CHALLENGES WITH FOREIGN DATA FORMATS & GEOLOGICAL CONFIDENCE

Transcription

1 CHALLENGES WITH FOREIGN DATA FORMATS & GEOLOGICAL CONFIDENCE Some Case Study Examples from Russia and Central Asia Grant van Heerden, Pr.Sci.Nat. Nick Ryan, CP(Geo)

2 PRESENTATION OVERVIEW The Concept of Foreign Data Basic Definitions Global Code Recognition Use of Foreign Data What is Geological Confidence? How is this determined? Language Barriers, Classification Systems, Data Formats What are the Impacts on Classification, Estimation, and Reporting? Case Study Examples Russian Federation

3 FOREIGN DATA CONCEPTS What constitutes Foreign Data? Not a term defined in any Code Does not refer to data from another country used in estimates for a local company i.e. Foreign Estimates An ASX-listed Chinese company with properties in Australia Chinese geologists operating on the Australian assets Their work will invariably be in Chinese language using Chinese notation and formats An English language Australian consultancy will see this data as Foreign Data

4 FOREIGN DATA CONCEPTS A foreign company (based in Russia) requests an Australian-based consultancy to prepare a report detailing the Coal Resource estimates for its Russian Asset Russian data is transferred to Australia Russian language, notation and formats This is Foreign Data The request is for a Public Report (Capital Raising) Often referred to by others as a JORC Report

5 GLOBAL RECOGNITION OF THE JORC CODE The JORC Code is seen globally as the standard for Mineral Resource and Ore Reserve Reporting The term JORC Report or JORC Statement is often used Also, we often read JORC Compliant A misnomer is JORC Estimates or JORC Compliant Estimates We see more international clients asking for JORC Reports and JORC Statements

6 USE OF FOREIGN DATA Almost every JORC Report contains some form of Resource Statement Almost every Resource Statement requires some form of Data Review Foreign Data will therefore need to be translated and reformatted in order to be reviewed The real meaning (truth) can often be Lost in Translation

7 GEOLOGICAL KNOWLEDGE & CONFIDENCE JORC Figure 1 The Competent Person s assessment of Geological Confidence directly impacts Resource Classification If the Foreign Data is poorly or incompletely translated and reformatted, confidence MUST be low Resource Downgrading Foreign Data could actually be very good!

8 GEOLOGICAL KNOWLEDGE & CONFIDENCE Incomplete and inaccurate translation can also lead to inappropriate Resource Classification Unjustified Resource Upgrading Translation of Foreign Language to English 1 st language English Russian Translator? 1 st language Russian English Translator? translate.google.com? MS Word s Translation Function?

9 GEOLOGICAL KNOWLEDGE & CONFIDENCE Knowledge is gained through Reading and Understanding According to the task for the execution of the geological part of Feasibility Study of final mining parameters for calculation of cocking coals at ANON deposit of CERTAIN coal basin approved by the General Director of COMPANY it is identified to execute calculation of reserves for the following variants of parameters: the minimum seam thickness of the seam of simple and complex construction is 0,5, 0,6, 0,7, 0,8 and 0,9 m; the maximum ash content based on 100% inclusion of intraformational interlayers in intersection - 30, 35 and 40 %.

10 GEOLOGICAL KNOWLEDGE & CONFIDENCE Another example The deposit was explored with the help of core holes. On the lots of initial development according to the grid х m (category А), on the lots adjacent to the lots corresponding to the grid х m (category В), on the main part of the deposit area under the grid х for transfer of reserves of category С 2 into category С 1. In general, the grid of higher category was received by concentrating by 2 times the grid of the lower category. The following correlation of categories was received: А 11 %, В 24 %, С 1 65 %. The accepted grid fits the existing grid accepted for preliminary exploration.

11 GEOLOGICAL KNOWLEDGE & CONFIDENCE Yet another example Coal-bearing rock-mass is complicated by two plicative structures A syncline B anticline which take the central part of the area. In the western part of the deposit the decline of the series is to the east and north-east at о in the direction of A syncline. A syncline has the wavelength of 2-3 km. In the ore of the fold the depth of COAL seam amounts to 850 m (hole 761), actual elevation of the seam floor is up to 130 m (hole 764). The wavelength declines to the center at о. The fold stretches throughout the whole of the deposit to the southeast. B anticline is located in the north-eastern part of A syncline. Its a brachiform fold with a flat dome and low angle, not complicated by smaller plicative displacements. Strike axis is eastern south-eastern. The hinge is plunged in the same direction at 3-5 о. The fold extends for about 5 km, it width is from 2 to 3 km. The rock declines at 7-10 о on the eastern wave, on the western wave from о to 30 о. To the north-east of the anticline, on the right bank of the river the coalbearing thickness lies monoclinally, without any complications with the angle of decline of 7-10 о.

