Showing papers in "Natural resources research in 2017"
TL;DR: In this paper, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated.
Abstract: Research on processing geochemical data and identifying geochemical anomalies has made important progress in recent decades Fractal/multi-fractal models, compositional data analysis, and machine learning (ML) are three widely used techniques in the field of geochemical data processing In recent years, ML has been applied to model the complex and unknown multivariate geochemical distribution and extract meaningful elemental associations related to mineralization or environmental pollution It is expected that ML will have a more significant role in geochemical mapping with the development of big data science and artificial intelligence in the near future In this study, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated More applications are needed to demonstrate the advantage of ML in solving complex problems in the geosciences
122 citations
TL;DR: In this article, the authors derived typical production curves of tight oil wells based on monthly production data from multiple horizontal Eagle Ford shale oil wells, and estimated ultimate recovery (EUR) was calculated using two empirical production decline curve models, the hyperbolic and the stretched exponential function.
Abstract: This study derives typical production curves of tight oil wells based on monthly production data from multiple horizontal Eagle Ford shale oil wells. Well properties initial production (IP) rate and production decline rate were documented, and estimated ultimate recovery (EUR) was calculated using two empirical production decline curve models, the hyperbolic and the stretched exponential function. Individual well productivity, which can be described by IP level, production decline curvature and well lifetime, varies significantly. The average monthly IP was found to be around 500 bbl/day, which yields an EUR in the range of 150–290 kbbl depending on used curve, assumed well lifetime or production cutoff level. More detailed analyses on EUR can be made once longer time series are available. For more realistic modeling of multiple wells a probabilistic approach might be favorable to account for variety in well productivity. For less detailed modeling, for example conceptual regional bottom-up production modeling, the hyperbolic function with deterministic parameters might be preferred because of ease of use, for example with the average parameter values IP = 500 bbl/day, D = 0.3 and b = 1 resulting in an EUR of 250 kbbl with a 30-year well lifetime, however, with the recognition that this extrapolation is uncertain.
77 citations
TL;DR: In this paper, the Synthetic Minority Over-sampling Technique was applied to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50.
Abstract: Data-driven prospectivity modelling of greenfields terrains is challenging because very few deposits are available and the training data are overwhelmingly dominated by non-deposit samples.
This could lead to biased estimates of model parameters. In the present study involving Random Forest (RF)-based gold prospectivity modelling of the Tanami region, a greenfields terrain in Western Australia, we apply the Synthetic Minority Over-sampling Technique to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50. An optimal threshold range is determined objectively using statistical measures such as the data sensitivity, specificity, kappa and per cent correctly classified. The RF regression modelling with the modified dataset of close to 50:50 sample ratio of deposit to non-deposit delineates 4.67% of the study area as high prospectivity areas as compared to only 1.06% by the original dataset, implying that the original “sparse” dataset underestimates prospectivity.
50 citations
TL;DR: In this paper, robust principal components analysis (RPCA) and singularity mapping (SM) were applied to extract anomalous patterns of the multivariate geochemical footprint of mineralization.
Abstract: In this study, stream sediment geochemical data have been subjected to robust principal components analysis (RPCA) and singularity mapping (SM) to enhance and map significant multivariate geochemical anomalies (i.e., mineralization-related) in Ahar area, NW Iran. The RPCA was applied to (a) account for the compositional nature of stream sediment geochemical data using suitable log-ratio transformation, (b) modulate the effect of outliers in component estimation and (c) derive a multivariate geochemical footprint of mineralization. The SM was applied to extract anomalous patterns of the multivariate geochemical footprint of mineralization. The exploration targets were then delineated using Student’s t-statistics analysis. The correlations of mapped exploration targets with the known mineral occurrences and mineralization-related patterns were further evaluated using normalized density index and overall accuracy analyses.
48 citations
TL;DR: In its 26 years of existence, Natural Resources Research (NRR) has published and continues to publish papers on geochemical anomaly and mineral potential mapping This is consistent with its aims and scope to publish quantitative studies of natural resources exploration, evaluation and exploitation, including environmental and risk-related aspects as discussed by the authors.
Abstract: In its 26 years of existence, the journal of Natural Resources Research (NRR) has published and continues to publish papers on geochemical anomaly and mineral potential mapping This is consistent with its aims and scope to publish quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects Over the years, NRR has contributed significantly more to the publication of developments in mineral potential mapping and notably less to the publication of developments in geochemical anomaly mapping In more detail, NRR has contributed significantly more to the publication of research on development of robust quantitative methods for analysis and synthesis of spatial evidence of mineral potential but notably less to the publication of research on development of geologically focused models of mineral potential The editorship of NRR recognizes the latter as a challenge to promote further research on development of numerically robust as well as geologically focused mineral potential models, and this special issue is a major initiative in response to that challenge The recent inclusion of Natural Resources Research for coverage by the Clarivate Analytics (formerly the Institute for Scientific Information) in the Science Citation Index Expanded™ and Journal Citation Reports® (JCR) Science Edition will help make Natural Resources Research meet that challenge
40 citations
TL;DR: Radial basis function link neural network (RBFLN) and fuzzy-weights of evidence (fuzzy-WofE) methods were used to assess regional-scale prospectivity for chromite deposits in the Western Limb and the Nietverdiend layered mafic intrusion of the Bushveld Complex in South Africa as discussed by the authors.
