Geological Variables Research Articles

Amplitude-Preserving Imaging Condition for Scattering-Based RTM in Acoustic VTI Media

Conventional seismic imaging methods rely on reflected waves to obtain the interfaces of geologic variations and the normal-incidence reflectivity at the interfaces. However, the scattering phenomena affect the imaging quality, especially in anisotropic media. Most of the current techniques which neglect the scattering effects are insufficient to acquire accurate images. To correct such scattering effects and reduce the image artifacts, we have derived a new, scattering-based, amplitude-preserving imaging condition with anisotropic background assumption in acoustic vertical transverse isotropic (VTI) media. In addition, we present a correlation-type representation for this imaging condition to avoid the problem of being divided by small numbers during reverse-time migration (RTM). Based on this proposed imaging condition, the amplitude of imaging result has the specific physical meaning of the velocity perturbation, and can be retrieved by RTM accurately. Numerical experiments on synthetic data show that our inverse-scattering imaging condition can produce more accurate amplitudes on images than cross-correlation imaging condition.

PetroChron Antarctica: A Geological Database for Interdisciplinary Use

AbstractWe present PetroChron Antarctica, a new relational database including petrological, geochemical and geochronological data sets along with computed rock properties from geological samples across Antarctica. The database contains whole‐rock geochemistry with major/trace element and isotope analyses, geochronology from multiple isotopic systems and minerals for given samples, as well as an internally consistent rock classification based on chemical analysis and derived rock properties (i.e., chemical indices, density, p‐velocity, and heat production). A broad range of meta‐information such as geographic location, petrology, mineralogy, age statistics and significance are also included and can be used to filter and assess the quality of the data. Currently, the database contains 11,559 entries representing 10,056 unique samples with varying amounts of geochemical and geochronological data. The distribution of rock types is dominated by mafic (36%) and felsic (33%) compositions, followed by intermediate (22%) and ultramafic (9%) compositions. Maps of age distribution and isotopic composition highlight major episodes of tectonic and thermal activity that define well known crustal heterogeneities across the continent, with the oldest rocks preserved in East Antarctica and more juvenile lithosphere characterizing West Antarctica. PetroChron Antarctica allows spatial and temporal variations in geology to be explored at the continental scale and integrated with other Earth‐cryosphere‐biosphere‐ocean data sets. As such, it provides a powerful resource ready for diverse applications including plate tectonic reconstructions, geological/geophysical maps, geothermal heat flow models, lithospheric and glacial isostasy, geomorphology, ice sheet reconstructions, biodiversity evolution, and oceanography.

Evaluation of transfer learning in data-driven methods in the assessment of unconventional resources

In a novel application of data-driven analytics to the assessment of unconventional oil and gas resources, we present a Transfer Learning framework based on a combination of unsupervised and supervised machine learning techniques on a relatively large dataset from one of the main unconventional plays in the United States. To improve the generalizability of our work, instead of focusing on building one model with the highest predictive accuracy, we focus on quantifying the changes in predicted productivity of multiple sub-basin models that are trained on data from different sub-basins in the same shale play. We present the main steps of developing a data-driven solution in the unconventional oil and gas domain and discuss the importance of taking into account the domain knowledge of petroleum engineering and geology during the process. In an unsupervised clustering problem, we show the impact of clustering variable choice guided by domain expertise. We investigate the variation of model's accuracy when trained and tested on different parts of the unconventional play. The proposed Transfer Learning framework allows quantification of the change in predictive performance of the model of one part of the unconventional play as the amount of training data taken from another part is varied. We show how a careful selection of data types can balance between three objectives: reduce overfitting of the model to training data, augment model transferability across a very large shale play, and enhance accuracy in a part or sub-basin of the play. We show that selecting the clusters based on geologic variables such as average depth of the formation and thermal maturity of the source rock instead of geographical coordinates of the wells significantly increases the predictive performance of the models. In addition, we demonstrate the increase in model accuracy after adding production data to our predictors. We also discuss the limitations associated with accessing production data in a real-world Transfer Learning workflow. The variable importance ranking interpretation of monthly production rates also supports the significance of production data. We used statistical analysis of large samples of decline curves to find optimum duration of production history for training. To address the hyperparameter fine-tuning for our machine learning algorithms, we identified optimal ranges of Random Forest hyperparameters to achieve lowest test-to-train error ratio. Finally, we demonstrate a successful application of the proposed workflow on a real dataset from the Eagle Ford basin in South Texas in a few examples of improved Transfer Learning under the domain experts' guidance. Our findings show that by using models that have been trained on a large group of wells in a mature field in a transfer learning framework we can achieve better predictive performance in a greenfield with very few wells.

