Search Documents

Talk some about your background. Mining, particularly coal, runs in the family. I come from a mining family, one generation re-moved. Both of my grandfathers worked in the coal mines. My mother?s father had worked as a coal miner in the South Hills area of Pittsburgh after coming to America from Croatia. My father?s father (who died of black lung long before I was born) had been born in America and worked as a mine foreman and superintendent in mines just outside the Pittsburgh area. My father often talked about when, as a child, he accompanied his father underground on Sundays to check the mine pumps. While I never expected to go into mining, I often read some of my grandfather?s books on mining technology that my father had kept. I started college at Penn State and drifted into mining engineering as my third declared major. I was finally realizing the importance of a career choice. I?ve never regretted that decision. One of my children, Mark, has followed me into mining engineering, much to my surprise and pride. Evidently, he had been listening to the many conversations I had with friends and colleagues in the industry and never considered any other career path. He has since married another mining engineer, Kray Davis Luxbacher, so we truly have a mining family and can talk mining when we get together. My other children are in more mundane lines of work and rapidly tire of the mining discussions. I?ve worked for operating companies: Pittsburgh Coal Co. (then a division of Consolidation Coal Co., and a company I believe both of my grandfathers had worked for), Island Creek Coal Co., and a research organization: Occidental Research Corp. I am now with Glenn Springs Holdings Inc., dealing with legacy issues (primarily environmental) related to Occidental?s previous mining operations (coal and minerals). As such, I have a broad view of the industry and the critical changes that are shaping our industry.

Jan 1, 2008

By R. J. Pruett

The Georgia kaolin district is the world’s largest producer of kaolin. Georgia production tripled from 2.87 Mt (3.17 million st) in 1968 to8.35 Mt (9.2 million st) in 1998. Development of the Georgia kaolin industry can be traced to the identification of the mineral resource, the adoption of innovative process technologies and the creation of markets for kaolin. Georgia kaolin is an abundant and high-quality resource that shows a range of advantageous properties. Technical developments in wet processing, delamination and calcination allowed production of high-value products. The use of kaolin pigments in the paper industry provided a market that absorbed the rapid growth in kaolin production.

Jan 1, 2000

By John Montgomery

This paper discusses the major changes to the Commissioners Rules and Regulations, Chapter 120-3-10 “Rules and Regulations for Explosives and Blasting Agents”; how the changes came about, and a summary of the status of the blasting industry in Georgia.

Jan 1, 2003

By T N. Little

The author contends that geoscience is presently under-utilised in the rock fragmentation by blasting process. Before we can set standards in this area, the following is required: mine geologists need to gain a better understanding of the blasting process and the blasting team needs to obtain a better appreciation of geology. Both groups need to be informed of what can be done to improve blasting outcomes, why it is necessary and how it can be achieved. By working closely together, both the geoscience toolkits and blasting expertise can be applied and focused on priority blast sensitive issues, with potential to improve mine economics and safety. Finally, and ideally, mine geologists need to take a leadership role in raw rock mass data collection and data management, and invited to take an active role in rock mass related behaviour data collection, integration and presentation.This paper attempts to provide insight into all these prerequisites for setting a new standard for collaboration and mutual learning in this strategic opportunity area. The potential for blasting to leverage downstream processes is highlighted. Blasting objectives and blast design principles are introduced along with the required rock mass related inputs. Some hazardous geological materials and environmental conditions are described, along with how they can affect operational complexity.Four case examples are used to illustrate the need for closer liaison between geology and blasting professionals. The first related to grade control blasting, aims to demonstrate how a blasting geology approach can add significant value. Case example two demonstrates how geological models and bench face analysis can assist with fragment control. The third and fourth case examples involve an overview of two case studies where timely geological and geotechnical knowledge input and intervention into blasting activities may have reduced losses due to blast initiated open pit slope collapses.CITATION:Little, T N, 2017. Geoscience input into blasting operations – let’s set a new standard, in Proceedings Tenth International Mining Geology Conference 2017, pp 375–384 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 20, 2017

