Showing papers in "Natural resources research in 2010"
TL;DR: The results demonstrate that SVMs provide an accurate means to identify lithofacies in a heterogeneous sandstone reservoir and reveal that the SVM classifier performs the best of the three methods.
Abstract: The increasing technical demands placed on models with promising generalization performance to identify lithology from well-log data points has led to search for more efficient methods than conventional statistical methods. Conventional methods such as discriminant analysis recently applied neural networks belong to the general class of Empirical Risk Minimization techniques which aim to minimize training error. On the other hand, methods built on support vector machines (SVMs) are based on the Structural Risk Minimization principle which in turn is based on statistical learning theory. Statistical learning enhancement gives better generalization abilities by minimizing the testing error. In this research, a new modeling framework based on SVMs is described to identify lithfacies from well logs. A SVM classification formulation is presented together with feature selection based on fuzzy theory to identify potential features to enable discriminatory power from well logs. The results demonstrate that SVMs provide an accurate means to identify lithofacies in a heterogeneous sandstone reservoir. The true classification was based on detailed core description of a training well. The SVM-based lithology classifier was compared to discriminant analysis and probabilistic neural networks. The results reveal that the SVM classifier performs the best of the three methods.
84 citations
TL;DR: In this paper, the authors investigated underlying assumptions on resource availability and future production expectations to determine whether exaggerations can be found in the present set of emission scenarios as well as previous scenarios.
Abstract: Anthropogenic global warming caused by CO2 emissions is strongly and fundamentally linked to future energy production. The Special Report on Emission Scenarios (SRES) from 2000 contains 40 scenarios for future fossil fuel production and is used by the IPCC to assess future climate change. Previous scenarios were withdrawn after exaggerating one or several trends. This study investigates underlying assumptions on resource availability and future production expectations to determine whether exaggerations can be found in the present set of emission scenarios as well. It is found that the SRES unnecessarily takes an overoptimistic stance and that future production expectations are leaning towards spectacular increases from present output levels. In summary, we can only encourage the IPCC to involve more resource experts and natural science in future emission scenarios. The current set, SRES, is biased toward exaggerated resource availability and unrealistic expectations on future production outputs from fossil fuels.
79 citations
TL;DR: In this article, an artificial neural network (ANN) and a geographic information system (GIS) environment were used to analyze hydrothermal gold-silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an ANN and a GIS environment.
Abstract: The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPML and MPMW gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPIL and MPIW.
73 citations
TL;DR: In this paper, the authors studied the evolution of steam assisted gravity drainage (SAGD) steam chambers in heavy oil and bitumen reservoirs and found that the length scales of steam chamber growth depend on the permeability heterogeneity.
Abstract: Evolution of steam assisted gravity drainage (SAGD) steam chambers in heavy oil and bitumen reservoirs is tied to uniformity of steam pressure and quality along the length of the perforated interval of the well and reservoir geology and fluid properties adjacent to the well. If the reservoir geology has poor permeability at an interval along the wellpair, then steam delivery to and fluids production from the reservoir is not uniform. If the steam is well distributed throughout the injection well, then the key factor for a uniform steam chamber along the wellpair is reservoir geology. This is especially important in highly heterogeneous, variable thickness reservoirs where geology and reservoir oil composition may vary significantly over the length of a wellpair. Heterogeneity of a growing SAGD steam chamber is related to heterogeneity of the underlying geology. In this study the oil sands models are geostatistically populated to model spatial heterogeneity of permeability. The temperature profile (chamber growth), steam chamber height, conductive, convective, and total heat fluxes have been examined in each case. The results reveal that the length scales of steam chamber growth depend on the permeability heterogeneity. This provides a means to decide length scales for placement of in-well control devices in steam injectors in SAGD.
47 citations
TL;DR: A growing number of commentators are forecasting a near-term peak and subsequent terminal decline in the global production of conventional oil as a result of the physical depletion of the resource as discussed by the authors.