Impacts of Soil Properties, Topography, and Environmental Features on Soil Water Holding Capacities (SWHCs) and Their Interrelationships

Soil water holding capacities (SWHCs) are among the most important factors for understanding the water cycle in forested catchments because they control available plant water that supports evapotranspiration. The direct determination of SWHCs, however, is time consuming and expensive, so many pedotransfer functions (PTFs) and digital soil mapping (DSM) models have been developed for predicting SWHCs. Thus, it is important to select the correct soil properties, topographies, and environmental features when developing a prediction model, as well as to understand the interrelationships among variables. In this study, we collected soil samples at 971 forest sites and developed PTF and DSM models for predicting three kinds of SWHCs: saturated water content (θS) and water content at pF1.8 and pF2.7 (θ1.8 and θ2.7). Important explanatory variables for SWHC prediction were selected from two variable importance analyses. Correlation matrix and sensitivity analysis based on the developed models showed that, as the matric suction changed, the soil physical and chemical properties that influence the SWHCs changed, i.e., soil structure rather than soil particle distribution at θS, coarse soil particles at θ1.8, and finer soil particle at θ2.7. In addition, organic matter had a considerable influence on all SWHCs. Among the topographic features, elevation was the most influential, and it was closely related to the geological variability of bedrock and soil properties. Aspect was highly related to vegetation, confirming that it was an important variable for DSM modeling. Information about important variables and their interrelationship can be used to strengthen PTFs and DSM models for future research.

Geoelectrical Survey for Evaluation of Groundwater Potential Within the Basaltic Terrain of Chikotra River Basin, Maharashtra (India)

Geoelectrical data was acquired using Wenner array over 23 sites with constant electrode separation of 70 m over Chikotra Basin, Dist. Kolhapur, Maharashtra (India). The spatial variation maps of resistivity at depths from 2 to 70 m were plotted using Inverse Distance Weighted (IDW) technique for interpolation in ArcGIS 10.5 to obtain a comprehensive subsurface hydrogeological representation of the study area. High resistivity (>140 Ωm) up to 20m depth, indicative of massive basalts is deciphered towards the NE part of the study area, while the NW sector reveal low resistive (up to 40 Ωm) feature at shallow depths due to fractured basalts, thus conducive for groundwater exploration. Alluvium deposits and columnar jointed basalts in the central part depicts as EW trending conductive (< 30 Ωm) feature suggesting prospective groundwater zone. Low resistivity (6-50 Ωm) from shallow to deeper depths (up to 70m), in the southern region can be identified as potential aquifer system. Longitudinal geoelectric cross-sections are generated over four profiles to identify the lateral and vertical variation in geology and groundwater potential zones. The western and central part of the northern profile (A-A') is highly resistive with resistivity of the order of 80-140 Ωm constituting compact basalts and thus devoid of water. Low resistive zone (30 Ωm) in the eastern part suggests groundwater at shallow depths. Low resistivity zones ranging from 10-50 Ωm is observed at different depth levels over the central profile (B-B') which can be tapped for groundwater exploitation. Several sites over profiles C-C' and the southern-most D-D' suggest promising aquifer zones. Because defining prospective groundwater zones in hard rock terrain is difficult, it’s crucial to look into a river basin’s hydrogeological arrangement early on in the planning process.

Some Observations on Phytoplankton Community Structure, Dynamics and Their Relationship to Water Quality in Five Santiago Island Reservoirs, Cape Verde

Reservoirs provide valuable services to human beings, especially in arid, semi-arid, and Mediterranean regions affected by water scarcity. The present effort aims to study the environmental descriptors of variation and the main factors influencing phytoplankton composition, structure, and diversity in five reservoirs in Santiago Island, Cape Verde, a region affected by water availability. Five campaigns took place from 2016 to 2020 to sample phytoplankton and measure environmental variables according to standard analytical methodologies. Environmental results (17 water physicochemical variables, air temperature, and precipitation) revealed that reservoirs differ in the geological influence variables. The high levels of P and N in water seem to be related to Land Use/Land Cover and are responsible for water-quality degradation. Cyanobacteria dominated the phytoplankton community and posed high risk levels, especially considering that the identified taxa are potential producers of different toxins. Taxa responsible for this dominance were not the same in all reservoirs, emphasizing the dominant role of local habitat factors on community composition and diversity. Overall, the results reveal the importance of defining integrated management plans/strategies for the set of five studied reservoirs, since the processes influencing variation in the phytoplankton community are temporal-scale dependent, with similar biogeographic patterns.

