Bluecap: A geospatial model to assess regional economic-viability for mineral resource development

© 2020 Frontier mineral exploration is often exclusively focused on assessing geological potential without consideration for the economic viability of resource development. This strategy may overlook potentially prosperous zones for more geologically-favoured but financially-disadvantageous regions, or conversely, may introduce implicit biases against potential developments without due regard to underlying economies of scale or proximity to infrastructure. Accordingly, in this paper, we introduce a numerical model aimed at identifying economic fairways, i.e. areas permissive to mineral development from an economic perspective. The model, Bluecap, combines large-scale infrastructure and geological datasets to conduct geospatial analysis of the economic-viability of mining operations across Australia. We provide a detailed description of the inputs and assumptions that underlie the cost models employed in Bluecap, outlining the methods used to evaluate mining, processing, administrative and infrastructure expenses. We also describe the databases used by the model to evaluate available infrastructure, transportation distances and depth of cover. Finally, we present examples that demonstrate the use of the Bluecap model on regions around Mount Isa and the Murray Basin to verify its ability to evaluate commercially feasible mineral prospects. While the immediate utility of this model stands to benefit mineral explorers, its ability to map mineral economic fairways also provides an objective, evidence base to underpin government decision making with respect to position of new infrastructure and consideration of competing land use claims

Schedule Optimization To Accelerate Offshore Oil Projects While Maximizing Net Present Value in the Presence of Simultaneous Operations, Weather Delays, and Resource Limitations

Summary Cost and schedule overruns are endemic problems for offshore oil projects. This can be partly attributed to weather delays, resource limitations, and scheduling risks. The problem is further compounded because of the large number of interdependent activities, such as drilling and platform installation, typically involved in the buildup period of oilfield development. As a result, there is a pressing need to find robust project planning and scheduling models that consider these interacting components and associated risks in offshore oil projects. This study considers three techniques to optimize offshore oil project schedules while accounting for the impact of numerous field activities and potential delay factors; these are mixed-integer linear programming (MILP), single-objective genetic algorithms (SOGAs), and nondominated sorting genetic algorithms (NSGA-II). The study compares the performance of each using a model that integrates field planning with scheduling while accounting for weather delays, resource limitations, and simultaneous operations (SIMOPS; i.e., the ability to conduct more than one activity at once). The first two techniques (MILP and SOGA) optimize the oilfield schedule based on a single objective, which is to maximize net present value (NPV) or minimize project time. However, the maximum NPV schedule may result in a longer project time, whereas the shortest project time may result in a lower NPV. Therefore, the third method using NSGA-II finds Pareto-optimal schedules that balance these competing objectives. Four case studies are provided to compare the MILP and SOGA approaches with the suggested multiobjective NSGA-II.</jats:p

Design of an uncontaminated textile CFRP specimen optimised for both mechanical testing and X-ray microtomography

The aim of this paper is to develop a novel specimen configuration optimised for developing and validating structure-property relationships for textile carbon fibre reinforced polymers (CFRPs). The specimen is designed to be imaged non-destructively using X-ray Microtomography (μCT), but is also optimised for in- and ex-situ mechanical testing. The investigation bridges a gap in current research where modified/enhanced (i.e. contaminated) CFRPs are often used to obtain suitable reconstructions to analyse. This paper looks at identifying the textile architecture of composites at the meso-level without the use of contrast enhancement agents (i.e. uncontaminated) and then proposes the optimum specimen size and scanning parameters to achieve successful reconstructions of the materials system. It was found that the Histogram of Oriented Gradients (HOG) gave the best segmentation outcome when the specimen was sized to fit at least two voxels within a fibre width. In addition to this prepping the specimen to include a cast epoxy jacket prevented CT artefacts during reconstruction. The application of these results will assist researchers in better understanding the evolution of microcracks and damage in textile composites while enabling physics based multiscale modelling approaches to be validated with realistic textile architectures

Extending a Gray Lattice Boltzmann Model for Simulating Fluid Flow in Multi-Scale Porous Media

Abstract A gray lattice Boltzmann model has previously been developed by the authors of this article to simulate fluid flow in porous media that contain both resolved pores and grains as well as aggregates of unresolved smaller pores and grains. In this model, a single parameter is introduced to prescribe the amount of fluid to be bounced back at each aggregate cell. This model has been shown to recover Darcy-Brinkman flow but with effective viscosity and permeability correlated through the model parameter. In this paper, we prove that the model parameter relates to the fraction of the solid phase of a sub-pore system for a specific set of bounce-back conditions. We introduce an additional parameter to the model, and this enables flow simulation in which cases with variable effective viscosity and permeability can be specified by selecting the two parameters independently. We verify and validate the model for layered channel cases and mathematically analyze fluid momentum and energy losses for the single- and two-parameter models to explain the roles of the parameters in their conservation. We introduce a strategy to upgrade our model to an isotropic version. We discuss the fundamental differences between our model and the Brinkman body-force LBM scheme