Abstract: A growing number of commentators are forecasting a near-term peak and subsequent terminal decline in the global production of conventional oil as a result of the physical depletion of the resource. These forecasts frequently rely on the estimates of the ultimately recoverable resources (URR) of different regions, obtained through the use of curve-fitting to historical trends in discovery or production. Curve-fitting was originally pioneered by M. King Hubbert in the context of an earlier debate about the future of the US oil production. However, despite their widespread use, curve-fitting techniques remain the subject of considerable controversy. This article classifies and explains these techniques and identifies both their relative suitability in different circumstances and the level of confidence that may be placed in their results. This article discusses the interpretation and importance of the URR estimates, indicates the relationship between curve fitting and other methods of estimating the URR and classifies the techniques into three groups. It then investigates each group in turn, indicating their historical origins, contemporary application and major strengths and weaknesses. The article then uses illustrative data from a number of oil-producing regions to assess whether these techniques produce consistent results as well as highlight some of the statistical issues raised and suggesting how they may be addressed. The article concludes that the applicability of curve-fitting techniques is more limited than adherents claim and that the confidence bounds on the results are wider than usually assumed.
45 citations
TL;DR: In this paper, the authors examined depth-temperature relationships near large population centers in western Canada, as well as remote communities in northern Canada, in order to provide a first order assessment of Enhanced Geothermal Systems (EGS) potential for electrical generation.
Abstract: Previous estimates of geothermal energy potential in Canada give an indication of available heat to be ‘farmed’ at depth. This article examines in more detail depth–temperature relationships near large population centers in western Canada, as well as remote communities in northern Canada, in order to provide a first order assessment of Enhanced Geothermal Systems (EGS) potential for electrical generation. Quantities of EGS thermal power output and electrical generation are dependent on output temperature and flow rate. We relate these potential power rates as a whole to drilling and installation cost for the doublet systems and triplet system. Results show areas with significant EGS potential in northern Alberta, northeastern British Columbia, and southern Northwest Territories related to high heat flow and thermal blanketing of thick sedimentary cover. Estimated installation costs in 2008 dollars are under 2 mln$/MWe. We also estimate significant reductions in CO2 emissions by conversion to geothermal electric production.
41 citations
TL;DR: In this paper, a long history of coal mining provides detailed time series of production and reserve estimates, which can be used to identify historical trends, and the historical trend toward reduced recoverable amounts is likely to continue into the future, with even stricter regulations imposed by increased environmental concern.
Abstract: The geological coal resource of the U.S. is abundant and proved coal reserves are listed as the world’s largest. However, the reserves are unevenly distributed and located in a small number of states, giving them major influence over future production. A long history of coal mining provides detailed time series of production and reserve estimates, which can be used to identify historical trends. In reviewing the historical evolution of coal reserves, one can state that the trend here does not point toward any major increases in available recoverable reserves; rather the opposite is true due to restrictions and increased focus on environmental impacts from coal extraction. Future coal production will not be entirely determined by what is geologically available, but rather by the fraction of that amount that is practically recoverable. Consequently, the historical trend toward reduced recoverable amounts is likely to continue into the future, with even stricter regulations imposed by increased environmental concern. Long-term outlooks can be created in many ways, but ultimately the production must be limited by recoverable volumes since coal is a finite resource. The geologic amounts of coal are of much less importance to future production than the practically recoverable volumes. The geological coal supply might be vast, but the important question is how large the share that can be extracted under present restrictions are and how those restrictions will develop in the future. Production limitations might therefore appear much sooner than previously expected.
39 citations
TL;DR: In this article, the authors extend minimum acceptance criteria to multiple point statistical comparisons between geostatistical realizations made with MPS algorithms and the associated training image (TI) to assess how well the MPS models have reproduced the input statistics of the training image.
