Sequential Simulation Research Articles

A unified cloud-enabled discrete event parallel and distributed simulation architecture

Cloud infrastructure provides rapid resource provision for on-demand computational requirements. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M&S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M&S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M&S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M&S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M&S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95× and 1.84×, respectively.

Effect of permeability heterogeneity on the dissolution process during carbon dioxide sequestration in saline aquifers: two-and three-dimensional structures

The convection–diffusion process of carbon dioxide (CO2) dissolution in a saline reservoir is investigated to shed light on the effects of the permeability heterogeneity. Using sequential Gaussian simulation method, random permeability fields in two and three-dimension (2D and 3D) structures are generated. Quantitative (average amount of the dissolved CO2 and dissolution flux) and qualitative (pattern of the dissolved CO2 and velocity streamlines) measurements are used to investigate the results. A 3D structure shows a slightly higher dissolution flux than a 2D structure in the homogeneous condition. Results in the random permeability fields in 2D indicates an increase in the standard deviation of the permeability nodes enhances the dissolution efficiency, fluctuations in CO2 dissolution flux, separation between the different realizations from the same input parameters, and tendency toward more jagged convective fingers’ shape. Furthermore, the distance between the permeability nodes increases the convective fingers’ dissolution efficiency and jagged structure. The degree of freedom in 3D structures results in a higher chance of escaping from the low permeability zones and reduces the interactions between convective fingers in 3D systems. With the same variance and correlation length between permeability nodes, connectivity between high permeable zones in 3D cases are less than that of 2D cases; therefore, 2D realizations overestimate the dissolution flux of real heterogeneous 3D structures, which should be considered carefully.Article HighlightsCO2 sequestration in two and three dimensional heterogeneous saline aquifers are investigated.3D structures in homogeneous conditions show higher dissolution than 2D structures.2D realizations overestimates the dissolution flux over real heterogeneous 3D reservoirs.

Integrated Geologic Modeling of Fault-Block Reservoir: A Case Study of Ss Oil Field

The Ss oil field is found in the Turpan-Hami Basin’s Taipei Sag’s arc structural belt. This reservoir has a complicated character that has a significant impact on reservoir modeling and production prediction. This is a fault-block reservoir with ultralow permeability and low porosity that is divided by 57 faults. A static model was constructed by Petrel software based on reinterpretation of original log and core data and seismic information so as to clarify the spatial distribution of oil and water in the reservoir and to fit the development history of the later simulated reservoir. The integrated geological modeling approach is described in this work using the Ss reservoir as an example. A 3D structural model was built based on the spatial cutting relationship between the layer model and the fault, and the model’s quality was improved by breakpoint data, which more correctly depicted the structural properties of the research area. The lithofacies model was built within the restrictions of sedimentary facies using the sequential Gaussian simulation (SGS) stochastic modeling approach, which is paired with variogram data analysis to achieve the range value. To obtain the porosity and permeability model, the empirical formula of porosity and permeability, the SGS method, and the variation range value was input into the lithofacies model. It is important to note that the input lithofacies and property models have values of the same range. To gain the water saturation model, the distinct S w function formulas of the S 1 ~ S 4 layer derived from the J S w function were fed into the software. The NTG model was created according to the lower limit of porosity, which is 11%. The merging of detailed reservoir description and simulation led to the establishment of the Ss reservoir geological model. In the plane, the scale of the geological model has reached the meter level and decimeter level in the longitudinal direction. It also offers a framework for optimum reservoir modeling for complex fault-block reservoirs. This method improves the accuracy and precision of the model by reflecting the reservoir’s heterogeneity and the oil-water distribution. It could provide more details for future reservoir research such as fine reservoir simulation.