Abstract: Radial basis function link neural network (RBFLN) and fuzzy-weights of evidence (fuzzy-WofE) methods were used to assess regional-scale prospectivity for chromite deposits in the Western Limb and the Nietverdiend layered mafic intrusion of the Bushveld Complex in South Africa Five predictor maps derived from geological, geochemical and geophysical data were processed in a GIS environment and used as spatial proxy for critical processes that were most probably responsible for the formation of the chromite deposits in the study area The RBFLN was trained using input feature vectors that correspond to known deposits, prospects and non-deposits The training was initiated by varying the number of radial basis functions (RBFs) and iterations The results of training the RBFLN provided optimum number of RBFs and iterations that were used for classification of the input feature vectors The results show that the network classified 73% of the validation deposits into highly prospective areas for chromite deposit, covering 65% of the study area The RBFLN entirely classified all the non-deposit validation points into low prospectivity areas, occupying 866% of the study area In general, the efficiency of the RBFLN in classifying the validation deposits and non-deposits indicates the degree of spatial relationship between the input feature vectors and the training points, which represent chrome mines and prospects The RBFLN and fuzzy-WofE analyses used in this study are important in guiding identification of regional-scale prospect areas where further chromite exploration can be carried out
37 citations
TL;DR: In this paper, a combination of economic tradeoffs, property rights definitions, liability issues, and risk considerations suggests that CO2 storage offshore of the United States may be more feasible than onshore, especially during the current (early) stages of industry development.
Abstract: Given a scarcity of commercial-scale carbon capture and storage (CCS) projects, there is a great deal of uncertainty in the risks, liability, and their cost implications for geologic storage of carbon dioxide (CO2). The probabilities of leakage and the risk of induced seismicity could be remote, but the volume of geologic CO2 storage (GCS) projected to be necessary to have a significant impact on increasing CO2 concentrations in the atmosphere is far greater than the volumes of CO2 injected thus far. National-level estimates of the technically accessible CO2 storage resource (TASR) onshore in the United States are on the order of thousands of gigatons of CO2 storage capacity, but such estimates generally assume away any pressure management issues. Pressure buildup in the storage reservoir is expected to be a primary source of risk associated with CO2 storage, and only a fraction of the theoretical TASR could be available unless the storage operator extracts the saltwater brines or other formation fluids that are already present in the geologic pore space targeted for CO2 storage. Institutions, legislation, and processes to manage the risk, liability, and economic issues with CO2 storage in the United States are beginning to emerge, but will need to progress further in order to allow a commercial-scale CO2 storage industry to develop in the country. The combination of economic tradeoffs, property rights definitions, liability issues, and risk considerations suggests that CO2 storage offshore of the United States may be more feasible than onshore, especially during the current (early) stages of industry development.
37 citations
TL;DR: In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies, where the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from.
Abstract: In this paper, a pixel-based mapping of geochemical anomalies is proposed to avoid estimation errors resulting from using interpolation methods in the modeling of anomalies. The pixel-based method is a discrete field modeling of geochemical landscapes for mapping lithogeochemical anomalies. In this method, the influence area of each composite rock sample is the whole area covered by a pixel where the materials of the sample were taken from. In addition to the pixel-based method, because delineation of mineral exploration target areas using geochemical data is a challenging task, the application of metal zoning concept is demonstrated for vectoring into porphyry mineralization systems. In this regard, different geochemical signatures of the deposit-type sought were mapped in a model. Application of the proposed pixel-based method and the metal zoning concept is a powerful tool for targeting areas with potential for porphyry copper deposits.
32 citations
TL;DR: In this article, it is shown through spatial correlation analysis that using enrichment factors is no better than using log-ratios of geochemical data for mapping of deposit-related anomalies.
Abstract: According to previous studies, the use of enrichment factors in environmental studies is inconsistent with its original concept and that such indiscriminate use of enrichment factors should be stopped. In this contribution, it is shown through spatial correlation analysis that using enrichment factors is no better than using log-ratios of geochemical data for mapping of deposit-related anomalies.
31 citations
TL;DR: In this paper, the Sarvak Formation was divided into nine zones, and the thinner sub-zones were used for further fine modeling procedure, which provided sophisticated distribution of petrophysical parameters throughout the grids of the model, and therefore it can handle strong heterogeneity of the complex reservoir.
