To meet the global hydrocarbon energy demand, it is imperative either to enhance the production from existing fields by applying innovative engineering solutions or discovering new field /resource areas. Both of these options are investigated by petroleum engineers intensively to tackle the challenges of meeting the ever-increasing demand. Meeting the energy demand as, like any other developing country, Jordan is facing a formidable challenge and requires exploration for conventional and unconventional hydrocarbon resources. As Jordan has a long exploration history for conventional reservoirs, Unconventional resource exploration and production seems to be the way to find new energy sources. Different exploration wells were drilled to evaluate the hydrocarbon potential. This research work is focusing on an experimental investigation to evaluate Jordanian hydrocarbon potential as well as to provide recommendations for future exploration activities in shale resources. The Evaluations were performed through comprehensive laboratory experiments that include measurements of Total Organic Content, Grain density, Pore Size Distribution, Specific Surface Area (BET), Mineralogy, Thermogravimetry Analysis, and Rock-Eval pyrolysis. The petrophysical properties (TOC, grain density, pore size distribution) of Jordanian shale (nine different wells) are investigated. The TOC and grain density are in an inversely proportional relationship. The TOC results show a gradual increment with the depth. All the samples have higher porosity dominated by macro pores. Fourteen (14) samples were selected primarily based on TOC (above 1.5%) for further analysis. The specific surface area results show a proportional relationship with the TOC content. Considering the petrophysical properties and mineralogy, these Jordanian shales broadly can be considered as high porosity clay and mudstone type of shale. Thermogravimetry analysis (TG/DTG) results indicate quantitative information related to organic and inorganic matter. Detection of thermos-reactive minerals, especially clay, carbonate, muscovite, pyrite is possible due to the combination of TG/DTG/DSC. The samples are examined under three different procedures which includes different heating programs. The oxidizing and inert atmospheric conditions (procedure i & ii) have the same heating program whereas procedure iii (inert atmospheric condition) has a heating program similar to the Rock-Eval pyrolysis program. The results of these samples show the complex nature of shale as well as organic matter by reacting in different stages (two or, three stages). Depending of the maturity of organic matter, the reaction occurring temperature range varies. Maximum oxidization reaction peaks happen between 479°C to 502°C. The maximum pyrolysis reaction peaks between 498°C to 521°C. Compared with complex heating (procedure iii) and rock Eval pyrolysis, S2 results indicate a high amount of inorganic compounds. Considering TGA reaction peaks and rock Eval pyrolysis results, these Jordanian shales indicate immature with low hydrocarbon generation potential. The Jordanian shale samples are analyzed by using Rock-Eval pyrolysis. Analysis results are used to interpret petroleum potential in rocks. The most important information includes organic matter types (also connected with the depositional settings), organic matter thermal maturity, and the remaining hydrocarbon generation potential in the current form. The organic geochemical analysis results indicate mostly poor to no source rock potential except JF2-760 samples. The hydrogen index (HI) and oxygen index (OI) result suggests that type iii kerogen and type iii/ iv kerogen are most likely from terrestrial and varied settings origin. The low hydrogen, as well as, low S2 value indicate very little hydrocarbon generation potential. Similarly, The Tmax and PI data indicate immature to early mature source rock status and low conversion scenario. Furthermore, the supercritical CO2 is injected into the samples, which is similar to gas flooding experiments to understand the recovery process. Hydrocarbon recovery or, CO2-shale interaction is determined by comparing three different properties (TOC, SSA, and TGA) pre-and-post supercritical CO2 injection. Supercritical CO2 injection in immature shale shows very limited property changes (TOC, SSA, and TGA) to the samples. However, in presence of hydrocarbon the pre-and post-injection property changes TOC, TGA, and SSA (BET) are noticeable enough to conclude HC recovery. Although in the case of immature shale with no hydrocarbon potential the kerogen or bitumen extraction has not been detected, which can be significant in the case of greenhouse gas storage, especially CCUS. This could reduce the risk of Organic Matter (OM) migration possibility in case immature shale formation is present in a suitable geological location.