Simplified A-Diakoptics for Accelerating QSTS Simulations

The spread of distributed energy resources (DERs) across the distribution power system demands complex planning studies based on quasi-static-time-series (QSTS) simulations, requiring a significant amount of computing time to complete, leading planners to look for alternatives to QSTS. Diakoptics based on actors (A-Diakoptics) is a technique for accelerating simulations combining two computing techniques from different engineering fields: diakoptics and the actor model. Diakoptics is a mathematical method for tearing networks, reducing their complexity by using smaller subcircuits that can be solved independently. The actor model is used to coordinate the interaction between these subcircuits and their control actions, given the pervasive inconsistency that can be found when dealing with large-scale models. A-Diakoptics is a technique that simplifies the power flow problem for improving the simulation time performance, leading to faster QSTS simulations. This paper presents a simplified algorithm version of A-Diakoptics for modernizing sequential power simulation tools to use parallel processing. This simplification eliminates critical points found in previous versions of A-Diakoptics, improving the performance of the algorithm and facilitating its implementation to perform QSTS simulations. The performance of the new version of A-Diakoptics is evaluated by its integration into EPRI’s open-source simulator OpenDSS, which uses standard computing architectures and is publicly available.

A Scalable Quantum Key Distribution Network Testbed Using Parallel Discrete-Event Simulation

Quantum key distribution (QKD) has been promoted as a means for secure communications. Although QKD has been widely implemented in many urban fiber networks, the large-scale deployment of QKD remains challenging. Today, researchers extensively conduct simulation-based evaluations for their designs and applications of large-scale QKD networks for cost efficiency. However, the existing discrete-event simulators offer models for QKD hardware and protocols based on sequential event execution, which limits the scale of the experiments. In this work, we explore parallel simulation of QKD networks to address this issue. Our contributions lay in the exploration of QKD network characteristics to be leveraged for parallel simulation as well as the development of a parallel simulation framework for QKD networks. We also investigate three techniques to improve the simulation performance including (1) a ladder queue based event list, (2) memoization for computationally intensive quantum state transformation information, and (3) optimization of the network partition scheme for workload balance. The experimental results show that our parallel simulator is 10 times faster than a sequential simulator when simulating a 128-node QKD network. Our linear-regression-based network partition scheme can further accelerate the simulation experiments up to two times over using a randomized network partition scheme.

Prospect Evaluation of Hydrocarbon Reserves Using 3-D Static Modeling in D-Field Onshore, Niger Delta Basin Area

Qualitative and quantitative approaches are often adopted to characterize known reservoir in any given field. A 3-D static modeling has been used to have full understanding of the field of the reservoir uniqueness of the study area, D-field. The hydrocarbon bearing sounds of the field were modelled using 3-D seismic data of the field, integrating with the well log and checkshot data of the field. The stochastic model approach was adopted to distribute the rock properties (Structural and Petrophysical) into a 3D grid using Sequential Gaussian Simulation which identified fifteen (15) major faults across the reservoirs. Reservoirs R_3000 had average thickness of 123 m, net-to-gross of 65%, porosity of 26%, water saturation of 43%, permeability of 1570.649 mD, based on Rider’s classification the reservoir R_3000 shows a very good porosity and an excellent permeability. These values are satisfactory for economic production. The environments of deposition of the reservoirs based on log motifs are interpreted as distributary channel fill and shoreface of the porosity and permeability of D-Field are within the range of values reported in the Niger Delta. Stochastic volumetric analysis estimated that the reservoir of interest to contain a reserve of averagely 14245.50 MMSTB. Furthermore, the integration of these subsurface data (well log and seismic) has led to simulation of a consistent 3-D static model of the reservoir which very well serves as input into the dynamic simulation model, so that forecasting and other sensitivity analysis can be run to provide the basis for effective reservoir management and development strategy. Keywords: Stochastic model, Qualitative, Quantitative, Gaussian simulation, Static modeling, Volumetric DOI: 10.7176/JEES/12-3-05 Publication date: March 31 st 2022

Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation

Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity.

