Richard Sinding-Larsen

Uncertainty in fractal dimension estimated from power spectra and variograms

The reliability of using fractal dimension (D) as a quantitative parameter to describe geological variables is dependent mainly on the accuracy of estimated D values from observed data. Two widely used methods for the estimation of fractal dimensions are based on fitting a fractal model to experimental variograms or power-spectra on a log-log plot. The purpose of this paper is to study the uncertainty in the fractal dimension estimated by these two methods. The results indicate that both spectrum and variogram methods result in biased estimates of the D value. Fractal dimension calculated by these two methods for the same data will be different unless the bias is properly corrected. The spectral method results in overestimated D values. The variogram method has a critical fractal dimension, below which overestimation occurs and above which underestimation occurs. On the bases of 36,000 simulated realizations we propose empirical formulae to correct for biases in the spectral and variogram estimated fractal dimension. Pitfalls in estimating fractal dimension from data contaminated by white noise or data having several fractal components have been identified and illustrated by simulated examples.

Image Filtering by Factorial Kriging—Sensitivity analysis and application to Gloria side-scan sonar images

Factorial Kriging (FK) is a data- dependent spatial filtering method that can be used to remove both independent and correlated noise on geological images as well as to enhance lineaments for subsequent geological interpretation. The spatial variability of signal, noise, and lineaments, characterized by a variogram model, have been used explicitly in calculating FK filter coefficients that are equivalent to the kriging weighting coefficients. This is in contrast to the conventional spatial filtering method by predefined, data-independent filters, such as Gaussian and Sobel filters. The geostatistically optimal FK filter coefficients, however, do not guarantee an optimal filtering effect, if filter geometry (size and shape) are not properly selected. The selection of filter geometry has been investigated by examining the sensitivity of the FK filter coefficients to changes in filter size as well as variogram characteristics, such as nugget effect, type, range of influence, and anisotropy. The efficiency of data-dependent FK filtering relative to data-independent spatial filters has been evaluated through simulated stochastic images by two examples. In the first example, both FK and data-independent filters are used to remove white noise in simulated images. FK filtering results in a less blurring effect than the data-independent fillers, even for a filter size as large as 9 × 9. In the second example, FK and data-independent filters are compared relative to the extraction of lineaments and components showing anisotropic variability. It was determined that square windows of the filter mask are effective only for removing Isotropie components or white noise. A nonsquare windows must be used if anisotropic components are to be filtered out. FK filtering for lineament enhancement is shown to be resistant to image noise, whereas data-independent filters are sensitive to the presence of noise. We also have applied the FK filtering to the GLORIA side-scan sonar image from the Gulf of Mexico, illustrating that FK is superior to the data-independent filters in removing noise and enhancing lineaments. The case study also demonstrate that variogram analysis and FK filtering can be used for large images if a spectral analysis and optimal filter design in the frequency domain is prohibitive because of a large memory requirement.

Building Bayesian networks from basin-modelling scenarios for improved geological decision making

Basin models are used to gain insights about a petroleum system, and to simulate geological processes required to form oil and gas accumulations. The focus of such simulations is usually on charge and timing-related issues, although uncertainty analysis about a wider range of parameters is becoming more common. Bayesian networks (BNs) are useful for decision making in geological prospect analysis and exploration. In this paper we propose a framework for merging these two methodologies: by doing so, we explicitly account for dependencies between the geological elements. The probabilistic description of the BN is trained by using multiple scenarios of Basin and Petroleum Systems Modelling (BPSM). A range of different input parameters are used for total organic content, heat flow, porosity and faulting to span a full categorical design for the BPSM scenarios. Given the consistent BN for trap, reservoir and source attributes, we demonstrate important decision-making applications, such as evidence propagation and the value of information. Supplementary material: Tables and figures of analyses and data are available at: www.geolsoc.org.uk/SUP18607.

Non-Renewable Resource Issues

1. Introduction 2. Stretching the Availability of Non-Renewable Resources 3. Raw Materials Initiative: A Contribution to the European Minerals Policy Framework 4. Certified Trading Chains in Mineral Production -- A Way to Improve Responsibility in Mining 5. Is Depletion Likely to Create Significant Scarcities of Future Petroleum Resources? 6. Coal: An Energy Source for Future World Needs 7. Uranium and Thorium: The Extreme Diversity of the Resources of the Worlds Energy Minerals 8. Evaluating Supply Risk Patterns and Supply and Demand Trends for Mineral Raw Materials: Assessment of the Zinc Market 9. Issues and Challenges in Life Cycle Assessment in the Minerals and Metals Sector: A Chance to Improve Raw Materials Efficiency 10. Secondary Raw Material Sources for Precious and Special Metals 11. The Principal Rare Earth Elements Deposits of the United States -- A Summary of Domestic Deposits and a Global Perpective 12. Discovery and Sustainability Index

Application of discovery process models in estimating petroleum resources at the play level in China

Discovery process modeling has gained wide acceptance in the Chinese exploration community. In recent years, a variety of discovery process models have been applied to the prediction of undiscovered petroleum resources at the play level in sedimentary basins in China. However, challenging problems have been encountered, particularly when one method alone has been applied to small plays in nonmarine sedimentary basins or in plays with an unusual order of discovery wells. This paper presents results gotten by using the lognormal discovery process model of the Geological Survey of Canada and the geoanchored method for three petroleum plays in basins with different geologic settings. Although the predicted shapes of the parentsize distributions which use these two models, were not always similar, the expected values of the total resources and the number of fields (pools) to be discovered are comparable. The combined use of two discovery process models in the same play compensates for the weaknesses in one method compared with the other and vice versa. Thus, more reliable estimates are the result.