Abstract: Geostatistical models should be checked to ensure consistency with conditioning data and statistical inputs. These are minimum acceptance criteria. Often the first and second-order statistics such as the histogram and variogram of simulated geological realizations are compared to the input parameters to check the reasonableness of the simulation implementation. Assessing the reproduction of statistics beyond second-order is often not considered because the “correct” higher order statistics are rarely known. With multiple point simulation (MPS) geostatistical methods, practitioners are now explicitly modeling higher-order statistics taken from a training image (TI). This article explores methods for extending minimum acceptance criteria to multiple point statistical comparisons between geostatistical realizations made with MPS algorithms and the associated TI. The intent is to assess how well the geostatistical models have reproduced the input statistics of the TI; akin to assessing the histogram and variogram reproduction in traditional semivariogram-based geostatistics. A number of metrics are presented to compare the input multiple point statistics of the TI with the statistics of the geostatistical realizations. These metrics are (1) first and second-order statistics, (2) trends, (3) the multiscale histogram, (4) the multiple point density function, and (5) the missing bins in the multiple point density function. A case study using MPS realizations is presented to demonstrate the proposed metrics; however, the metrics are not limited to specific MPS realizations. Comparisons could be made between any reference numerical analogue model and any simulated categorical variable model.
33 citations
TL;DR: In this paper, spatial statistics were used to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures in the Upper Cretaceous Mesaverde Group.
Abstract: The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.
20 citations
TL;DR: This paper highlights the performance of a radial basis function (RBF) network for ore grade estimation in an offshore placer gold deposit by using an orthogonal least-square algorithm and an approach utilizing data segmentation and genetic algorithm.
Abstract: This paper highlights the performance of a radial basis function (RBF) network for ore grade estimation in an offshore placer gold deposit. Several pertinent issues including RBF model construction, data division for model training, calibration and validation, and efficacy of the RBF network over the kriging and the multilayer perceptron models have been addressed in this study. For the construction of the RBF model, an orthogonal least-square algorithm (OLS) was used. The efficacy of this algorithm was testified against the random selection algorithm. It was found that OLS algorithm performed substantially better than the random selection algorithm. The model was trained using training data set, calibrated using calibration data set, and finally validated on the validation data set. However, for accurate performance measurement of the model, these three data sets should have similar statistical properties. To achieve the statistical similarity properties, an approach utilizing data segmentation and genetic algorithm was applied. A comparative evaluation of the RBF model against the kriging and the multilayer perceptron was then performed. It was seen that the RBF model produced estimates with the R2 (coefficient of determination) value of 0.39 as against of 0.19 for the kriging and of 0.18 for the multilayer perceptron.
17 citations
TL;DR: In this paper, the authors applied both Hydrochemical and numerical approaches to evaluate the groundwater quality of unconfined aquifer lying in Manukan Island, Sabah, East Malaysia.
Abstract: A rapid increase in the number of tourists has placed a heavy demand for freshwater on Manukan Island, a small island located offcoast of Kota Kinabalu, Sabah. Hydrochemical and numerical approaches have been applied in this study to evaluate the groundwater quality of unconfined aquifer lying in Manukan Island, Sabah, East Malaysia. This is vital to enhance better understanding about groundwater management. Hydrochemical analysis output indicated NaCl water type in sampling locations. Seawater intrusion is marked by its relatively high Na+, Mg2+, Cl− and SO4
2− concentrations. Hydrochemical analysis output clearly showed the influence of seawater in groundwater of Manukan Island. The numerical model output proved the influence of seawater in groundwater of Manukan Island by indicating the upconing process at the beneath of the pumping well. Current status of seawater intrusion in Manukan Island is about 14.6% of freshwater and seawater mixing ratio in low lying area of Manukan Island as simulated by SEAWAT-2000 model output. Numerical model SEAWAT-2000 output showed clearly that the upconing process is the possible route of seawater to influence the fresh groundwater aquifer chemistry in Manukan Island. The results have enhanced the current understanding of seawater intrusion in the study area. Future studies will focus on using numerical models to simulate and suggest suitable groundwater management plans in Manukan Island.
TL;DR: It will be shown that, in the presence of spatial autocorrelation, both in terms of in-sample fit and out-of-sample predictions the SACWE model is on par with the ALR model, while significantly outperforming the conventional THE AUTHORS model.