Abstract: Introducing and applying an appropriate strategy for reservoir modeling in strongly heterogeneous and fractured reservoirs is a controversial issue in reservoir engineering. Various integration approaches have been introduced to combine different sources of information and model building techniques to handle heterogeneity in geological complex reservoir. However, most of these integration approaches in several studies fail on modeling strongly fractured limestone reservoir rocks of the Zagros belt in southwest Iran. In this study, we introduced a new strategy for appropriate modeling of a production formation fractured rock. Firstly, different rock types in the study area were identified based on well log data. Then, the Sarvak Formation was divided into nine zones, and the thinner subzones were used for further fine modeling procedure. These subzones were separated based on different fracture types and fracture distribution in each zone. This strategy provided sophisticated distribution of petrophysical parameters throughout the grids of the model, and therefore, it can handle strong heterogeneity of the complex reservoir. Afterward, petrophysical parameters were used to produce an up-scaled 3D gridded petrophysical model. Subsequently, maps of petrophysical properties were derived for each zone of the Sarvak Formation. Evidences achieved in this study indicates Sarvak Formation zone 2 as the target production zone with better performance of reservoir rock and the southwestern part of the field as area of maximum porosity.
31 citations
TL;DR: In this paper, an AHP-Shannon entropy weighting approach was proposed for the estimation of porphyry-Cu potential mapping in Markazi Province, Iran, and the output mineral potential map was evaluated by field checking and chemical analysis of samples.
Abstract: This paper presents an AHP–Shannon Entropy weighting approach as a new hybrid method for assigning evidential weights in mineral potential mapping. For demonstrating the proposed method, a case study was selected for porphyry-Cu potential mapping in Markazi Province, Iran. Then, geo-datasets were gathered, and evidence layers were generated for integration by TOPSIS method (via combination of AHP–Shannon Entropy weighting). Finally, the output mineral potential map was evaluated by field checking and chemical analysis of samples. Two outcrops with evidence of a porphyry system were encountered in areas with high potential values. In addition, there was good correlation between high potential values and Cu content of samples taken from the field. Hence, the usefulness of the AHP–Shannon Entropy weighting of evidence for MPM was demonstrated.
TL;DR: The potential costs of commercial-scale CO2 storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources as discussed by the authors.
Abstract: Carbon capture from stationary sources and geologic storage of carbon dioxide (CO2) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO2 storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO2, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO2 storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO2 injection will be mitigated by reservoir pressure management, estimates of the costs of CO2 storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO2 storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO2 storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the scarcity of (data from) long-term commercial-scale CO2 storage projects, decision makers may experience considerable difficulty in ascertaining the realistic potential, the likely costs, and the most beneficial pattern of deployment of CCS as an option to reduce CO2 concentrations in the atmosphere.
TL;DR: In this paper, the authors combine knowledge-and data-driven prospectivity mapping approaches by using the receiver operating characteristics (ROC) spatial statistical technique to optimize the process of rescaling input data.
Abstract: This paper combines knowledge- and data-driven prospectivity mapping approaches by using the receiver operating characteristics (ROC) spatial statistical technique to optimize the process of rescaling input datasets and the process of data integration when using a fuzzy logic prospectivity mapping method. The methodology is tested in an active mineral exploration terrain within the Paleoproterozoic Perapohja Belt (PB) in the Northern Fennoscandian Shield, Finland. The PB comprises a greenschist to amphibolite facies, complexly deformed supracrustal sequence of variable quartzites, mafic volcanic rocks and volcaniclastic rocks, carbonate rocks, black shales, mica schists and graywackes. These formations were deposited on Archean basement and 2.44 Ga layered intrusions, during the multiple rifting of the Archean basement (2.44–1.92 Ga). Younger intrusive units in the PB comprise 2.20–2.13 Ga gabbroic sills or dikes and 1.98 Ga A-type granites. Metamorphism and complex deformation of the PB took place during the Svecofennian orogeny (1.9–1.8 Ga) and were followed by intrusions of post-orogenic granitoids (1.81–1.77 Ga). The recent mineral exploration activities have indicated several gold-bearing mineral occurrences within the PB. The Rompas Au-U mineralization is hosted within deformed and metamorphosed calc-silicate veins enclosed within mafic volcanic rocks and contains uranium-bearing zones without gold and very high-grade (>10,000 g/t Au) gold pockets with uraninite and uraninite-pyrobitumen nodules. In the vicinity of the Rompas, a magnesium skarn hosted disseminated-stockwork gold mineralization was also recognized at the Palokas-Rajapalot prospect. The exploration criteria translated into a fuzzy logic prospectivity model included data derived from regional till geochemistry (Fe, Cu, Co, Ni, Au, Te, K), high-resolution airborne geophysics (magnetic field total intensity, electromagnetic, gamma radiation), ground gravity and regional bedrock map (structures). The current exploration licenses and exploration drilling sites for gold were used to validate the knowledge-driven mineral prospectivity model.