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Mining will remain indispensable for the foreseeable future. For millennia, our society has been exploring and exploiting mineral deposits. Consequently, most of the easily exploitable high-grade deposits, which were of primary interest given their obvious technical and economic advantages, have already been depleted. For the future, the mining sector will have to efficiently produce metals and minerals from low-grade orebodies with complex mineralogical and microstructural properties -- these are generally referred to as complex orebodies. The exploitation of such complex orebodies carries significant technical risks. However, these risks may be reduced by applying modelling tools that are reliable and robust. In a broad sense, modelling techniques are already applied to estimate the resources and reserves contained in a deposit, and to evaluate the potential recovery (i.e., behaviour in comminution and separation processes) of these materials. This thesis focusses on the modelling of recovery processes, more specifically mineral separation processes, suited to complex ores. Despite recent developments in the fields of process mineralogy and geometallurgy, current mineral separation modelling methods do not fully incorporate the available information on ore complexity. While it is well known that the mineralogical and microstructural properties of individual particles control their process behaviour, currently widely applied modelling methods consider only distributions of bulk particle properties, which oftentimes require much simplification of the particle data available. Moreover, many of the methods used in industrial plant design and process modelling are based on the chemical composition of the samples, which is only a proxy for the mineralogical composition of the ores. A modelling method for mineral separation processes suited to complex ores should be particle-based, taking into consideration all quantifiable particle properties, and capable of estimating uncertainties. Moreover, to achieve a method generalizable to diverse mineral separation units (e.g., magnetic separation or flotation) with minimal human bias, strategies to independently weight the importance of different particle properties for the process(es) under investigation should be incorporated. This dissertation introduces a novel particle-based separation modelling method which fulfills these requirements. The core of the method consists of a least absolute shrinkage and selection operator-regularized (multinomial) logistic regression model trained with a balanced particle dataset. The required particle data are collected with scanning electron microscopy-based automated mineralogy systems. Ultimately, the method can quantify the recovery probability of individual particles, with minimal human input, considering the joint influence of particle shape, size, and modal and surface compositions, for any separation process. Three different case studies were modelled successfully using this new method, without the need for case-specific modifications: 1) the industrial recovery of pyrochlore from a carbonatite deposit with three froth flotation and one magnetic separation units, 2) the laboratory-scale magnetic separation of a complex skarn ore, and 3) the laboratory-scale separation of apatite from a sedimentary ore rich in carbonate minerals by flotation. Moreover, the generalization potential of the method was tested by predicting the process outcome of samples which had not been used in the model training phase, but came from the same geometallurgical domain of a specific ore deposit. In each of these cases, the method obtained high predictive accuracy. In addition to its predictive power, the new particle-based separation modelling method provides detailed insights into the influence of specific particle properties on processing behaviour. To name a couple, the influence of size on the recovery of different carbonate minerals by flotation in an industrial operation; and a comparison to traditional methodologies demonstrated the limitation of only considering particle liberation in process mineralogy studies -- the associated minerals should be evaluated, too. Finally, the potential application of the method to minimize the volume of test work required in metallurgical tests was showcased with a complex ore. The approach developed here provides a foundation for future developments, which can be used to optimize mineral separation processes based on particle properties. The opportunity exists to develop a similar approach to model the comminution of single particles and ultimately allow for the full prediction of the recovery potential of complex ores.:1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 State-of-the-art in particle-based separation models . . . . . . . . . . . 11 1.4 Moving forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.1 Particle data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Mathematical tools required for the particle-based separation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.3 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.5 Structure of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2 The method and its application to industrial operations 23 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.1 Assumptions and limitations . . . . . . . . . . . . . . . . . . . . 26 2.2.2 Data structure and required pre-treatment . . . . . . . . . . . . 27 2.2.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.1 Artificial test cases . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.2 Real case study . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4 Discussion and final considerations . . . . . . . . . . . . . . . . . . . . 39 3 The robustness of the method towards compositional variations of new feed samples 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2 Generalization potential of current Particle-based Separation Model (PSM) methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3.2 Dry magnetic separation tests . . . . . . . . . . . . . . . . . . . 53 3.3.3 Sample characterization . . . . . . . . . . . . . . . . . . . . . . 53 3.3.4 Particle-based separation models . . . . . . . . . . . . . . . . . 54 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4 Flotation kinetics of individual particles 67 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2.2 Cumulative recovery probability . . . . . . . . . . . . . . . . . . 72 4.2.3 Particle-based kinetic flotation model . . . . . . . . . . . . . . . 74 4.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.1 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.4 Discussion and final thoughts . . . . . . . . . . . . . . . . . . . . . . . 80 5 Conclusions and outlook 85 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Bibliography 89