Abstract: One of the most important features of spatial datasets is that they often exhibit spatial autocorrelation, where locational similarities are observed jointly with similarities in values. Both logistic regression (LR) modelling and weights of evidence (WE) modelling are methods commonly applied in binary pattern recognition. While a spatially autocorrelated variant of the LR model, the so-called autologistic regression (ALR) model, exists in the literature, a spatially autocorrelated variant of the WE model does not exist. In this paper, a spatially autocorrelated weights of evidence (SACWE) model will be proposed. It will be demonstrated that the new model contains the same amount of spatial information as does an ALR model, and it is easy to program and implement. Via a simulation study, it will be shown that, in the presence of spatial autocorrelation, both in terms of in-sample fit and out-of-sample predictions the SACWE model is on par with the ALR model, while significantly outperforming the conventional WE model.
TL;DR: In this paper, the authors proposed a parallel upgrade to the more efficient supercritical steam turbines, which could decrease current emissions by up to 50% (from the current average plant efficiency of 32% to over 45%).
Abstract: The continuously decreasing average coal rank (heating value), inadequate investment, and ever stricter air-emission controls have caused the average efficiency of electricity generation from coal in the U.S. to plummet to a mere 32% by the year 2008. The U.S. gas-fired powerplants are 30% more efficient than the coal-fired ones, with average efficiency of 43% in 2008. Replacing each 1,000 MW
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generated by an average coal-fired powerplant with an average gas-fired powerplant would avoid today 7 million tonnes of CO2 emissions, 1.2 million tonnes of toxic ash, and significant issues with water contamination. The parallel upgrades to the more efficient supercritical steam turbines would decrease current emissions by up to 50% (from the current average plant efficiency of 32% to over 45%). The CO2 captured in the new combined-cycle powerplants might be used to enhance oil recovery in local fields, where feasible. The CO2 enhanced oil recovery (EOR) can never become the main sink for the gigantic CO2 volume generated each year by electric powerplants. Currently, EOR could absorb only 1% of that volume.
TL;DR: A brief review of the geology of the state of Kansas with respect to the known occurrence of oil and gas can be found in this paper, where the authors describe the geological story of Kansas as a simple story in generalities but complex in detail.
Abstract: The geological story of Kansas is told through the rocks that are present. It is a simple story in generalities but complex in detail. Knowing the story, gives insight into understanding the occurrence and location of possible economic valuable minerals, such as petroleum. This is a brief review of Kansas geology with respect to the known occurrence of oil and gas. Kansas is part of the Midcontinent oil province with oil having been discovered 150 years ago and commercial production commencing in 1873. Although many prospects remain in Kansas, the state has gone from the number 1 producer in the U.S. in 1916 to 8th today. Exploration for new oil and gas production therefore is going to have to be more imaginative and utilize new approaches and techniques to find the elusive petroleum. There are possibilities however for the prospector who can search diligently. Although the big fields probably have been discovered, the prospects today are deeper, in more undetectable traps, and in essentially untested places.
TL;DR: A multiple dependent variable weights of evidence (MDVWE) model will be developed and it will be shown that the MDVWE model outperforms the traditional THE AUTHORS model both in terms of in-sample fit and out-of-sample prediction accuracy.
Abstract: In binary spatial pattern recognition, there are many situations where the researcher could be interested in a number of dependent variables that are themselves correlated. For instance, different types of crime often coexist in the same area, or different species could share the same habitat. In cases like these, a natural correlation exists amongst the dependent variables of interest and is informative for spatial probability mapping. Weights of evidence (WE) modelling is a popular Bayesian probability method for binary pattern recognition, but it only deals with one single dependent variable at a time and ignores the correlation between the dependent variables. In this article, a multiple dependent variable weights of evidence (MDVWE) model will be developed. It will be shown that the new MDVWE model can be viewed as a restricted version of the conditional dependence-adjusted weights of evidence (CDAWE) model of Deng (Nat Resour Res 18(4):249–258, 2009). The MDVWE model is easy to program and implement. By means of a simulation study, it will be shown that the MDVWE model outperforms the traditional WE model both in terms of in-sample fit and out-of-sample prediction accuracy.
