Long-term implications of new U.S. gas estimates

The US Geological Survey’s 1995 estimates of domestic undiscovered plus undeveloped natural gas nearly tripled quantities estimated in its 1989 Assessment. Much of the increase came from selected unconventional resources assessed using the paradigm of continuous-type accumulations. These include such seemingly unrelated “unconventional” gas occurrences as “tight gas,” coalbed gas, gas in shales, and deep basin-center gas. Though only a small fraction of the assessed 352 trillion cubic feet is now economic, the quantity is nevertheless significant. Moreover, the lowest cost resources are close to major gas markets where competing conventional gas is modest. With continued technological improvements these resources can contribute significantly to future U.S. gas supply, even without subsidies     

A Modified Weights-of-Evidence Method for Regional Mineral Resource Estimation

Weights-of-evidence (WofE) modeling and weighted logistic regression (WLR) are two methods of regional mineral resource estimation, which are closely related: For example, if all the map layers selected for further analysis are binary and conditionally independent of the mineral occurrences, expected WofE contrast parameters are equal to WLR coefficients except for the constant term that depends on unit area size. Although a good WofE strategy is supposed to achieve approximate conditional independence, a common problem is that the final estimated probabilities are biased. If there are N deposits in a study area and the sum of all estimated probabilities is written as S, then WofE generally results in S > N. The difference S − N can be tested for statistical significance. Although WLR yields S = N, WLR coefficients generally have relatively large variances. Recently, several methods have been developed to obtain WofE weights that either result in S = N, or become approximately unbiased. A method that has not been applied before consists of first performing WofE modeling and following this by WLR applied to the weights. This method results in modified weights with unbiased probabilities satisfying S = N. An additional advantage of this approach is that it automatically copes with missing data on some layers because weights of unit areas with missing data can be set equal to zero as is generally practiced in WofE applications. Some practical examples of application are provided.     

Development and Implementation of a Bayesian-based Aquifer Vulnerability Assessment in Florida

The Florida Aquifer Vulnerability Assessment (FAVA) was designed to provide a tool for environmental, regulatory, resource management, and planning professionals to facilitate protection of groundwater resources from surface sources of contamination. The FAVA project implements weights-of-evidence (WofE), a data-driven, Bayesian-probabilistic model to generate a series of maps reflecting relative aquifer vulnerability of Florida’s principal aquifer systems. The vulnerability assessment process, from project design to map implementation is described herein in reference to the Floridan aquifer system (FAS). The WofE model calculates weighted relationships between hydrogeologic data layers that influence aquifer vulnerability and ambient groundwater parameters in wells that reflect relative degrees of vulnerability. Statewide model input data layers (evidential themes) include soil hydraulic conductivity, density of karst features, thickness of aquifer confinement, and hydraulic head difference between the FAS and the watertable. Wells with median dissolved nitrogen concentrations exceeding statistically established thresholds serve as training points in the WofE model. The resulting vulnerability map (response theme) reflects classified posterior probabilities based on spatial relationships between the evidential themes and training points. The response theme is subjected to extensive sensitivity and validation testing. Among the model validation techniques is calculation of a response theme based on a different water-quality indicator of relative recharge or vulnerability: dissolved oxygen. Successful implementation of the FAVA maps was facilitated by the overall project design, which included a needs assessment and iterative technical advisory committee input and review. Ongoing programs to protect Florida’s springsheds have led to development of larger-scale WofE-based vulnerability assessments. Additional applications of the maps include land-use planning amendments and prioritization of land purchases to protect groundwater resources.     

Estimation of undiscovered deposits in quantitative mineral resource assessments—examples from Venezuela and Puerto Rico

Quantitative mineral resource assessments used by the United States Geological Survey are based on deposit models. These assessments consist of three parts: (1) selecting appropriate deposit models and delineating on maps areas permissive for each type of deposit; (2) constructing a grade-tonnage model for each deposit model; and (3) estimating the number of undiscovered deposits of each type. In this article, I focus on the estimation of undiscovered deposits using two methods: the deposit density method and the target counting method.In the deposit density method, estimates are made by analogy with well-explored areas that are geologically similar to the study area and that contain a known density of deposits per unit area. The deposit density method is useful for regions where there is little or no data. This method was used to estimate undiscovered low-sulfide gold-quartz vein deposits in Venezuela.Estimates can also be made by counting targets such as mineral occurrences, geophysical or geochemical anomalies, or exploration plays and by assigning to each target a probability that it represents an undiscovered deposit that is a member of the grade-tonnage distribution. This method is useful in areas where detailed geological, geophysical, geochemical, and mineral occurrence data exist. Using this method, porphyry copper-gold deposits were estimated in Puerto Rico.     