12 GEOLOGICAL KNOWLEDGE & CONFIDENCE Once Data and Information have been translated, we need to understand the format of data presentation (notation) This is particularly relevant to Coal Quality No single standard for data presentation and notation Coal Classification Systems are different from country to country Location Specific Foreign Data

13 COAL CLASSIFICATION AS : Classification and Coding System for Australian Coals REVCAS higher rank coals REVMAS lower rank coals R = mean maximum Reflectance of vitrinite E = specific Energy (dry ash free, daf) V = Volatile matter (daf) C = Crucible swelling number M = bed Moisture A = Ash (dry, d) S = total Sulphur (d)

14 COAL CLASSIFICATION Traditional Names (AS ) Anthracite = volatile matter < 8% (daf) Semi Anthracite = volatile matter % (daf) Bituminous Coal = volatile matter 14% and GSE MJ/kg (ash free moist, afm) Or MJ/kg (afm) where CSN 1 Sub-bituminous Coal = GSE MJ/kg (afm) Or MJ/kg (afm) where CSN ½ Brown Coal = GSE < MJ/kg

15 COAL CLASSIFICATION Russian Classification 17 Coal Marks (primary groups) One for Low Rank Coal 15 for Medium Rank Coals One for High Rank Coals 27 Sub-groups within the 17 Primary Groups 3 in the Low Rank Group 21 in the Medium Rank Group 3 in the High Rank Group Gas, Long Flame, Coking Fat, Gas Fat

16 COAL CLASSIFICATION Chinese Classification

17 COAL QUALITY DATA FORMATS Based on the ACARP CoalLog Log Sheet Developed for ease of loading into SQL DBMS

18 COAL QUALITY DATA FORMATS Russian Data Capture Lost in Translation!

19 COAL QUALITY DATA FORMATS Typical Russian Format The ideal would be to properly translate each field s heading, load into and SQL DBMS, and then export to translated table formats

20 COAL QUALITY DATA FORMATS

21 EXPLORATION BOREHOLE DATABASES A collection of data captured into a range of spreadsheets is NOT a database! Most true database systems will export to a series of CSV files Facilitates data transfer and movement between different DBMS s May receive MS Access Database! Worst Case Scenario Digital scans of paper logs and data sheets!

22 LITHOLOGICAL DATA THE SCAN

23 LITHOLOGICAL DATA THE SCAN

24 LITHOLOGICAL DATA THE SPREADSHEET

25 BOREHOLE DATA - ACCESS

26 VALIDATE AND VERIFY CONFIDENCE BOOST In terms of Public Reporting, and in fact just best practice, some level of QA/QC measure should be applied Where possible, attempt to verify accurate transcription from original hardcopy scans through the spreadsheet to the Access Database This is a trying, tiring, and fiddly exercise! It must be done

27 IMPACTS ON CLASSIFICATION, ESTIMATION, AND REPORTING The Competent Person signing off on the Coal Resources is expected to: Have a reasonable to high degree of understanding with respect to the geological nature of the Coal Deposit (regional and local controls on deposit formation and coal quality) Understand the exploration methodology and technique to adequately assess the resultant lithological and laboratory data Apply this knowledge to ensure effective geological modelling of the deposit

28 IMPACTS ON CLASSIFICATION, ESTIMATION, AND REPORTING Poor and incomplete data translations, with a poor understanding of the various classification systems, reporting formats, and notation methodologies, lead to poorly defined Coal Resources (classification, tonnage, and grade) Garbage In = Garbage Out? Sometimes the INPUTS are good but the translation CREATES garbage!

29 GEOLOGICAL DATABASE BUILD SUMMARY A total of 1,048 boreholes were processed through a validation exercise to be incorporated into the final geological database (MS ACCESS & VULCAN ISIS databases) Data imported included the following: Collar data Lithological data (original field logging data) Downhole survey (verticality) data Mechanical properties data Cementing data Borehole diameter data Consolidated register of all the above data

30 GEOLOGICAL DATABASE BUILD SUMMARY All supplied boreholes were categorised according to the types of structural or lithological information they contained Lithology and geophysics A high degree of confidence in seams that had been depth corrected on the 1:100 scale and a moderate degree of confidence in the lithological data derived purely from field data Geophysics only A high degree of confidence in seams that had been interpreted from resistivity, gamma, gamma-gamma and caliper geophysical traces A default rock unit coding (rock code: XX) was inserted between seam picks to form a full lithological dataset for these geophysics only boreholes Lithology only A low degree of confidence in the lithological data presented in these boreholes as no apparent depth measurements were present

31 THE RESERVE EVALUATION PLAN

32 GEOLOGICAL DATABASE BUILD SUMMARY Corrections were made to the data during compilation and modelling iterations (structural and quality models) Sample and analytical electronic data not able to be verified on the Reserve Evaluation Plans i.e. samples not reflected on the Reserve Evaluation Plans for the seam it was associated with Correction of seam floor depth information noted on the Reserve Evaluation Plans by cross verification of the Reserve Evaluation Plans Relative Level of the seam floor against borehole collar Relative Level Sampled thicknesses (either intra-seam partings and coal or coal alone) not summing to the rock unit thicknesses as indicated on the Reserve Evaluation Plans

33 FINAL RESULT A comprehensive 3D geological model from which Coal Resources were adequately defined, classified and estimated. Due to certain inconsistencies with data and failure to verify instances, no Measured Coal Resources were defined A detailed list of recommendations and process improvements have been suggested

34 CLOSURE Thanks for your attention!