TL;DR: In this article, the impact of well operation conditions on wax precipitation in an oil sample, and to predict the wax-free well flowrate was simulated. But the authors only used microscopy under high pressure with grain size analysis and light-scattering technique.
Abstract: The objective of this research is to simulate the impact of well operation conditions on wax precipitation in an oil sample, and to predict the wax-free well flowrate. Laboratory studies help producers to protect oil wells from potential problems. The maximum rise of simulated well operation conditions to in situ oil recovery leads to oilfield practice. The methods used for testing of oil sample were microscopy under high pressure with grain size analysis and light-scattering technique, which were conducted using laboratory equipment suited for investigations of reservoir fluids in conditions close to oilfield conditions. Experiments with modeling of temperature and pressure drop rates, flow velocity, and flow through time from downhole to wellhead were carried out. These experiments resulted in modeling of the relationship between functional pressure and wax appearance temperature (WAT), which is properly consistent with the Clapeyron–Clausius equation in a range of well operation conditions. Experimental simulation of well thermobaric operation conditions also resulted in definition of potential wax formation area in the tubing. Research data showed that WAT declines with increase in flow velocity and temperature, and pressure drop rates. Calculations demonstrated that an increase in flow velocity by 0.04 m/sec (equivalent to a well flowrate of 20 m3 per day) leads to a decrease in wax formation depth of up to approximately 200 meters. Guidelines for slowdown of asphaltene–resin–paraffin particles formation in the well by chemical treatment are made.
TL;DR: In this paper, the impact of varying equipment sizes on a highly variable three destination, Au and Cu bench, in a sulfide/oxide deposit was analyzed, and it was shown that selectivity sizing profit and size relationships are nonlinear, and exhibit severe break points if insufficiently selective equipment is used.
Abstract: Equipment sizing is a developed field of mining engineering, which considers all aspects related to productivity, and grade distribution. Current methods of equipment sizing consider block dilution, but do not analyze the impact of the selectivity changes on practical dig-limits. This research analyzed the impact of varying equipment sizes on a highly variable three destination, Au and Cu bench, in a sulfide/oxide deposit. The study shows that selectivity sizing profit and size relationships are nonlinear, and exhibit severe break points if insufficiently selective equipment is used. The proposed technique can be used for sizing mine equipment in complex deposits.
TL;DR: In this article, a simple mathematical model for the lifetime of a fossil fuel resource is presented and presented in this paper based on several observations of historical production data, and the authors show that a very important period in the life of energy resources is a period when the demand of these resources increases almost linearly.
Abstract: A critical examination of Hubbert’s model proves that it does not account for several factors that have significantly influenced the production of petroleum and other fossil fuels. The effect of these factors comes into the price of the fossil fuels, and the latter has a significant influence on the demand and rate of production of energy resources as well as on the long-term rate of production growth at both the regional and global levels. Based on several observations of historical production data, a simple mathematical model is constructed and presented in this paper for the lifetime of a fossil fuel resource. The recent data of global petroleum and natural gas production show that a very important period in the life of energy resources is a period when the demand of these resources increases almost linearly. The linear part of the production curve makes the entire lifetime production of the resource asymmetric. Information on the total available quantity of a resource at any time and of the average slope during this linear period yields an estimate of the timescale, T
2, when peak production is reached and depletion follows. The total available quantity of the energy resource is laden with significant uncertainty, which propagates in the estimates of the timescale of the peak production in any resource model. The time asymmetry of the current model leads to a delay of the timescale, when the onset of the resource production commences (e.g., peak oil). However, the rate of the resource production decline is significantly higher than that predicted by other models that use a symmetrical curve-fitting method.
TL;DR: In this article, a conceptual mineral system model and corresponding prospectivity model were developed by delineating the known mineral deposits and occurrences of Sn-F-REE mineralization that were not used to assign weights to the evidential maps.
