Drillhole Spacing Determination with Value of Information

• Author / Creator

  Harding, Benjamin E

• Different quantities of information are available at various stages of the development of a mining project. Consequential decisions are made given the data available at the time. Geological uncertainty due to sparse data presents economic risks. The collection of additional information reduces geological uncertainty leading to a better technical decision and greater value. Subjectivity in the choice of data collection scheme may lead to sub-optimal outcomes. The value of information (VOI) allows a decision-maker to quantify the future value data could provide before collecting it. Evaluating many future configurations over a range of data spacings identifies the optimal given the value metric. The optimal data spacing represents the balance between the cost of uncertainty and the cost of information. A framework for establishing VOI in a mining context is proposed. The application of VOI is particularly useful for advanced mining projects with an established basis to calculate value. A geostatistical resample'' andresimluate'' approach is adopted. The resampling of simulated realizations provides access to virtually any future data configuration. Geological models are simulated conditional to future data and passed through an objective function to optimize a technical design and establish value. The difference in value generated with future information and the current information is the VOI. VOI is a non-linear response to the interaction of geologic scale, engineering scale and technical design parameters, decision-maker risk preferences and economic parameters. The relationship between these parameters and their influence on VOI and optimal sample spacing is addressed. The numerical VOI workflow and considerations for implementation are documented. Alternative methods for evaluating optimal data spacing based on geologic uncertainty measures are addressed, establishing a link between uncertainty and value. A semi-automatic algorithm for partitioning future drillhole configurations into spatially reasonable groups based on data spacing is proposed. The methodology and techniques developed in this thesis are applied to synthetic examples and a case study. The case study encompasses VOI principles, data spacing, engineering design parameters, economic factors, and uncertainty in reserve calculation.

• Subjects / Keywords

  • Value of Information
  • Geostatistics
  • Mining

• Graduation date

  Spring 2021

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-4gvr-sz52

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.