Oil- and gas-resource assessment in certain South American Basins—An application of ARDS (Ver. 5.0) to complex exploration and discovery histories

The modified Arps-Roberts Discovery Process Modeling System [ARDS (Ver. 4.01)] has recently been upgraded [ARDS (Ver. 5.0)] and applied to a wide variety of field discovery and wildcat drilling data with differing characteristics. ARDS is designed to forecast the number and sizes of undiscovered fields in an exploration play or basin by using historical drilling and discovery data. Fields used as input may be grown or ungrown. Two models for field growth—one offshore and the other onshore—have been implemented (Schuenemeyer and Drew, 1996). Uncertainty attributable to field growth is estimated via simulation. This upgrade of ARDS has been designed to handle situations when the data cannot be partitioned into homogeneous regions, but where estimation of the number of remaining oil and gas fields is still meaningful. In this upgrade of ARDS, many restrictions, which include those on the number of fields and wildcat wells required to forecast the size distribution of the oil and gas fields that remain to be discovered in an exploration play, a basin, or other target area, have been removed. In addition, flexibility has been gained by reforming the criteria for convergence of the model. In all, 32 basins and subbasins in South America were examined, 18 of which had sufficient data to be amenable to forecasting the field-size distribution of undiscovered oil and gas resources directly by using the Petroconsultants Inc. (1993) field discovery and wildcat drilling data. Overall, ARDS (Ver. 5.0) performed well in estimating the field-size distribution of undiscovered oil and gas resources in the 18 basins and subbasins. The aggregate volume of undiscovered petroleum resources was characterized by using histograms of the distribution of resources and the following five statistics: the mean, the 80% trimmed mean, and the 10,50 (median), and 90 quantiles. More than 38 billion barrels of oil equivalent (BOE) in fields that contain more than one million BOE individually were forecast as remaining to be discovered. The largest basin, the Campos (Brazil), is forecast to contain nearly 10 billion BOE undiscovered resources. The East Venezuela Basin (excluding the Furrial Trend) is forecast to contain about 8 billion BOE; the Austral-Magallanes Basin (Argentina and Chile), about 7 billion BOE; and the Napo (Colombia and Ecuador) and the Neuquen (Argentina) Basins, between 3 billion and 4 billion BOE. A subset of these basins that illustrate the increased flexibility of ARDS are discussed.     

Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential,Baguio District,Philippines

An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the unknown gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere.     

A Modified Weights-of-Evidence Method for Mineral Potential Prediction Based on Structural Equation Modeling

Structural equation modeling (SEM) was applied here to modify the ordinary weights-of-evidence (WofE) method for calculating posterior probability to improve conditional independence (CI) in the application of this method mineral potential prediction. The new method attempts to reduce the effect of CI by defining new binary patterns with an optimum combination of cutoff values of patterns. The solution is calculated through SEM, and the goodness of fit between evidence and mineral deposit occurrences is evaluated by a specified target function. The main difference between the new WofE and ordinary WofE is that evidence in the new method maintains a balance between the significance for mineral potential prediction and CI, rather than the significance for mineral prediction only as in ordinary WofE. A case study of prediction of potential for hydrothermal Au mineral deposits in Nova Scotia, Canada, is discussed here. The results indicate that the new method performs better than the ordinary WofE.

     

The “Three-Component” Digital Prospecting Method: A New Approach for Mineral Resource Quantitative Prediction and Assessment

Stimulated by the exceeding progress of information technology, the development of mineral exploration has entered a new period of digitization and quantification. The “three components” approach of mineral prediction is suggested as a new approach to the “digital mineral prospecting,” which is based on the geoanomaly analysis, directed by the research on the diversity of mineralization and on the spectrum of mineral deposits. Close combination of these three aspects of quantitative study makes a new starting point to the digital prospecting. In this paper, the basic theories of the “three components” approach of mineral prediction are discussed. In addition, based on the new achievements in the studies on the prediction and assessment of solid minerals and gas–oil resources, we have centered our discussion on the thought of analysis of geoanomaly evolution and on the “5P” method for approaching the target area in the “three components” approach of mineral prediction.     