Abstract: This paper presents mineral prospectivity mapping to identify potential new exploration ground for polymetallic Sn–F–REE mineralization associated with the Bushveld granites of the Bushveld Igneous Complex, South Africa. The Lebowa Granite Suite, commonly known as the Bushveld granites, is host to a continuum of polymetallic mineralization with a wide range of metal assemblages (Sn–Mo–W–Cu–Pb–Zn–As–Au–Ag–Fe–F–U–REE), ranging from a high-temperature to a low-temperature magmatic hydrothermal mineralizing environment. The prospectivity map was generated by fuzzy logic modeling and a selection of targeting criteria (or spatial proxies) based on a conceptual mineral system highlighting critical processes responsible for the formation of the polymetallic mineralization. The spatial proxies include proximity to differentiated granites (as heat and metal-rich fluid sources), Rb geochemical map (fluid-focusing mechanism such as fractionation process), principal component maps (PC 4 Y–Th and PC 14 Sn–W, fluid pathways for both high- and low-temperature mineralization) and proximity to roof rocks (traps for fluids). Logarithmic functions were used to rescale rasterized evidential maps into continuous fuzzy membership scores in a range of [0, 1]. The evidential maps were combined in two-staged integration matrix using fuzzy AND, OR and gamma operators to produce the granite-related polymetallic Sn–F–(REE) prospectivity map. The conceptual mineral system model and corresponding prospectivity model developed in this study yielded an encouraging result by delineating the known mineral deposits and occurrences of Sn–F–(REE) mineralization that were not used to assign weights to the evidential maps. The prospectivity model predicted, on average, 77% of the known mineral occurrences in the BIC (i.e., 56 of 73 Sn occurrences, 12 of 15 F occurrences and 6 of 8 REE occurrences). Based on this validation, 13 new targets were outlined in this study.
TL;DR: In this article, it was shown that the best statistical model for Cu deposits is a worldwide Pareto-lognormal model, in which the basic lognormal size-frequency distribution is flanked by two juxtaposed pareto distributions for the largest and smallest Cu deposits.
Abstract: Recently, large worldwide databases with statistics on amounts of metal in mineral deposits have become available. Frequently, most metal is contained in the largest deposits for a metal. A major problem in meaningful modeling of the size–frequency distributions of the largest deposits is that they are very rare. Until
now it was rather difficult to establish the exact form of their size–frequency distribution. However, because of the new very large databases it can now be concluded that two commonly used approaches (lognormal and Pareto) thought to be mutually incompatible in the past, are both correct with a high probability. One approach does not necessarily exclude validity of the other. Patino-Douce (Nat Resour Res 25(1):97–124, 2016b) has shown that metal tonnage frequency distributions for worldwide metal deposits are approximately lognormal with similar standard deviations (σ) of log-transformed data. In this paper, it is assumed that worldwide metals satisfy both lognormal and Pareto models simultaneously. Copper and Au are taken for example for comparison with results previously obtained for these two metals in the Abitibi area of the Canadian Shield. Worldwide there are 2541 Cu deposits approximately satisfying a lognormal distribution. Total amount of Cu in these deposits is 2.319 × 109 tons of Cu. However, the 45 largest deposits, which together contain 1.281 × 109 tons of Cu, satisfy a Pareto distribution. If their lognormal model would apply in the upper tail as well, these 45 largest deposits should have contained only about 0.076 × 109 tons of Cu. It is shown in detail for Cu that the best statistical model for Cu deposits is a worldwide Pareto–lognormal model in which the basic lognormal size–frequency distribution is flanked by two juxtaposed Pareto distributions for the largest and smallest Cu deposits, respectively. Both Pareto distributions smoothly change into the central lognormal by means of bridge functions that can be determined separately. The worldwide Pareto–lognormal model also was found to be applicable to several other metals, especially Ag, Ni, Pb, and U. For Au, the model does not work as well for the upper tail Pareto distribution as it does for the other metals taken for example.
TL;DR: In this paper, the results of predictive mapping using up-to-date mineral system concepts and recently finished regional-scale geological mapping, stream sediment and airborne geophysical surveys conducted by the Geological Survey of Brazil are presented.
Abstract: The Gurupi Belt hosts a Paleoproterozoic gold province located in north–northeastern Brazil, at the borders of Para and Maranhao states. It is considered to be an extension of the prolific West African Craton’s Birimian gold province into South America. Additionally, the belt has been the object of recent mineral exploration programs with significant resource discoveries. This study presents the results of predictive mapping using up-to-date mineral system concepts and recently finished regional-scale geological mapping, stream sediment and airborne geophysical surveys conducted by the Geological Survey of Brazil. We relate gold mineralization to an initially enriched crust, metamorphism, deep fluid pathways, structurally controlled damage zones and hydrothermal alteration. Prospective targets were generated using only regional public datasets and knowledge-driven targeting technique. This work did not incorporate any known gold deposits, yet it predicted the largest known deposits and their satellite targets. Besides, high prospective targets mapped almost 40% of known primary gold occurrences within 7% of the project area. This work allowed considerable search area reduction and identification of new target areas, thus collaborating on reducing costs, time and risk of mineral exploration. Results indicate that we achieved an efficient understanding of the geological processes related to the Gurupi Belt mineral system.
TL;DR: In this article, the significance of dissolved organic carbon (DOC) in layered coastal aquifers of the Pondicherry region during four different seasons was studied in three different seasons: pre-monsoon, southwest monsoon, northeast monsoon and postmonsoon.
