechanical properties (Poisson’s ratio and Young’s modulus) and propose any operation in case where sonic logs are missing.

An integrated approach for estimating static Young’s modulus using artificial intelligence tools

Elastic parameters play a key role in managing the drilling and production operations. Determination of the elastic parameters is very important to avoid the hazards associated with the drilling operations, well placement, wellbore instability, completion design and also to maximize the reservoir productivity. A continuous core sample is required to be able to obtain a complete profile of the elastic parameters through the required formation. This operation is time-consuming and extremely expensive. The scope of this paper is to build an advanced and accurate model to predict the static Young’s modulus using artificial intelligence techniques based on the wireline logs (bulk density, compressional time, and shear time). More than 600 measured core data points from different fields were used to build the AI models. The obtained results showed that ANN is the best AI technique for estimating the static Young’s modulus with high accuracy [R2 was 0.92 and the average absolute percentage error (AAPE) was 5.3%] as compared with ANFIS and SVM. For the first time, an empirical correlation based on the weights and biases of the optimized ANN model was developed to determine the static Young’s modulus. The developed correlation outperformed the published correlations for static Young’s modulus prediction. The developed correlation enhanced the accuracy of predicting the static Young’s modulus. (R2 was 0.96 and AAPE was 6.2%.) The developed empirical correlation can help geomechanical engineers determine the static Young’s modulus where laboratory core samples are not available.

UGS in giant offshore salt caverns to substitute the actual Brazilian NG storage in LNG vessels

This paper evaluates the importance of underground natural gas storage in Brazil and the selection of an area for such, considering logistical and geomechanical aspects. Currently, the power plants are fueled by natural gas. This gas consumption is seasonal, volatile and associated with the level of rainfall in the hydroelectric power plants' basins. Therefore, Brazil has been using floating stocks of Liquefied Natural Gas (LNG) in LNG carriers. This contingency storage is inefficient and costly. This article demonstrates the technical feasibility of replacing LNG floating stocks by underground caverns created by dissolving rock salt domes offshore. Rock salt has a negligible permeability to most fluids and gasses even under high pressure. The rock salt skeleton also develops the phenomenon of creep, through which can absorb high levels of strain and can self-heal cracks and faults over time. This article also presents the design of the caverns by computer simulation. The computer codes were developed by (Costa, 1978) and (Costa, 1984), since than theses codes have been used in several different types of geomechanical engineering projects.

The geology of geomechanics: petroleum geomechanical engineering in field development planning

Abstract The application of geomechanics to oil and gas field development leads to significant improvements in the economic performance of the asset. The geomechanical issues that affect field development start at the exploration stage and continue to affect appraisal and development decisions all the way through to field abandonment. Field developments now use improved static reservoir characterization, which includes both the mechanical properties of the field and the initial stress distribution over the field, along with numerical reservoir modelling to assess the dynamic stress evolution that accompanies oil and gas production, or fluid injection, into the reservoirs. Characterizing large volumes of rock in the subsurface for geomechanical analysis is accompanied by uncertainty resulting from the low core sampling rates of around 1 part per trillion (ppt) for geomechanical properties and due to the remote geophysical and petrophysical techniques used to construct field models. However, some uncertainties also result from theoretical simplifications used to describe the geomechanical behaviour of the geology. This paper provides a brief overview of geomechanical engineering applied to petroleum field developments. Select case studies are used to highlight how detailed geological knowledge improves the geomechanical characterization and analysis of field developments. The first case study investigates the stress regimes present in active fault systems and re-evaluates the industry's interpretation of Andersonian stress states of faulting. The second case study discusses how the stress magnitudes and, potentially, stress regimes can change as a result of production and pore pressure depletion in an oil or gas field. The last case study addresses the geomechanical characterization of reservoirs, showing how subtle changes in geological processes are manifested in significant variations in strength. The studies presented here illustrate how the timely application of petroleum geomechanical engineering can significantly enhance field development, including drilling performance, infill drilling, completion design, production and recovery.

Simulation of weathered profiles coupled with multivariate block-support simulation of the Puma nickel laterite deposit, Brazil

Modelling and assessing spatial variability and uncertainty of mineral deposits is critical for both capital investments in mining projects as well as operational issues once a mine is developed. However, traditional approaches for modelling geological domains and geostatistical estimation provide smoothed representations of the pertinent deposit attributes, ignore spatial variability and, thereby, can mislead downstream decisions. Spatial variability and related uncertainty in modelling mineral deposit characteristics of interest, ranging from metal content and geological boundaries to geomechanical - geotechnical rock properties, can be modelled and quantified by stochastic spatial simulations. This is demonstrated through a detailed, step- by-step application to the Puma deposit, a major nickel lateritic asset in Brazil, part of the Onça-Puma mining complex. To integrate the variability of the regolith profiles of the deposit, their thicknesses are calculated after an unwrinkling process is applied and the deposit is then jointly simulated using min/max autocorrelation factors (MAF). The realizations serve as geological boundaries within which Ni, Co, Fe, SiO2, MgO and Dry-tonnage factor (DTF) are subsequently jointly simulated directly at block support scale. The final result is a series of equally probable representations of the Puma deposit, which are used to quantify and assess the uncertainty about key aspects of the project at the Puma deposit, such as the uncertainty of the in-situ resources documented herein, and the strict control of the ore's quality that feeds the ferronickel processing plant. The framework presented shows the advantages of the MAF and direct block simulation approaches for the efficient joint simulation of spatially variant geological attributes of large deposits for industrial environments. The methods presented are general, new in the context of geotechnical and geomechanical engineering, and can assist in the modelling of spatial variability and quantification of uncertainty linked to geotechnical rock properties and pertinent lithological boundaries.

Dear geomechanical engineers and enthusiastic tunnellers! / Liebe Geomechaniker und begeisterte Untertagebauer!

Geomechanics and TunnellingVolume 6, Issue 6 p. 624-625 EditorialFree Access Dear geomechanical engineers and enthusiastic tunnellers! / Liebe Geomechaniker und begeisterte Untertagebauer! Robert Galler, Robert GallerSearch for more papers by this author Robert Galler, Robert GallerSearch for more papers by this author First published: 02 December 2013 https://doi.org/10.1002/geot.201390045AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume6, Issue6December 2013Pages 624-625 RelatedInformation

Dear geomechanical engineers and tunnelling friends! / Liebe Geomechaniker und Tunnelbaufreunde!

Geomechanics and TunnellingVolume 6, Issue 4 p. 294-294 EditorialFree Access Dear geomechanical engineers and tunnelling friends! / Liebe Geomechaniker und Tunnelbaufreunde! Robert Galler, Robert GallerSearch for more papers by this author Robert Galler, Robert GallerSearch for more papers by this author First published: 01 August 2013 https://doi.org/10.1002/geot.201390029AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume6, Issue4August 2013Pages 294-294 RelatedInformation