Estimating number and field size distribution in frontier sedimentary basins using a Pareto model

Estimating the number of deposits likely to be found and their size distribution is important in exploration planning. In a frontier basin the geologic information for conducting a resource assessment is mostly limited to the regional level. Some methods, such as the play analysis approach (Crovelli and Balay, 1986) and the conceptual play model (Lee and Wang, 1983), can be used in conceptual plays or frontier basins, but these methods require the knowledge of the number of prospects, pool data characterizing the conditional field size distribution, and information documenting the exploration risk. For unexplored sedimentary basins, such as those in southern Africa, there are neither sufficient data covering reservoir volumetric parameters nor a number of mapped prospects that can be used to conduct such an assessment. In such a case a volumetric method using geologic analogy is applicable, by which a point estimate of hydrocarbon potential can be estimated, but this estimate provides no information on the likely size distribution. As a complement to the volumetric method, we discuss how to use the empirical Pareto law for estimating the number of fields and their size distribution in such a frontier region.

Estimating Petroleum Resources Using Geo-Anchored Method—A Sensitivity Study

The Geo-anchored method, based on a moment-type estimator, has been developed for estimating parent population properties from a successive sample of discoveries. By substituting the expectation of the waiting time z(n+1) of the (n + 1)th discovery to occurrence for an unknown parameter λ in the anchored method, the Geo-anchored method allows estimation of inclusion probabilities directly from observed data, thus eliminating the need for a priori selection of a value of N, R, or some other feature of the parent population. Because direct estimation of N and R requires an ordered sample, the Geo-anchored method is more sensitive to the data-generating process than the anchored method. This paper presents a sensitivity study on the Geo-anchored method. The test is based on simulated discovery sequences with different assumptions regarding discovery efficiency, exploration maturity, and the shape of the parent field size distribution. As a reference for comparison, estimates from the Horvitz–Thompson estimator also are presented.

Weighted characteristic analysis of spatially dependent mineral deposit data

There are four concepts involved with the methodology used in this analysis: classical second derivative surfaces, boolean representation of the surfaces, determination of weighted model characteristics, and multiple variable regional appraisal.

Non-renewable Resource Issues: Geoscientific and Societal Challenges: An Introduction

This chapter discusses non-renewable resource issues: the raw materials boom of the first decade of the third millennium, availability of a resource (geoscientific characteristics), society’s dependence on the resource (economic, social and environmental challenges), the gradual dematerialization of advanced economies and the possibility of finding alternatives (whether the resource can be substituted or recycled). Assessing important resources against these issues determine their “criticality” and the risk for supply interruptions of e.g. rare-earths and other raw materials needed for industrial products. ‘Peaks’ in the production of natural resources can be driven by demand or supply. However, fundamental and important differences between a peak in the production of oil and peaks in the production of minerals have been exemplified. Concerning the future availability of natural resources the fixed stock paradigm is contrasted with the opportunity cost paradigm that takes into account the inherent dynamics of market forces and changing technology. Regardless of paradigm increasing resource efficiency through human ingenuity and creativity both in the upstream and downstream sector is shown to be vital if mankind is to successfully advance on the road to a sustainable economy based upon transparency and “good governance” that has to be mirrored on the companies’ side by corporate social responsibility. The societal aspect is however, only one side of the natural resources coin, the other side being the geological and technical availability.

Mapping oil seeps on the sea floor by Gloria side-scan sonar images—A case study from the northern Gulf of Mexico

An oil seep site in the northern Gulf of Mexico is characterized by high backscattering levels on the GLORIA (Geological Long-Range Inclined Asdic) side-scan sonar images against a background of low backscattering. The high backscattering from the oil-seep area are most likely caused by a combination of small-scale roughness and porosity reduction due to the precipitation of CaCO3 formed during biodegradation of the oil-seep. Geostatistical methods have been applied to analyze side-scan images from both oil seep and nonseep areas. The results show that GLORIA images from oil seep areas can be distinguished from nonseep areas in terms of local histograms, variograms, and textural patterns. Pixels from seep and nonseep areas cluster into distinct groups on a textural feature plot. GLORIA side-scan images could be used as a reconnaissance tool to delineate oil and gas seep sites on the seafloor and thereby, reduce the dry-hole risk of petroleum exploration in deep-water frontier areas.