Abstract: Carbon, which is an essential element found in rocks and minerals, is used by biologically diverse life forms as a source of energy. Natural organic carbon is mainly derived from decomposing vegetation and other organic matter in the soil zone. Dissolved organic carbon (DOC) is an important component in biogeochemical cycling of elements characterized by high susceptibility to leaching. The significance of DOC was studied in layered coastal aquifers of the Pondicherry region during four different seasons. Pondicherry region has varied geological setup ranging from Cretaceous to Recent formations. A total of 324 groundwater samples were collected from various aquifers, namely Alluvium, Tertiary, Cretaceous, and Mixed formations, during different seasons of pre-monsoon, southwest monsoon, northeast monsoon, and post-monsoon. The samples were analyzed for major ions and DOC. The range of DOC in the study area is 0–10 mg/l. Very high DOC concentrations were measured in most of the samples from Alluvium and Upper Cuddalore Formation and in few samples from the Lower Cuddalore Formation. The relationships of DOC with other ions in this study indicate that the hydrochemistry of groundwater was controlled by both aerobic and anaerobic environments in the different formations of the study area.
TL;DR: In this article, a real-world mining application of pair-copulas is presented to model the spatial distribution of metal grade in an ore body, where the spatial paircopula model is adopted over other copula-based spatial models since it is better able to capture complex spatial dependence structures.
Abstract: A real-world mining application of pair-copulas is presented to model the spatial distribution of metal grade in an ore body. Inaccurate estimation of metal grade in an ore reserve can lead to failure of a mining project. Conventional kriged models are the most commonly used models for estimating grade and other spatial variables. However, kriged models use the variogram or covariance function, which produces a single average value to represent the spatial dependence for a given distance. Kriged models also assume linear spatial dependence. In the application, spatial pair-copulas are used to appropriately model the non-linear spatial dependence present in the data. The spatial pair-copula model is adopted over other copula-based spatial models since it is better able to capture complex spatial dependence structures. The performance of the pair-copula model is shown to be favorable compared to a conventional lognormal kriged model.
TL;DR: In this article, the authors assess the utility of using a space-time cube (STC) and associated analyses to evaluate and characterize mining claim activities around the McDermitt Caldera in northern Nevada and southern Oregon.
Abstract: Resource managers and agencies involved with planning for future federal land needs are required to complete an assessment of and forecast for future land use every ten years. Predicting mining activities on federal lands is difficult as current regulations do not require disclosure of exploration results. In these cases, historic mining claims may serve as a useful proxy for determining where mining-related activities may occur. We assess the utility of using a space–time cube (STC) and associated analyses to evaluate and characterize mining claim activities around the McDermitt Caldera in northern Nevada and southern Oregon. The most significant advantage of arranging the mining claim data into a STC is the ability to visualize and compare the data, which allows scientists to better understand patterns and results. Additional analyses of the STC (i.e., Trend, Emerging Hot Spot, Hot Spot, and Cluster and Outlier Analyses) provide extra insights into the data and may aid in predicting future mining claim activities.
TL;DR: In this paper, Agterberg et al. proposed a Pareto-lognormal model for estimating the approximate value at which the upper-tail frequency amplification model comes into effect.
Abstract: Pareto-lognormal modeling of worldwide metal deposit size–frequency distributions was proposed in an earlier paper (Agterberg in Nat Resour 26:3–20, 2017). In the current paper, the approach is applied to four metals (Cu, Zn, Au and Ag) and a number of model improvements are described and illustrated in detail for copper and gold. The new approach has become possible because of the very large inventory of worldwide metal deposit data recently published by Patino Douce (Nat Resour 25:97–124, 2016c). Worldwide metal deposits for Cu, Zn and Ag follow basic lognormal size–frequency distributions that form straight lines on lognormal Q–Q plots. Au deposits show a departure from the straight-line model in the vicinity of their median size. Both largest and smallest deposits for the four metals taken as examples exhibit hyperbolic size–frequency relations and their Pareto coefficients are determined by fitting straight lines on log rank–log size plots. As originally pointed out by Patino Douce (Nat Resour Res 25:365–387, 2016d), the upper Pareto tail cannot be distinguished clearly from the tail of what would be a secondary lognormal distribution. The method previously used in Agterberg (2017) for fitting the bridge function separating the largest deposit size–frequency Pareto tail from the basic lognormal is significantly improved in this paper. A new method is presented for estimating the approximate deposit size value at which the upper tail Pareto comes into effect. Although a theoretical explanation of the proposed Pareto-lognormal distribution model is not a required condition for its applicability, it is shown that existing double Pareto-lognormal models based on Brownian motion generalizations of the multiplicative central limit theorem are not applicable to worldwide metal deposits. Neither are various upper tail frequency amplification models in their present form. Although a physicochemical explanation remains possible, it is argued that preferential mining of the largest and smallest orebodies can have economic historical reasons. The size–frequency distribution of uranium can be regarded as lognormal without Pareto tails. At the end of the paper, it is shown that original copper deposit size data can be used for forward projection of discovery trends toward the end of this century.