Is selenium beneficial or detrimental to earthworm? Growth and metabolism responses of Eisenia Fetida to Na2SeO3 exposure

Se unevenly distributed in soils due to variations of geology and anthropogenic input, which results in different effects on earthworms. The effects of Se were characterized by analyzing the growth and metabolism responses of earthworms after exposure to three different concentrations of Na2SeO3. The results showed that except the possible growth promotion at 5 mg/kg, low and middle-level exposure to Na2SeO3 (0.3–10 mg/kg) did not significantly affect the growth of earthworms. While a significant inhibition effect on growth was observed in the high-level exposure group (30–70 mg/kg). There was an inflection point for Se performing promotion to inhibition effects on earthworm growth. To investigate the metabolic response of earthworms, a novel HPLC-ESI-MS (High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry) method was used to determine sensitive biomarkers. Selenium exposure significantly altered the metabolism of seven essential amino acids, namely tyrosine, leucine, phenylalanine, valine, alanine, glycine, and lysine, and two selenoamino acids, namely selenomethionine and methylselenocysteine. The overall metabolism level of earthworms was not affected at low exposure concentrations, but was affected at medium and high exposure concentrations. The metabolic pathways that integrated the selenocompound metabolism and the tricarboxylic acid cycle from the perspective of energy supply and demand were affected by Na2SeO3 exposure. The derived reactive oxygen species at high exposure concentrations were probably the reason for the growth inhibition effect of Se on earthworms. This study provides biochemical insights into the effects of Na2SeO3 on earthworms and suggests that an Se concentration of about 2.3 mg/kg is appropriate for soil organism health.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Incorporation of Geometallurgical Input into Gold Mining System Simulation to Control Cyanide Consumption

The Alhué deposit (Melipilla, Chile) is an example of a hydrothermal Au-Ag-Zn(-Pb) vein system hosted within the volcanic rocks of the Las Chilcas Formation. The dominant ore minerals observed are free electrum and native gold associated with silver sulfosalts, and with magnetite and base metal sulphides, including pyrite +/− sphalerite-galena-chalcopyrite. The alteration assemblage in the veins mainly consists of quartz epidote-chlorite-actinolite with lesser smectite, amphibole, and calcite-kaolinite-garnet. Mineralized veins also contain variable amounts of base metals, some of which (e.g., copper and iron) are considered harmful to the extraction of precious metals. Iron and especially copper minerals are known cyanide consumers; ore type classification schemes that do not consider the detrimental effects of such mineralogy or process elements can ultimately result in metal losses from ore feed restrictions, as well as spikes in cyanide consumption and higher operating costs. Mineralogical and geological variation can nonetheless be managed by applying alternating modes of operation as demonstrated in this paper; the decision to switch between modes is governed by current and forecasted stockpile levels feeding into the process. Simulations based on experiences at the Alhué deposit are provided that demonstrate the importance of standardized operational modes and their potential impact on cyanide consumption control.

Identification of Slip Surfaces Using the Geoelectric Imaging Method in the Kalirejo Area, Kokap District, Yogyakarta, Indonesia

Landslides are a significant threat to the environment, infrastructure, and human activity, especially in mountainous and hilly areas. It is, therefore, important to accurately identify the social movements that trigger these processes. The resistivity method can investigate subsurface geological variations, including the potential for landslides. This research was conducted to investigate the subsurface structures in the Kalirejo Village area, Kokap District, Kulon Progo Regency, Yogyakarta regarding identifying the sliding location. This investigation includes five lines of tomographic geoelectric measurements. The results show that the slips surface is at a depth of about 5-10 m with a high resistivity value and the landslide material is weathered soil, and the slip surface is andesite. The resistivity of andesite in range 668-1600 Ωm. The landslide material's thickness is around 5-8 m with resistivity in the range 4,01-22.1 Ωm. Landslide material is water-saturated soil.

The Wasatch Environmental Observatory: A mountain to urban research network in the semi‐arid westernUS

AbstractThe 2085 km2Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.

Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis

ABSTRACT The subsurface geological cross-section is indispensable before design and construction of a geotechnical structure can commence. The development of geological cross-sections often requires significant manual efforts for simplification of stratigraphic boundaries. For example, straight lines are commonly used to connect stratum boundaries at adjacent boreholes, and soil layers with small thicknesses are often ignored. Such a simplification heavily relies on practitioners’ experience and may induce great uncertainties in the developed geological cross-sections and subsequent geotechnical analysis and design (e.g. slope stability analysis). In this study, a Bayesian supervised machine learning method, multiple-point statistics, is adopted to automatically generate high-resolution subsurface geological cross-section with proper incorporation of prior geological knowledge and all details observed from limited borehole logs. Locations and number of boreholes are automatically determined with due consideration of slope failure mechanism. The proposed method is demonstrated using an illustrative example. It is shown that the proposed method successfully captures geological details and local stratigraphic variations within a slope and quantifies the associated interpretation uncertainties. The geological details and local stratigraphic variations have great effects on the slope failure mechanism. There is a risk of overestimating overall slope stability if an over-simplified geological model is adopted for slope design.