Towards a sampling protocol for the resource assessment of critical raw materials in tailings storage facilities

Resource estimates are crucial to assess the economic potential of tailings storage facilities (TSF) for re-mining and the extraction of critical raw materials. However, a lack of consensus exists on best practices in sampling for this purpose. This study aims to address this gap by assessing different sampling schemes for the resource classification of TSFs. To do so, one layer of a TSF was sampled with regular and nested grids of varying sizes and additional random holes. Systematic spatial trends in the data were fitted with low-order polynomial functions of the coordinates and the grayscale values of a historical aerial photograph of the same layer of the TSF. Variography was performed on the trend residuals, and exponential or Gaussian variogram models were fitted. Universal Sequential Gaussian Simulation was then used to produce 1000 realisations of the potential ground truth. The optimum sampling strategy was investigated by re-sampling these realisations with varying sample densities and configurations and using geostatistical modelling of the re-sampled data to assess uncertainties on the estimated metal tonnages. Robust estimates of metal tonnages can be achieved at relatively low sampling densities, particularly with regular grids. When historical image information is used, spatial variability is better reproduced, and a lower number of samples is required to reach a certain confidence level. Furthermore, an approach to approximate expected errors on grades/tonnages estimated with a given sampling scheme was developed to assess whether further sampling is required. Overall, the findings of this study have clear and transferrable implications for the best-practice sampling and modelling of TSFs and their critical raw material resources.

Facies modelling for hydrocarbon reservoirs using a combined Markov chain method

To predict the lithofacies distributions for modelling hydrocarbon reservoir heterogeneities, geostatistical tools like the indicator variogram-based methods provide an efficient solution. However, for the sparse datasets, they potentially encompass drawbacks like the ambiguity of variograms and generating unrealistic patterns. Nevertheless, Transition probabilities (TPs)-based techniques using Markov chains (MCs) have a notable potential to overcome these problems. Hence, the facies distributions were predicted in an Iranian hydrocarbon reservoir using three methods including a novel algorithm proposed here (CGCMC). Using generalized coupled Markov Chain (GCMC), sequential indicator simulation (SIS), and a new combination of GCMC with the transition probability Markov Chain (TP/MC) framework, 50 realizations were generated through each method. Then, the models were evaluated regarding the reproduction of their: facies proportions, variograms/TPs and geological soundness. By ensemble probability distributions (EPDs) the spatial uncertainties were represented. Accordingly, CGMC produced more acceptable results quickly and more conveniently with a higher data integration capacity.

Hierarchical Modeling of Carbonate Fault-Controlled Paleokarst Systems: A Case Study of the Ordovician Reservoir in the Tahe Oilfield, Tarim Basin

As a special type of fracture-cavity carbonate reservoir, fault/fracture-controlled paleokarsts are affected by large strike-slip faults with various storage spaces, irregular geometry, and stochastic spatial distribution, which brings new challenges to their 3D quantitative characterization and geological modeling. Based on multi-scale research data, a hierarchical scheme of a fault-controlled paleokarst is established for the Tahe oilfield, and then a hierarchical modeling method for the paleokarst is proposed based on the principles of hierarchical constraint and genetic control. The results show that the fault-controlled paleokarst hierarchy is divided into four levels according to genesis and scale. These are the strike-slip fault impact zone, the fault-controlled external paleokarst envelope, the fault-controlled internal paleokarst architecture, and the internal karst cave fillings. The strike-slip fault impact zone model is established using deterministic data of the main fault, secondary fault, envelope range, and key geological horizons obtained from seismic interpretation. Based on the seismic FL (Fault Likelihood) attribute, the external envelope model of the fault-controlled paleokarst is established using the deterministic method through automatic attribute segmentation and drilling calibration. With the constraint of the external envelope, the internal architectural elements are classified and modeled. A large-scale karst cave model and mesoscale discrete fracture distribution model are established using the deterministic method based on the seismic texture property and seismic ant property truncation. Taking well data as conditional data and seismic-geological probability cubes as constraint data, the sequential indication simulation and object-based marked point process simulation methods are used to obtain the model of the dissolution pores distribution and the model of the discrete small-scale fractures distribution, respectively. Finally, a typical fault-controlled paleokarst reservoir in the TP area of the Tahe oilfield is taken as an example to test the above modeling method; the 3D integrated model developed in this study can reflect the spatial hierarchy of the fault-controlled paleokarst carbonate reservoir.