TL;DR: In this paper, the authors present an improved methodology to assess potential unconventional gas resources, which is a stochastic approach that includes Monte Carlo simulation and correlation between input variables, and apply it to the Uinta-Piceance province of Utah and Colorado with USGS data.
Abstract: Considering the important role played today by unconventional gas resources in North America and their enormous potential for the future around the world, it is vital to both policy makers and industry that the volumes of these resources and the impact of technology on these resources be assessed. To provide for optimal decision making regarding energy policy, research funding, and resource development, it is necessary to reliably quantify the uncertainty in these resource assessments. Since the 1970s, studies to assess potential unconventional gas resources have been conducted by various private and governmental agencies, the most rigorous of which was by the United States Geological Survey (USGS). The USGS employed a cell-based, probabilistic methodology which used analytical equations to calculate distributions of the resources assessed. USGS assessments have generally produced distributions for potential unconventional gas resources that, in our judgment, are unrealistically narrow for what are essentially undiscovered, untested resources. In this article, we present an improved methodology to assess potential unconventional gas resources. Our methodology is a stochastic approach that includes Monte Carlo simulation and correlation between input variables. Application of the improved methodology to the Uinta–Piceance province of Utah and Colorado with USGS data validates the means and standard deviations of resource distributions produced by the USGS methodology, but reveals that these distributions are not right skewed, as expected for a natural resource. Our investigation indicates that the unrealistic shape and width of the gas resource distributions are caused by the use of narrow triangular input parameter distributions. The stochastic methodology proposed here is more versatile and robust than the USGS analytic methodology. Adoption of the methodology, along with a careful examination and revision of input distributions, should allow a more realistic assessment of the uncertainty surrounding potential unconventional gas resources.
TL;DR: This paper shows that a structured PCA procedure based upon special lithological units is superior to an unstructured PCA, when the focus is within lithology variations, to data from the Heidrun field, offshore mid-Norway.
Abstract: Multivariate analysis is employed to investigate the structure of variations within highly heterogeneous data. Traditionally, principal component analysis (PCA) is run by analyzing the entire wireline log and using PCA scores to characterize variability within and between lithologies. In this paper, we propose a technique using only specific subsets of all well records to quantify reservoir heterogeneity due to second order lithological variability. These subsets are chosen from uniform lithofacies parts of the wireline log in order to reduce the variability in the correlation matrix that otherwise would cause lithological changes. The purpose is to assess the efficiency of structured PCA in analyzing small-scale heterogeneity that is captured by wireline logs but often masked by traditional PCA approaches. This paper shows that a structured PCA procedure based upon special lithological units is superior to an unstructured PCA, when the focus is within lithology variations. This structured procedure is applied to data from the Heidrun field, offshore mid-Norway. The results demonstrate clear benefits from added insight into the variability of a complex fluviodeltaic heterolithic sequence that poses great challenges to hydrocarbon development.
TL;DR: In this paper, the authors provided a new method to estimate recovery factors of oil resources, based on the geological features, such as the Mesozoic rift unit, the mesozoic and Cenozoic foreland unit, etc.
Abstract: This paper provides a new method to estimate recovery factors of oil resources. The China National Petroleum Assessment (2003–2007) (CNPA 2007) evaluates in-place oil resources and applies the recovery factor (RF) to estimate recoverable oil resources. The RF of oil resources plays an important role in the CNPA 2007. Based on the geological features, 24 types of oil assessment units are defined, such as the Mesozoic rift unit, the Mesozoic and Cenozoic foreland unit, etc. Through the recovery factor statistics of oil reserves (discovered) in different accumulations, as well as the potential analyses of enhanced petroleum recovery, appropriate RF valuing standards of oil resources (discovered and undiscovered) in different assessment units are developed. Calculation methods of oil resource RFs are established, including the appraisal standards, scoring, and calculation steps of oil resource RFs. Through the case studies, the valuing and appraisal standards of oil resource RFs are verified. Robust appraisal standards allow the RF method to be a valuable tool to effective assessment of China’s recoverable oil resources.