By Leach B. G

A geostatistical study was made of diamond drill hole intersections within 18CC stope block at the C.S.A. Mine, Cobar, N.S.W. The stope block is located in 18CC orebody which is a massive sulphide - multiple vein, Copper-Zinc type, lacking clear definition on gangue mineralogy, base-metal assays and total sulphide considerations. Of the variograms constructed for inter- section width and copper, lead and zinc grades and accumulations, only zinc displayed behaviour amenable to model fitting and kriging procedures. Zinc grades were calcu- lated for sub-blocks comprising the total stope block and comparison with equivalent grades determined by traditional methods showed them to be generally less extreme in the highest and lowest grade sub-blocks. The total-block grades derived by the two methods were almost the same. It is suggested that the failure of copper to show variogram continuity might reflect the difficulty in defining the actual 18CC orebody, a proposition which is supported by the poor continuity in the variogram of horizontal width. In the case of lead the poor continuity may be due to the frequent manner of occurrence of galena as coarse grained patches of limited extent. The results of the study, did not provide a convincing reason to depart from the traditi- onal methods of grade estimation at the C.S.A. Mine.

Jan 1, 1979

By Michael J. Friedel

As part of a study aimed at understanding the hydrology of in situ leach mining (ISLM), the Bureau of Mines, in cooperation with the Cyprus Casa Grande Corporation and the University of Arizona, is investigating dynamic transmissivity conditions during ISLM of a copper oxide ore deposit. Spatial and temporal variations in ore zone transmissivity during two years of ISLM operations at the Cyprus Casa Grande mine are evaluated during sixteen intervals of steady-state injection and recovery well operation. Direct inversion of an analytical hydrologic model is used to obtain estimates of transmissivity at 58 well locations during these intervals. Univariate statistical and geostatistical methods are used to describe the temporal and/or spatial relationships that exist between transmissivity and other ISLM parameters, including average well injection pressure, average sulfuric acid concentration in leach solution, and the relative configuration of injection and recovery wells. Average transmissivity during the sixteen steady-state time intervals varies between 6.00 x 10-6 m2/min. (6.46 x 10-5 ft2/min.) and 3.61 x 10-8 m2/ min. (3.89 x 10-7 ft2/min.). A trend toward decreas¬ing transmissivity is evident during two year of ISLM operations. This decreasing trend is found to correlate with a decreasing sulfuric acid concentration in injected leach solution, and suggests increasing geochemical precipitation within the deposit. Geostatistical analyses reveal a dynamic and anisotropic transmissivity structure within the target ore zone, that is dependent upon spatial and temporal changes in injection pressure and well configuration.

Jan 1, 1992

By R. Cao

Geostatistics was initially developed in the mining sector, but has been extended to other geoscience applications, including forestry, environmental science, soil science, and petroleum science and engineering. This paper presents methods, workflows, and pitfalls in using geostatistics for hydrocarbon resource modelling and evaluation. Examples are presented of indicator variogram analysis of categorical variables, lithofacies modelling by sequential indicator simulation and hierarchical workflow, porosity modelling by kriging and stochastic simulation, collocated cokriging for integrating seismic data, and collocated cosimulation for modelling porosity and permeability relationships. These methods together form a systematic approach that can be effectively used for modelling natural resources.

Jan 1, 2014

By Royle A. G

The paper describes a geostatistical structural analysis of an 80 square mile lease of the Athabasca tar sands. Each step of the analysis is presented with the ultimate objective of condensing the principal structural characteristics of the chosen regionalised variables into the opera- tional form of the variogram model. The variogram model is then used to form kriged local and global estimates, with the corresponding estimation errors.