From petrophysical properties to forward-seismic modeling of facies heterogeneity in the carbonate realm (Majella Massif, central Italy)

Forward modeling is a fundamental support to study the seismic response of reservoirs structure and subsurface architectures. Carbonate reservoirs result in non-univocal seismic response caused by the facies heterogeneity and due to the possible presence of infilling fluids. The carbonate ramp outcropping in the Majella Massif (Central Italy) is an excellent surface analogue of buried heterogeneous structures. It offers the opportunity to directly analyze a carbonate reservoir which clearly shows facies variations and natural hydrocarbon-impregnations allowing to quantify the induced petrophysical changes. In this study, we integrated original field and laboratory measurements with 3D facies modeling to carry out 1D and 2D forward seismic models of a carbonate reservoir following a structured workflow. A careful petrophysical characterization measuring density porosity and seismic wave velocities has been performed in all the sampled facies and then used as input for the 3D velocity model. The “Sequential Gaussian Simulation Co-Kriging” (SGS-CK) results to be the best algorithm to build the seismic velocity model, consequently a low-frequency (40 Hz) synthetic 1D seismogram was carried out simulating facies and hydrocarbon-saturation variations. Thus, a 9 km long synthetic profile from the platform top to the basin, SE-NW oriented, was carried out simulating the outcropping architecture and spatial distribution of the facies.The obtained synthetic seismic outputs are closer to real geophysical surveys with respect to classical forward modeling. Perturbations of the seismic signal derived from the modeled facies heterogeneity without introducing artificial noises made the synthetic results more realistic preserving the horizons architecture. We also quantitatively show that variations in the signal related to the hydrocarbon saturation can result in an increase or decrease in reflectivity depending on the seismic properties of the surrounding layers.The presented results give new insights about reservoir architectures and can be useful to better process as well as to interpret the field seismic data and the resulting seismic sections acquired in carbonate realms.

Facies-Controlled Modeling for Permeability of Tight Gas Reservoir Based on Hydrodynamic and Geophysics Characteristics

Due to poor physical properties and strong heterogeneity of Daniudi tight gasfield (China), traditional methods are not ideal for predicting reservoir permeability. Based on geoscience data mining algorithms and modeling techniques, this parameter is predicted and characterized from a new perspective. The high precision hydraulic unit information was outputted by the BP neural network and the interwell database was predicted by sequential indicator simulation based on the logging and core data, the exponential and power law relationship functions were selected to participate in the construction of the permeability model. The results showed that the facies-controlled modeling system for permeability based on the hydrodynamic characteristics of tight gas reservoirs could effectively improve the accuracy of reservoir prediction, and the logging information in the longitudinal direction and the facies information in the plane were combined by the hydraulic unit.

Posterior probability-based hydraulic unit division and prediction: A case study

Hydraulic units (HUs) with analogous petrophysical and flow characteristics are commonly employed to describe reservoirs. However, their prediction performances would be greatly influenced if the relationship between well-logging parameters and HUs is not well revealed. In this study, the model broke through the dimensionality of the traditional model and expanded the dimensionality of the logging intersection space to three dimensions. Specifically, HUs of cored samples were divided into 5 classes based on flow zone indicators, and the Bayes theorem was used to write a VB program based on the posterior probability to determine the HU class in the 3D cube. Scheme B (RT-2 & AC & SH) exhibited good prediction performances when applied to predict the 30% cored data, with an overall accuracy rate of 90.06%, which exceeded that of the artificial neural network (80.45%). Therefore, scheme B was applied to predict HUs of un-cored wells. Then, sequential instruction simulation was conducted on HUs of inter wells in the Petrel software, based on which well section analysis was performed to identify the relationship between lithology and predicted HUs. HUs of wells with similar production time were dynamically verified based on the perforation thickness data of HUs, which were combined with the production performance data to confirm the distribution rationality of predicted HUs along the well trajectory. When planning infill wells, injection and production wells should be deployed in the same HU class to ensure good connectivity. HU#2 and HU#3 with relatively good quality occupy more than 26% (7.78% + 18.93%) of the total geological space, and they should be the main targets in the future remaining oil exploitation. More attention should be paid in the future to refine the interpretation results of well-logging parameters.

