Statistics for Spatial Data.

Statistics and Data Analysis in Geology

Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Introduction to stochastic programming

At temperatures up to about 80 °C, petroleum in subsurface reservoirs is often biologically degraded, over geological timescales, by microorganisms that destroy hydrocarbons and other components to produce altered, denser 'heavy oils'. This temperature threshold for hydrocarbon biodegradation might represent the maximum temperature boundary for life in the deep nutrient-depleted Earth. Most of the world's oil was biodegraded under anaerobic conditions, with methane, a valuable commodity, often being a major by-product, which suggests alternative approaches to recovering the world's vast heavy oil resource that otherwise will remain largely unproduced.

Biological activity in the deep subsurface and the origin of heavy oil

Stochastic mining supply chain optimization: A study of integrated capacity decisions

A new efficient joint simulation framework and application in a multivariable deposit

By estimating ore quality and assuming the distribution of rare earth elements present in a deposit using the total rare earth oxide grade, a mine planner does not have the necessary resolution to assess the geological risk and inform their decisions Thus, it is unlikely that the mine production schedule will maximise the generation of cash flows or meet the expected production targets Suitable representation of a deposits’ spatial variability and uncertain characteristics, coupled with advancements in the field of stochastic mine planning, can offer the tools to develop a mine design that will yield a higher net present value and ensure more reliable ore quality The application herein consists of the joint simulation of 15 rare earth elements in a monazite–bastnasite deposit and the generation of a risk-resilient production schedule that maximises net present value and ensures consistent ore grades and rare earth mineral blends at the processing facility

Risk-resilient mine production schedules with favourable product quality for rare earth element projects

Mining operations can be modelled as a supply chain from the sources of raw material (mines) through various processing streams to the final saleable products sold to customers. Existing research in mine design optimization has predominantly focused on the extraction sequence of materials and neglects the impact that selecting the optimal processing paths has on the economic viability of a mining operation. Additionally, the vast majority of existing models assume that the value of the extracted material must be calculated for each block individually, and neglects the opportunity to blend material together to prolong the life of the resource and generate higher cash flows. One major contributing factor for not modelling such complexities is the difficulty in modelling and optimizing with non-linear relationships that occur in the supply chain’s processing streams. This paper addresses the issue of selecting the optimal block destinations and processing streams throughout a mining supply chain for complex blending operations. This non-linear optimization formulation is solved using a particle swarm optimization algorithm. The k-means clustering technique is used to aggregate the processing decision variables and substantially reduce the size of the problem, leading to more efficient solution times with minimal loss in the quality of the resultant solution. By clustering the decision variables and applying the same decisions over a set of geological simulations, the proposed method also provides complex destination policies for material extracted from a mine under geological uncertainty, somewhat akin to a more advanced cut-off policy. This method is tested at Vale’s Onça Puma nickel laterite deposit, located in Pará, Brazil.

Mining Supply Chain Optimization under Geological Uncertainty

ABSTRACT This paper overviews the simultaneous stochastic optimisation of mining complexes or mineral value chains where raw materials mined from mineral deposits in an area are transformed into a set of sellable products. The supply of materials extracted from available mines represents a major source of uncertainty and technical risk that needs to be managed, along with market demand. An overview of the main concepts, case studies and comparisons show how the approach manages risk and capitalises on synergies between the components of the mining complex and major differences from conventional methods. Results lead to strategic plans with larger amounts of metal produced from the same mineral resources, a substantially improved ability for operations to meet production forecasts, and a significantly higher net present value.

https://www.tandfonline.com/doi/pdf/10.1080/17480930.2022.2065730?needAccess=true

Roussos Dimitrakopoulos

Papers

Stochastic mining supply chain optimization: A study of integrated capacity decisions

A new efficient joint simulation framework and application in a multivariable deposit

Risk-resilient mine production schedules with favourable product quality for rare earth element projects

Mining Supply Chain Optimization under Geological Uncertainty

Simultaneous stochastic optimization of mining complexes - mineral value chains: an overview of concepts, examples and comparisons