Jan 1, 1977

By C. Özgen Karacan

The formation of the gas emission zone in longwall mining and the potential amount of gas in this zone are factors of local geology and spatial variability within this geology. Therefore, geostatistical methods can be used for modeling and prediction of gas amounts and for assessing its uncertainty in the gas emission zone of longwall mines. This study used core data obtained from 64 exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal layers for the gas emission zone, semivariogram modeling was conducted for different coal seams to define the distribution and continuity of various attributes. Sequential simulations were performed for stochastic assessment of these attributes to calculate gas-in-place (GIP) in each coal seam. GIP calculations for coals of various gas emissions zones were combined and ranked for their cumulative distribution function to find GIP in the caved zone and in the fractured zone at the 5%, 50%, and 95% quantiles. Fifty percent quantile results were later used to isolate a panel from the whole area and create a mosaic representation corresponding to the daily advance rate of the longwall face. This approach helped to estimate the daily emissions of this panel from the caved zone and from the fractured zone. Results showed that gas-in-place in the Pittsburgh coal seam, in the caved zone, and in the fractured zone showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that gas-in- place volumes in the study area may be as high as 12.3 Bscf and as much as 3.5 MMscf per acre of mining.

Jan 1, 2012

Knowledge of true block grades is obtained from a mathematical model of an orebody, simulated with sample grades normally distributed and spatially correlated according io a spherical semi-variogram model.The superiority of kriging to the polygonal method of estimation is demonstrated on this modelled deposit by comparing the true values with the kriged and polygonal estimates obtained from sampling the deposit. Finally, selection of blocks of the deposit on the basis of the estimated values, shows that if kriging is used, what is predicted is largely mined, whilst if the polygonal method is used large biases can occur depending on the position of the cut-off grade.

Jan 1, 1978

During a programme to determine the suitability of the UK site for deep disposal of radioactive wastes, geostatistical methods were used to characterise rock heterogeneity on the basis of seismic and wireline acoustic-impedance data. The data were analysed to yield semi-variogram models that quantified the three-dimensional spatial heterogeneity of acoustic impedance. These models were reconciled with geological interpretations of the area and used to generate simulations of acoustic impedance, with wireline acoustic impedance as conditioning data. The three-dimensional simulated images were compared with actual seismic acoustic impedance. The variography confirmed a vertical zoning effect in the Borrowdale Volcanics, showing that it is caused by preferential clusters of high acoustic impedance values corresponding to regions of relatively low porosity. The method provided realistic three-dimensional models from sparse, one-dimensional wireline data

Jun 19, 1905

By J. A. McLennan

Geological uncertainty is an unavoidable characteristic of all mining projects since only limited information is available from sampling. Mine planning and development decisions with significant economic consequences are made when the geological heterogeneity is inherently uncertain. Geostatistical simulation is preferred over kriging because the methodology provides alternative property realizations that can be combined into a model of geological uncertainty. Geological uncertainty can be transferred into production uncertainty, which can help characterize the economic risk of the project. This paper presents a case study for the Pogo property, being prepared for development near Fairbanks, Alaska. The exact dimensions, grades and production predictions have been modified; however, the methodology is illustrative. The geostatistical methodology consists of (1) assembling the relevant drillhole data and establishing a suitable coordinate system, (2) calculating the target distribution, (3) creating a model of spatial correlation, (4) generating multiple realizations at a small scale and (5) linearly averaging all the realizations to the chosen selective mining unit (SMU) support size. The geostatistical model is then used to calculate production uncertainty.

May 1, 2004

By J. M. Ortiz

Mineral resource evaluation requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Usual practice is to consider the domain boundaries as hard, i.e. data from across the boundaries are disregarded when estimating the grades within a given domain. This practice may hinder the quality of the estimates when a significant spatial correlation of the grades exists across the domain boundaries. In this paper, several geostatistical methodologies to handle soft geological boundaries are compared through a case study on a copper mine in Chile. The estimation methods considered are: ordinary kriging using hard boundaries between geological domains; ordinary kriging omitting the geological boundaries; traditional and standardized ordinary cokriging of the grades assayed in different domains; ordinary kriging within dilated geological domains, that is, incorporating samples from adjacent domains up to a given radius from the boundary of the domain being considered. The estimations are performed using a set of exploration data (drill hole samples) and are validated using a set of production data (blast hole samples collected for grade control). Results indicate that kriging with dilated domains performs better than ordinary kriging using hard boundaries or no boundaries and better than the ordinary cokriging approaches. It therefore appears as a simple alternative to global kriging (without considering the geological domains) and allows accounting for changes in the grade average, dispersion and spatial continuity with the geological characteristics of the deposit, via the use of grade variograms proper to each geological domain. The maximal distance for searching samples from adjacent domains should be chosen according to the spatial correlation of the grades across the domain boundaries, or via a cross-validation at an early stage of the mine or a jack-knife if production data are available. Keywords: Kriging; cokriging; soft boundaries; hard boundaries; geological control; geological domains.