A joint standard-exceeding risk assessment of multiple pollutants based on robust geostatistics with categorical land-use type data: A case study of soil nitrogen and phosphorus

Joint standard-exceeding risk and its spatial uncertainty of soil available nitrogen (AN) and available phosphorus (AP) under the specific constraints are essential for guiding the joint regulation of pollutants but were rarely considered by previous studies. Moreover, traditionally-used spatial simulation models are not only non-robust but also ignoring valuable categorical information (e.g., land-use types), which may hinder the acquisition of high-precision spatial simulation results. This study first established optimally robust semi-variogram estimators to identify the spatial outliers of soil AN and AP in Jintan County, China. Then, robust sequential Gaussian simulation (RSGS) with land-use types (RSGS-LU) was proposed and further compared with RSGS, SGS-LU, and SGS in the spatial simulation accuracy. Last, a joint standard-exceeding probability model under the specific constraints was proposed, and the corresponding high-risk areas were delineated for the joint regulation of soil AN and AP. Results showed that: (i) 23 and 17 spatial outliers were identified for soil AN and AP, respectively; (ii) removing outliers or combining land-use types could improve the spatial simulation accuracy of soil AN and AP; (iii) RSGS-LU generated the highest spatial simulation accuracy for both soil AN and AP; (iv) the area with the joint standard-exceeding (AP > 30 mg kg−1∪ AN > 130 mg kg−1) probability >75% accounted for 9.98% of the county's area; (iv) the area with the joint standard-exceeding (AP > 30 mg kg−1∩ AN > 130 mg kg−1) probability >75% accounted for 2.29% of the county's area. It is concluded that RSGS-LU and joint standard-exceeding probability model under the specific constraints could provide more accurate and flexible spatial decision support for the joint regulation of soil AN and AP at a regional scale. Moreover, the methods recommended in this study also provide valuable tools for the joint standard-exceeding risk assessment of other multiple soil pollutants.

Capacity Analysis of a Passenger Rail Hub Using Integrated Railway and Pedestrian Simulation

As the level of passenger demand in rail transit systems increases, major railway stations in urban centres face serious capacity issues. Both analytical and simulation methods have been used to analyse complex station areas; however, prior efforts have only focused on either train or pedestrian movements with over-simplified assumptions that do not properly capture the impact of their interaction on capacity. This study applies an integrated crowd and transit simulation platform “Nexus” to simultaneously study the impact of pedestrian and train movements on the system performance of a complex railway station. Unlike other methods such as sequential simulation methods, the integrated simulation platform permits linkage between commercial-grade simulators. Instead of treating each simulator separately, this integrated method enables detailed modelling of how the train and crowd dynamic interact at station platforms. Such integration aims to explore the interactive effect on both types of movement and enable performance analysis possible only through this combination. To validate the model, a case study is performed on Toronto’s Union Station. Extensive data were collected, processed and input into railway and pedestrian models constructed using OpenTrack and MassMotion, respectively, and integrated via Nexus. Examining scenarios of increased levels of train and passenger volumes, a 9% drop in on-time performance of train operation is observed, while the level of service experienced by passengers on the platform deteriorates significantly due to crowding. Both length and variation in dwell time due to pedestrian movement are recognized as the main factors of performance deterioration, especially when the system approaches capacity limit. The simulation model produces estimates of the practical track-side capacity of the station and associated platform crowding levels, and helps identify locations where passengers experience severe overcrowding, which are not easily obtainable from mathematical models.

Numerical investigation of the impact of fracture aperture anisotropy on EGS thermal performance