TL;DR: In this article, the capability of different ISC kinetic models to predict the combustion behaviors of different types of oils (light oil, heavy oil, and bitumen) was investigated.
Abstract: The in situ combustion (ISC) process is of interest as an enhanced oil recovery method because it is an alternative to traditional steam-based processes for heavy oil and bitumen recovery. ISC is a technique applicable outside the window of reservoir conditions deemed appropriate for steam injection (such as deeper and thinner reservoirs). The process involves complex chemical reactions and physical recovery mechanisms, and predicting the likelihood of successful ISC in field applications remains challenging. This paper describes a numerical investigation of the capability of different ISC kinetic models to predict the combustion behaviors of different types of oils (light oil, heavy oil, and bitumen). Three kinetic models (of Coats, Crookston, and Belgrave) were selected from literature and compared using data from four published combustion-tube experiments. The comparison procedure is as follows: (1) validate the numerical modeling of each kinetic model by matching the selected experimental results or duplicating the numerical results found in published literature; (2) adjust fluid viscosities and densities to match the fluid properties of each experiment;and (3) use each validated kinetic model to predict the performance of the other experiments without further tuning the kinetic parameters. The knowledge derived from the experiments provides guidance for choosing the appropriate kinetic model when no other data are available and for the preliminary design and screening study of a potential ISC project.
TL;DR: In this paper, a loss distribution model is developed and applied to estimate the probabilities of finding enough new mineral deposits to meet demand, throughout the rest of this century, of the ten metals: Au, Ag, Cu, Mo, Pb, Zn, Ni, Co, Cr, and platinum-group elements (PGE).
Abstract: A loss distribution model is developed and applied to estimate the probabilities of finding enough new mineral deposits to meet demand, throughout the rest of this century, of the ten metals: Au, Ag, Cu, Mo, Pb, Zn, Ni, Co, Cr, and platinum-group elements (PGE). The model assumes that the necessary amount of metal exists in undiscovered mineral deposits and looks only at the probabilities of finding the required amount of metal in undiscovered resources. The probability density function that describes aggregate tonnage discovery over a specified period of time is the convolution of a mass density function that describes number of discoveries per unit time with a probability density function that describes deposit size distribution. Two alternatives for the deposit size distribution density function are used: pure lognormal or lognormal with a power-law upper tail, both taken from Patino Douce (Nat Resour Res 25:365–387, 2016c). A Pascal (negative binomial) distribution is found to accurately reproduce number of yearly metal discoveries for the period 1950–2007, and is used to estimate future discovery probabilities. The convolution of the two functions is accomplished by means of a simple Monte Carlo method. Numerical experiments consisting of 106 iterations are used to estimate the aggregate tonnage most likely to be discovered until the year 2100, as well as the largest deposit most likely to be discovered over the same time period, together with confidence intervals for the two quantities. The results for Au, Ag, Zn, and Cu strongly suggest that serious shortages of these metals are likely to occur before the year 2100. At the other end of the spectrum, the models suggest that supplies of Mo and Co are not likely to become critical over that time frame. Ni and Pb occupy intermediate positions, and the results for Cr and PGE are inconclusive, chiefly owing to the large variability found in their deposit size distributions. Using a pure lognormal distribution versus a lognormal distribution with a power-law upper tail for deposit sizes does not affect these conclusions. The results do not prove nor disprove that the required amount of metal exists in undiscovered resources, but provide concrete actionable intelligence about the intensity of the exploration efforts needed to find the metal, if the deposits exist.
TL;DR: In this article, a new approach using long-term commitment (LTC) and futures contracts is proposed to assess the Net Present Value (NPV) of an iron ore mining project.
Abstract: Iron ore was traditionally traded using long-term commitment (LTC) contracts. In the last decade, with the surging demand from China, a futures market was created for iron ore. In this paper, using historical information from this futures market, we focus on modeling market dynamics of Iron Fine 62% Fe—CFR Tianjin Port (China) futures contracts to determine optimal parameter values of the Schwartz (J Financ 52:923–973, 1997) two-factor model. A new approach using LTC and futures contracts is proposed to assess the Net Present Value (NPV) of an iron ore mining project. We apply Kalman filtering techniques to calibrate the two-factor commodity model to iron ore futures for the January 2014–November 2016 period. The Kalman filter is useful to infer unobservable variables from noisy measurements. In the Schwartz (1997) two-factor model, the unobservable spot price and convenience yield are inferred from futures contracts transactions. Model parameters are fitted using maximum likelihood optimization. Using parameters derived from the Kalman filtering and the maximum likelihood approach, spot price simulations for the next 7 years are made for three scenarios. The NPV of a mining project is calculated for each scenario. Then, both LTC and futures markets are treated separately and the mining company can choose which proportion of its production to sell in each market. Results show that the calibration and NPV simulation workflow can be effectively used to assess the profitability of a mining project, accounting for the exposure to futures markets.