Evaluation of geohazards in the Cape Girardeau area using LiDAR and GIS, Southeast Missouri, USA

The New Madrid Seismic Zone (NMSZ) has historically recorded some of the largest intensity earthquakes in North America, including significant earth movements that resulted in about 2000 felt earthquakes during 1811–1812. The region continues to experience mass wasting due to earth movements. The aim of this study is to understand the influence of geologic variables on mass wasting processes in the greater Cape Girardeau area, which forms the commercial center of Missouri's fertile "Bootheel" region. Earth movement susceptibility was evaluated in Cape Girardeau and Bollinger counties and portions of Stoddard and Scott counties by mapping potential landslide features on topographic maps, field verification of such features, and geospatial analysis of recent LiDAR imagery. In order to evaluate the changes in surface morphology, slope inclination, hillshade aspect, hydrology, lithology, faults, precipitation, seismicity, sinkholes, and geohydrology were considered. Geographically weighted analysis of the geomorphologic variables identified zones of relative risk. In addition, data were evaluated for oil and gas pipelines, bridges, utilities, and open pit mines associated with mass wasting on public and economic infrastructure. The results suggest that anthropogenic changes commonly associated with urban development impact land use, runoff, infiltration, and slope failures, while sustained precipitation and seismic ground shaking tend to trigger landslides. The scale of mass wasting in the study area was robust, varying from as small as one-half hectare to as much as 67 km2. The vulnerability of the population in susceptible areas tends to increase at the lower elevations and on alluvial flood plains. Thus, hazard susceptibility evaluation can be useful in both community planning as well as emergency preparedness.

EMI Prospection at Tabbel El-Guesh (Saqqara Plateau, Egypt)

– Specific EMI instrumental drift compensation processing. – High-resolution EMI in-phase mapping highlights buried archaeological structures. – EMI quadrature component mostly delineates local geological variability.

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Crystallographic and crystallochemical controls on oxygen isotope analysis of hematite by SIMS

Previous work on oxygen isotopic compositions of hematite samples by secondary ion mass spectrometry (SIMS) has yielded large oxygen isotopic variability (>3 ‰) within individual samples. Deconvolving instrumental effects from geological variation is of vital importance in correctly interpreting in situ SIMS δ18O isotope compositions recorded in hematite. Here, we demonstrate that the excess scatter in δ18O values acquired with SIMS results from a combination of true sample variability and crystallographically-induced instrument mass fractionation. The natural and crystallographically-induced isotopic variability of hematite was investigated by a combination of SIMS, laser fluorination, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) elemental mapping, and electron back-scattered diffraction (EBSD). We used the Sensitive High Resolution Ion Microprobe – Stable Isotopes (SHRIMP-SI) on a museum-quality hematite specimen from the Quadrilátero Ferrífero, Minas Gerais, Brazil, and orientation-induced analytical fractionation was found to contribute up to 5 ‰ to the observed SIMS δ18O isotopic scatter. Laser ablation ICP-MS elemental mapping of crystallographically uniform single hematite crystals revealed distinct chemical zonation patterns that indicate several discrete stages of crystal growth. Crystal zoning additionally coincides with abrupt shifts in δ18O ranging ca. 11 ‰ for a single crystallographic orientation. These results highlight an intimate link between crystal growth mechanism and isotopic evolution of the mineralizing fluid, showing the need for high spatial resolution analysis before the isotopic composition of hematite can be used to infer conditions of mineral precipitation and solution compositions. The results also demonstrate that an understanding of both crystallographic orientation and crystallochemical variability are needed to obviate problems previously encountered in oxygen isotope analysis of hematite by ion microprobe.