Jan 1, 2006

In the estimation of reserves the known values of the variables of interest from surrounding drill holes are used to compute or estimate the unknown value of the orebody over a given region or at a given point of interest. The classical means of doing this are: 1. The Polygonal Method û where the region is divided into polygons centred on bore holes, each variable is then assumed to have constant value over each polygon. 2. Inverse distance methods, where the variable is assumed to vary linearly with the distance between the known values of the drill holes. 3. Inverse distance squared method û a quadratic variation is assumed for the variables between the bore holes.

Jan 1, 1983

By A. H. Onur, S. Ural

"The Afsin-Elbistan coal deposit with its 3.4 billion metric tons of reserve is the biggest coal basin and one of the most important resources for electrical energy production in Turkey. The coal deposit is located north of Elbistan, Kahramanmaras Province, in southeast Turkey (Fig. 1).The Kislakoy sub-basin of the Afsin-Elbistan basin was selected as the first open cast mine to feed four power station blocks of 340 MW each. The mine is equipped with six identical bucket wheel excavators of the type Sch Rs (2300/5) 32. The annual coal demand of the power station is 18.5 million metric tons. The Elbistan basin encompasses an area of 900 km2, and its centre lies about 1150 m above mean sea level. The sedimentation took place mainly in the upper Pliocene period. The thickness of the coal bed varies from 20 m to 60 m, respectively. The basal part of the coal beds consists of clay, marl and sandy clay. These formations are overlain by gyttja, which may also occur in the form of calcareous ooze, enveloping coal beds. The thickness of the gyttja is up to 35 m (Otto-Gold, 1969). The coal of Afsin-Elbistan is soft coal and the quality has been found to vary considerably throughout the deposit. The thicknesses of the sterile intercalations vary from 0.50 m to 2.2 m and average 1 m in thickness. From the quality control point of view, the coal seam is regarded as upper seam where the gyttja formation occurs as sterile intercalations, and lower seam where the clay formation occurs instead of the gyttja (Fig. 2). The CaO content of upper Kislakoy coal is very high, grinding resistance is low and base to acid ratio of ash, that is, the ratio of acid oxides of metals to base, is greater than one (Ural and Onur, 2001). Contrary to upper Kislakoy coal, CaO content of lower Kislakoy coal is very low, grinding resistance is high and base:acid ratio of ash is smaller than one. Modified Impact Strength Index (MISI) tests are used to determine the grindability of the Elbistan coals (Ural, 1999)."

Jan 1, 2003

By Jennifer Riefenberg

Ground control hazards, coal quality, ore reserve estimation, and pollution modeling seem unrelated topics from most mining perspectives. However, geostatistical methods can be used to characterize each of these, and more topics. Exploratory drill core data, and continued drilling and field measurements, can provide a wealth of information related to each of the above areas and are often severely underutilized. Recent studies have led to the development of the Multiple Parameter Mapping (MPM) technology, which utilizes geostatistics and other numerical modeling methods, to generate a "hazard index" map, often from exploratory drill core data. This mapping has been presented for ground control hazards relating roof quality, floor quality, numerically modelled stresses due to mining geometry, and geologic features. A review of the MPM method, future directions with the MPM, and a discussion of using these and other geostatistical methods to quantify coal quality, ore reserve estimation, and pollutant modeling are presented in this paper.