TL;DR: A new Ordered Weights of Evidence (OWE) model will be developed, borrowing the conceptual framework of the latent variable interpretation of the standard Ordered Logistic Regression (OLR) model, which can produce probability estimates for the presence of ordered discrete events.
Abstract: The standard Weights of Evidence (WE) model produces probability estimates for the presence of binary events. However, in many empirical studies the discrete event of interest can take on ordered values. For instance, the presence of mineral deposits may be classified further into different grades. In this paper, a new Ordered Weights of Evidence (OWE) model will be developed. Borrowing the conceptual framework of the latent variable interpretation of the standard Ordered Logistic Regression (OLR) model, the OWE can produce probability estimates for the presence of ordered discrete events. It will be shown that the OWE is computationally less intensive than the OLR. Through a simulation study, it will be shown that the OWE is comparable to the OLR both in terms of in-sample fit and out-of-sample forecasts.
TL;DR: In this article, the shape factor of a log-geometric distribution was derived from the ratio of frequencies between adjacent bins, and the calculation equations of a ratio of the mean size to the lower size-class boundary were deduced.
Abstract: The U.S. Geological Survey procedure for the estimation of the general form of the parent distribution requires that the parameters of the log-geometric distribution be calculated and analyzed for the sensitivity of these parameters to different conditions. In this study, we derive the shape factor of a log-geometric distribution from the ratio of frequencies between adjacent bins. The shape factor has a log straight-line relationship with the ratio of frequencies. Additionally, the calculation equations of a ratio of the mean size to the lower size-class boundary are deduced. For a specific log-geometric distribution, we find that the ratio of the mean size to the lower size-class boundary is the same. We apply our analysis to simulations based on oil and gas pool distributions from four petroleum systems of Alberta, Canada and four generated distributions. Each petroleum system in Alberta has a different shape factor. Generally, the shape factors in the four petroleum systems stabilize with the increase of discovered pool numbers. For a log-geometric distribution, the shape factor becomes stable when discovered pool numbers exceed 50 and the shape factor is influenced by the exploration efficiency when the exploration efficiency is less than 1. The simulation results show that calculated shape factors increase with those of the parent distributions, and undiscovered oil and gas resources estimated through the log-geometric distribution extrapolation are smaller than the actual values.
TL;DR: In this article, the authors examined the possibility of utilizing mode-converted (P-SV) waves for sub-basalt imaging as well as likely complicacies one may expect in such processes.
Abstract: The subsurface imaging using conventional seismic reflection technique is challenging in areas where high velocity rocks such as basalts are underlain by low velocity rocks. The seismic image quality worsens in the presence of intercalated sediments within the basaltic layers. In the recent years, the multicomponent seismic exploration technique has drawn great attention because it reduces the ambiguity in seismic imaging, enlarges the S-wave information, and improves the prediction and identification of reservoir fluids. Improvements in sub-basalt imaging techniques could hold highly significant geologic implications such as resource exploration and identifying permanent geochemical trapping potential (such as for carbon sequestration studies). In this article, we examine the possibility of utilizing mode-converted (P-SV) waves for sub-basalt imaging as well as likely complicacies one may expect in such processes.
TL;DR: In this article, a hydrodynamic-based investigation of constructed wetland suitability for fish habitat is presented, in which a constructed wading board is manoeuvred to imitate river habitat by means of computer modelling.
Abstract: This paper presents a hydrodynamic-based investigation of constructed wetland suitability for fish habitat. A constructed wetland adjacent to river is manoeuvred to imitate riparian fish habitat by means of computer modelling. Flow-velocity conditions, water depths and effects of macrophytes to resistance of flow in constructed wetland are modelled and steered towards creating a favourable ground for natural substrate spawning of native fish species. The model suggests combination of two zones, which are a macrophyte zone for typical functions of a wetland and an open water zone with gravel bed to support fish reproductive cycles.