Diagnostic assessment of reservoir response to fracturing: a case study from Hydraulic Fracturing Test Site (HFTS) in Midland Basin

This paper outlines a data collection and diagnostics case study involving multiple horizontal shale wells. We look at well production profiles using rate transient analysis, differences in near wellbore complexity, geologic variations within the area of interest, as well as compositional differences in the rocks based on cores obtained from within the stimulated reservoir. The Hydraulic Fracturing Test Site is a multi-well experiment involving stimulation of unconventional shale wells in the southeastern Midland portion of the Permian Basin. The targeted formations include both the upper as well as the middle Wolfcamp formations, also referred alternatively as Wolfcamp A and Wolfcamp B. Data integration and analysis shared in this paper help us understand the various geologic controls impacting well productivity, particularly the wide variance observed between the Wolfcamp A and Wolfcamp B formations. Rate transient analysis indicates similar system permeabilities for stimulated wells. However, we observe higher effective fracture half-lengths for upper Wolfcamp wells. Using observations from 3D seismic interpretations (such as pad scale faults) as well as petrophysical and image log data, we highlight the substantial differences in stimulation as we move along the well laterals from the heel toward the toe sections. These differences are further reconciled with observations from zones with high data density at the core locations through stimulated rock, as well as independent data such as microseismic emissions. At the test site, Wolfcamp A was found to be relatively quartz rich with significant heterogeneity whereas Wolfcamp B is richer in clay and organic content. This impacts the geomechanical characteristics of the rock mass with much higher natural fracture density in the shallower interval. Thus, the fracture growth is more uniform in the deeper interval and more heterogeneous with branching likely in upper interval. Increased complexity also leads to consistently better productivity from the wells in the shallower interval as demonstrated from RTA results. This case study is unique because it provides valuable insights from actual sampling of the stimulated zones in hydraulically fractured wells and helps understand impact of various factors that contribute toward variability in well production. The findings from this study provides insights into need for optimization of completion designs in the various Wolfcamp landing zones, such as optimization of cluster or fracture spacing in various Wolfcamp intervals. In addition, it provides a useful template for data collection and research direction in future field test sites of similar nature in unconventional reservoirs.

Joint learning for spatial context-based seismic inversion of multiple data sets for improved generalizability and robustness

Seismic inversion plays a very useful role in the detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geologic variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We have developed a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject the spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple data sets are simultaneously used for training, sharing beneficial information among each of the sets. This results in improvement in the generalization performance on all data sets. We have developed a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 data sets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average mean squared error of 0.0476, the lowest among all other methodologies.

Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels and technologies still unable to fill the gap, research and development of unconventional petroleum resources have accelerated markedly in the past 20 years. With some of the largest bitumen deposits in the world, Canada has an active oil mining and refining industry. Bitumen deposits, also called oil sands, are formed in complex geological environments and subject to a host of syn- and post-depositional processes. As a result, some ores are heterogeneous, at both individual reservoir and regional scales, which poses significant problems in terms of extractive processing. Moreover, with increased environmental awareness and enhanced governmental regulations and industry best practices, it is critical for oil sands producers to improve process efficiencies across the spectrum. Discrete event simulation (DES) is a computational paradigm to develop dynamic digital twins, including the interactions of critical variables and processes. In the case of mining systems, the digital twin includes aspects of geological uncertainty. The resulting simulations include alternate operational modes that are characterized by separate operational policies and tactics. The current DES framework has been customized to integrate predictive modelling data, generated via partial least squares (PLS) regression, in order to evaluate system-wide response to geological uncertainty. Sample computations that are based on data from Canada’s oil sands are presented, showing the framework to be a powerful tool to assess and attenuate operational risk factors in the extractive processing of bitumen deposits. Specifically, this work addresses blending control strategies prior to bitumen extraction and provides a pathway to incorporate geological variation into decision-making processes throughout the value chain.

Application of geostatistical hierarchical clustering for geochemical population identification in Bondar Hanza copper porphyry deposit

Several machine learning approaches have been developed for the identification of geochemical populations. In these approaches, the geochemical elements are usually the sole quantitative variables used as inputs for geochemical population recognition. This means that the presence of other qualitative variables, such as geological information, is overlooked in the analysis. Hierarchical clustering, as an unsupervised machine learning method, is a common approach for dimensional reduction in the analysis of geochemical data. In this study, an alternative to this technique, known as geostatistical hierarchical clustering (GHC), is applied to identify geochemical populations in 3D in the Bondar Hanza copper porphyry deposit, Iran. In this paradigm, the qualitative geological variables can also be incorporated for geochemical population identification, in addition to qualitative geochemical elements. In this study, an innovative solution is presented to tune the weighting parameters of each variable in GHC, based on the associations that the clusters (i.e., geochemical populations) should have with the geological information. The results are compared with k-means and number–size fractal/multifractal (N–S) methods. As a result, GHC showed better agreement with alterations, rock types, and mineralization zones in this deposit. Finally, some important instructions are provided for further mineral exploration.