A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.     

Determination of Realistic and Statistical Value of the Information Gathered from Exploratory Drilling

Unlike other branches of geosciences, exploratory drilling has not been investigated within the framework of an information system; so, the expression “value of exploratory drilling information” (despite its common usage) is vague. This article presents a model for the evaluation of value of the information gathered from exploratory drilling after studying different mineral exploration and exploratory drilling systems within the framework of an “information system.” Although this model does not present the economic value of information, it is a suitable tool for comparing different drilling patterns. The model was verified on the basis of drilling data for the Gol-Gohar XIIA anomaly.     

Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.     

One-level prediction—A numerical method for estimating undiscovered metal endowment

One-level prediction has been developed as a numerical method for estimating undiscovered metal endowment within large areas. The method is based on a presumed relationship between a numerical measure of geologic favorability and the spatial distribution of metal endowment. Metal endowment within an unexplored area for which the favorability measure is greater than a favorability threshold level is estimated to be proportional to the area of that unexplored portion. The constant of proportionality is the ratio of the discovered endowment found within a suitably chosen control region, which has been explored, to the area of that explored region. In addition to the estimate of undiscovered endowment, a measure of the error of the estimate is also calculated. One-level prediction has been used to estimate the undiscovered uranium endowment in the San Juan basin, New Mexico, U.S.A. A subroutine to perform the necessary calculations is included.     

A Methodology for the Assessment of Unconventional (Continuous) Resources with an Application to the Greater Natural Buttes Gas Field,Utah

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.     

Assessment Methodology and Results From the Canadian Gas Potential Committee 2001 Report

Since 1991 volunteers from the Canadian Gas Potential Committee (CGPC) have conducted assessments of undiscovered gas potential in Canada. Reports were published in 1997 and 2001. The 2001 CGPC report assessed all established and some conceptual exploration plays in Canada and incorporated data from about 29,000 discovered gas pools and gas fields. Mainly year-end 1998 data were used in the analysis of 107 established exploration plays. The CGPC assessed gas in place without using economic cut offs. Estimates of nominal marketable gas were made, based on the ratio between gas in place and marketable gas in discovered pools. Only part of the estimated nominal marketable gas actually will be available, primarily because of restrictions on access to exploration and the small size of many accumulations. Most plays were assessed using the Petrimes program where it could be applied. Arps-Roberts assessments were made on plays where too many discovered pools were present to use the Petrimes program. Arps-Roberts assessments were corrected for economic truncation of the discovered pool sample. Several methods for making such corrections were tried and examples of the results are shown and compared with results from Petrimes. In addition to assessments of established plays, 12 conceptual plays, where no discoveries have been made, were assessed using Petrimes subjective methodology. An additional 65 conceptual plays were recognized, discussed, and ranked without making a quantitative assessment. No nominal marketable gas was attributed to conceptual plays because of the high risk of failure in such plays. Nonconventional gas in the form of coalbed methane, gas hydrates, tight gas, and shale gas are discussed, but no nominal marketable gas is attributed to those sources pending successful completion of pilot study projects designed to demonstrate commercially viable production. Conventional gas resources in Canada include 340 Tcf of gas in place in discovered pools and fields and 252 Tcf of undiscovered gas in place. Remaining nominal marketable gas includes 96 Tcf in discovered pools and fields and 138 Tcf of undiscovered nominal marketable gas. The Western Canada Sedimentary Basin holds 61% of the remaining nominal marketable gas. Future discoveries from that area will be mainly in pools smaller than 2.5 Bcf of marketable gas and increasing levels of exploratory drilling will be required to harvest this undiscovered resource. A pragmatic, geologically focussed approach to the assessment of undiscovered gas potential by the CGPC provides a sound basis for future exploration and development planning. Peer reviewed assessment on a play-by-play basis for entire basins provides both detailed play information and the ability to evaluate new exploration results and their impact on overall potential.     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.     

Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration

Index overlay and Boolean logic are two techniques customarily applied for knowledge-driven modeling of prospectivity for mineral deposits, whereby weights of values in evidential maps and weights of every evidence map are assigned based on expert opinion. In the Boolean logic technique for mineral prospectivity modeling (MPM), threshold evidential values for creating binary maps are defined based on expert opinion as well. This practice of assigning weights based on expert opinion involves trial-and-error and introduces bias in evaluating relative importance of both evidential values and individual evidential maps. In this paper, we propose a data-driven index overlay MPM technique whereby weights of individual evidential maps are derived from data. We also propose a data-driven Boolean logic MPM technique, whereby thresholds for creating binary maps are defined based on data. For assigning weights and defining thresholds in these proposed data-driven MPM techniques, we applied a prediction-area plot from which we can estimate the predictive ability of each evidential map with respect to known mineral occurrences, and we use that predictive ability estimate to assign weights to evidential map and to select thresholds for generating binary predictor maps. To demonstrate these procedures, we applied them to an area in the Kerman province in southeast Iran as a MPM case study for porphyry-Cu deposits.     

A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping

In data-driven mineral prospectivity mapping, a statistical model is established to represent the spatial relationship between layers of metallogenic evidence and locations of known mineral deposits, and then, the former are integrated into a mineral prospectivity model using the established model. Establishment of a data-driven mineral prospectivity model can be regarded as a process of searching for the optimal integration of layers of metallogenic evidence in order to maximize the spatial relationship between mineral prospectivity and the locations of known mineral deposits. Mineral prospectivity can be simply defined as the weighted sum of layers of metallogenic evidence. Then, the optimal integration of the layers of evidence can be determined by optimizing weight coefficients of the layers of evidence to maximize the area under the curve (AUC) of the defined model. To this end, a bat algorithm-based model is proposed for data-driven mineral prospectivity mapping. In this model, the AUC of the model is used as the objective function of the bat algorithm, and the ranges of the weight coefficients of layers of evidence are used to define the search space of the bat population, and the optimal weight coefficients are then automatically determined through the iterative search process of the bat algorithm. The bat algorithm-based model was used to map mineral prospectivity in the Helong district, Jilin Province, China. Because of the high performance of the traditional logistic regression model for data-driven mineral prospectivity mapping, it was used as a benchmark model for comparison with the bat algorithm-based model. The result shows that the receiver operating characteristic (ROC) curve of the bat algorithm-based model is coincident with that of the logistic regression model in the ROC space. The AUC of the bat algorithm-based model (0.88) is slightly larger than that of the logistic regression model (0.87). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets optimally delineated by using the bat algorithm-based model and logistic regression model account for 8.10% and 11.24% of the study area, respectively, both of which contain 79% of the known mineral deposits. These results indicate that the performance of the bat algorithm-based model is comparable with that of the logistic regression model in data-driven mineral prospectivity mapping. Therefore, the bat algorithm-based model is a potentially useful high-performance data-driven mineral prospectivity mapping model.

     

Extended weights of evidence modeling for the pseudo-estimation of metal grades

The weights of evidence modeling (WEM) for binary patterns is extended to take account of general categorical variables. The extension makes it possible to use the weights of evidence model in estimating the conditional probability distributions of metal grades. First, the target feature is converted into a set of binary target indicators. Second, the posterior probabilities are estimated for each of the target categories. Third, the estimates are combined to yield the posterior probability distribution of the target feature. Finally, the pseudometal estimates are derived from the probability distribution. The metal grade estimates are prefixed with pseudo, because the estimates are created from indirect evidence (explanatory variables). The pseudo-estimates provide a unique quantitative means to the delineation of exploration targets. This advantage reduces the ambiguities of target selection based solely on probability estimates. In order to use the generalized WEM, continuous geoscience attributes must be converted into categorical variables by means of optimal segmentation based on the target attribute of interest. The segmentation may be viewed as a process of defining evidence of the target feature. The extended weights of evidence model is demonstrated on a case study to select gold targets of Carlin type. The dataset used in the modeling includes apparent resistivity fields, soil geochemical samples, lithological and alteration information, and structural data.     

Combining indicator patterns in weights of evidence modeling for resource evaluation

The weights of evidence model for combining indicator patterns in mineral resource evaluation is briefly explained with emphasis on the effect of undiscovered deposits on the estimation of the weights and posterior probabilities. A group of six statistical tests is proposed for analyzing the interaction of two or three indicator patterns with the point pattern for mineral deposits. A distinction is made between statistics that depend on choice of unit cell size and those that are approximately or completely independent of it. Finally, weights of evidence are compared to regression coefficients obtained by means of the logistic model.     

Recovery Factors of Oil Resources in China

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.