Jan 1, 1996

By Oy Leuangthong

The McMurray formation in the Athabasca oil sands deposits of Northern Alberta is part of the world?s second largest proven crude oil reserves. The formation is characterized by stratigraphic layers that correspond to three different depositional environments: Marine, Estuarine and Fluvial facies. Resource estimation for oil sands has traditionally relied on polygonal and inverse distance schemes. These techniques are simple and straightforward in practice, but they do not permit reliable uncertainty assessment. This paper describes the modeling of the McMurray formation using modern geostatistical techniques. Geostatistical modeling should be performed within homogeneous geological facies. The methodology for geostatistical simulation within each of the identified facies involves: (1) assessing the most appropriate stratigraphic transformation for optimisation of the correlation structure, (2) determining representative distributions with declustering and debiasing techniques, (3) modeling spatial continuity of the bitumen grade, fines grade, water saturation and other petrophysical variables, (4) performing estimation and cross validation as checks against simulation results, (5) performing simulation for uncertainty quantification of bitumen and fines grade, and (6) model checking of simulation results against the input data and comparisons against the kriged models. These steps are described in detail. The application of these models for uncertainty quantification in support of resource classification is also discussed.

May 1, 2004

By P Landeros, D Benado, J Cornejo, A Pinochet, C Caviedes

In 2005, El Teniente mine began preconditioning the primary rock mass by hydraulic fracturing (HF). The major perceived benefits of this process are a decrease in the magnitude of the expected maximum seismic event and improved cave propagation. In addition, HF has resulted in new information on the propagation pressures of each fracture. Previous studies have attempted to relate propagation pressure to minor principal stress or confining stress.Considering the spatial continuity of propagation pressure and the significant quantity of collected data since the incorporation of the HF technique, minimum requirements for geostatistical modelling have been reached, permitting robust estimates inside the representative volume with acceptable errors (Cornejo, Muñoz and Rojas, 2012; Cornejo and Landeros, 2013). These results are then used to identify zones with higher magnitudes, which are mostly related to more complex geotechnical zones (intrusive and major faults) and caving singularities, making it possible to estimate the spatial extension of these zones.Finally, these results are integrated with other geomechanics aspects to support risk analysis of the mining plan.CITATION:Cornejo, J, Landeros, P, Benado, D, Caviedes, C and Pinochet, A, 2016. Geostatistical modelling of hydraulic fracturing pressures at El Teniente Mine, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 183–190 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

By Raymond G. F

Geostatistics - a tool used by geologists and mine planning engineers to estimate grades and rapidly evaluate alternative mining strategies has reached an advanced stage of development for the silver-lead-zinc orebodies at Isa and Hilton Mines. Geostatistics has been used for over a decade in the massive copper orebodies but the stratiform lead-zinc orebodies are much more complex and provided a completely different set of challenges. Recent increases in computer processing speed and power provided by the Integrated Mine Planning System (IMPS) has aided the development of the geostatistics package. Also the formation of a project team and the subsequent in-house development which has drawn together the expertise of geologists, engineers and system analysts has contributed significantly to the success of the project.The most important feature of any approach to modelling the lead-zinc orebodies is that the kriging search must consider estimate and sample points according to their positions relative to bedding surfaces. By transforming co-ordinates as perpendicular offsets from a plane representing the local strike and dip, it is possible to elongate the ellipsoidal influence to parallel bedding. By interactively modelling surfaces using CAD facilities and then specifying these surfaces as 'hard' or 'soft' boundaries, the geologist has complete control over the kriging process.

Jan 1, 1993

By Leahey T. A

The variability of gold mineralization in the Warrego Gold Pod is described by classical statistical techniques using sample distrib- utions and analysis of variance; and by the use of geostatistical variogram analysis to define the spatial variability and anisotropy of the mineralization. Comparison is made between declared mine production from the Nineteen Stope and a series of ore reserve estimates derived by manual, numerical and disjunctive kriging methods.

Jan 1, 1979