TL;DR: In this article, the authors measured local variability using the coefficient of variation (CV) and analyzed the error in block grade estimates as a function of sampling grid, for various block dimensions (volumes) and for a given CV interval.
Abstract: Mineral deposit grades are usually estimated using data from samples of rock cores extracted from drill holes. Commonly, mineral deposit grade estimates are required for each block to be mined. Every estimated grade has always a corresponding error when compared against real grades of blocks. The error depends on various factors, among which the most important is the number of correlated samples used for estimation. Samples may be collected on a regular sampling grid and, as the spacing between samples decreases, the error of grade estimated from the data generally decreases. Sampling can be expensive. The maximum distance between samples that provides an acceptable error of grade estimate is useful for deciding how many samples are adequate. The error also depends on the geometry of a block, as lower errors would be expected when estimating the grade of large-volume blocks, and on the variability of the data within the region of the blocks. Local variability is measured in this study using the coefficient of variation (CV). We show charts analyzing error in block grade estimates as a function of sampling grid (obtained by geostatistical simulation), for various block dimensions (volumes) and for a given CV interval. These charts show results for two different attributes (Au and Ni) of two different deposits. The results show that similar errors were found for the two deposits, although they share similar features: sampling grid, block volume, CV, and continuity model. Consequently, the error for other attributes with similar features could be obtained from a single chart.
TL;DR: In this paper, the authors confirm a correct approach of quantifying histogram uncertainty by comparing to reference uncertainty found by an automated scan-based approach, which is calculated by finding similar patterns of a data configuration within a large image: the mean of the specified domain is computed for each pattern to attain the true variance of the mean.
Abstract: There is always uncertainty in the representative histogram required as an input for geostatistical modeling. This uncertainty should be quantified correctly and incorporated into final modeling because it affects resource/reserve estimation, investment and development decisions. This paper confirms a correct approach of quantifying histogram uncertainty by comparing to reference uncertainty found by an automated scan-based approach. The true variance of the mean is considered as the reference uncertainty. This variance is calculated by finding similar patterns of a data configuration within a large image: The mean of the specified domain is computed for each pattern to attain the true variance of the mean. The correct quantification of histogram uncertainty is defined. The spatial bootstrap provides prior uncertainty that does not consider the domain limits and is not conditioned to the data. This uncertainty is updated in geostatistical modeling to consider the limits and data. The resulting uncertainty closely matches the scan-based approach. A realistic case study is presented.
TL;DR: In this paper, the authors investigated the CO2 sequestration and coal bed methane (CH4) production potential of the subbituminous to high-volatile C bituminous Healy Creek Formation coals through preliminary sensitivity analyses, experimental design methods, and fluid flow simulations.
Abstract: Naturally fractured, unmineable coal seam reservoirs are attractive targets for geological sequestration of carbon dioxide (CO2) because of their high CO2 adsorption capacity and possible cost offsets from enhanced coal bed methane production. In this study, we have investigated the CO2 sequestration and coal bed methane (CH4) production potential of the subbituminous to high-volatile C bituminous Healy Creek Formation coals through preliminary sensitivity analyses, experimental design methods, and fluid flow simulations. The sensitivity analyses indicate that the total volumes of CO2 sequestered and CH4 produced from the Healy Creek coals are mostly sensitive to bottom-hole injection pressure, coal matrix porosity, fracture porosity, fracture permeability, coal compressibility, and coal volumetric strain. The results of the Plackett–Burman experimental design were used to further generate proxy models for probabilistic reservoir forecasts. The probabilistic estimates for the mature, subbituminous to high-volatile C bituminous Healy Creek coals in the entire Nenana Basin indicate that it is possible to sequester between 0.41 trillion cubic feet (TCF) (P10) and 0.05 TCF (P90) of CO2 while producing between 0.36 TCF (P10) and 0.05 TCF (P90) of CH4 at the end of 44-year forecast. Fluid flow scenarios show that CO2 sequestration through a primary reservoir depletion method is the most effective way to inject CO2 in the coals of the Nenana Basin. Including a horizontal well instead of the vertical well resulted in relatively high average gas production rates and subsequent total cumulative gas production. The CO2 buoyancy scenario suggests that the effect of CO2 buoyancy and the nature of caprock should be considered in identifying potential geologic sites for CO2 sequestration.
TL;DR: In this article, the authors compare Monte Carlo simulation and stochastic programming for uncertainty analysis of a gas field development and an oilfield development, and show that both methods yield the exact same optimum design.
Abstract: Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.