The impact of anisotropy within spatially-varying fracture apertures on heat transfer in the context of Enhanced Geothermal Systems was investigated. The anisotropic fracture aperture distributions studied included samples from laboratory-scale fractures and additional 100 artificial fracture aperture distributions generated using Sequential Gaussian Simulation. Two flow configurations were investigated. The perpendicular flow configuration had a flow perpendicular to the lateral shear offset direction, while the parallel flow configuration had a flow parallel to the lateral shear offset direction. The thermal performance was simulated numerically using a thermohydraulic model with a 50 mm by 50 mm sheared fracture, typical granitic rock properties, and temperature dependence of fluid density and viscosity.The results showed that 70% of the fracture aperture distributions studied gave rise to favorable thermal performance in the perpendicular flow configuration. It was also observed from this study that the flow-wetted surface area had a direct and significant contribution to the amount of heat extracted. In addition, larger volumetric flow through preferential paths also contributed to more heat extraction and could offset surfaces without large flow-wetted surface areas. The results of this study suggest that placing an injector well in the direction perpendicular to shear or slip may result in favorable thermal performance over a parallel flow configuration.Geometric anisotropy was deduced from the joint roughness coefficient (JRC) and variogram modeling parameters of the datasets studied. High geometric anisotropy results in high differences in thermal drawdown between the flow configurations. Combining the difference in geometric anisotropy and difference in JRCs between the flow configuration could aid in identifying if the thermal performance will be favorable in a given flow configuration.

Computational screening of camostat and related compounds against human TMPRSS2: A potential treatment of COVID-19

The global coronavirus pandemic has burdened the human population with mass fatalities and disastrous socio-economic consequences. The frequent occurrence of these new variants has fueled the already prevailing challenge. There is still a necessity for highly effective small molecular agents to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we targeted the human transmembrane surface protease TMPRSS2, which is essential for proteolytic activation of SARS-CoV-2. Camostat is a well-known inhibitor of serine proteases and an effective TMPRSS2 inhibitor. A virtual library of camostat-like compounds was computationally screened against the catalytic site of TMPRSS2. Following a sequential in-depth molecular docking and dynamics simulation, we report the compounds that exhibited promising efficacy against TMPRSS2. The molecular docking and MM/PBSA free energy calculation study indicates these compounds carry excellent binding affinity against TMPRSS2 and found them more effective than camostat. The study will open doors for the effective treatment of coronavirus disease 2019.

Acceleration strategies for large-scale sequential simulations using parallel neighbour search: Non-LVA and LVA scenarios

This paper describes the application of acceleration techniques into existing implementations of Sequential Gaussian Simulation and Sequential Indicator Simulation. These implementations might incorporate Locally Varying Anisotropy (LVA) to capture non-linear features of the underlying physical phenomena. The implementation focuses on a novel parallel neighbour search algorithm, which can be used on both non-LVA and LVA codes. Additionally, parallel shortest path executions and optimized linear algebra libraries are applied with focus on LVA codes. Execution time, speedup and accuracy results are presented. Non-LVA codes are benchmarked using two scenarios with approximately 50 million domain points each. Speedup results of 2× and 4× were obtained on SGS and SISIM respectively, where each scenario is compared against a baseline code published in Peredo et al. (2018). The aggregated contribution to speedup of both works results in 12× and 50× respectively. LVA codes are benchmarked using two scenarios with approximately 1.7 million domain points each. Speedup results of 56× and 1822× were obtained on SGS and SISIM respectively, where each scenario is compared against the original baseline sequential codes.

Direct Sequential Simulation for spherical linear inverse problems

We present a method for obtaining efficient probabilistic solutions to geostatistical and linear inverse problems in spherical geometry. Our Spherical Direct Sequential Simulation (SDSSIM) framework combines information from possibly noisy observations, that provide either point information on the model or are related to the model by a linear averaging kernel, and statistics derived from a-priori information. It generates realizations from marginal posterior probability distributions of model parameters that are not limited to be Gaussian. We avoid the restriction to Cartesian geometry built into many existing geostatistical simulation codes, and work instead with grids in spherical geometry relevant to problems in Earth and Space sciences.We demonstrate our scheme using a synthetic example, showing that it produces realistic posterior realizations consistent with the known solution while fitting observations within their uncertainty and reproducing the distribution of model parameters and covariance statistics of a-priori models. Secondly, we present an application to real satellite observations, estimating the posterior probability distribution for the geomagnetic field at the core–mantle boundary. Our results reproduce well-known features of the core–mantle boundary magnetic field, and also allow probabilistic investigations of the magnetic field morphology. Small-length scale features in the posterior realizations are not determined by the observations but match the covariance statistics extracted from geodynamo simulations. The framework presented here represents a step towards more general approaches to probabilistic inversion in spherical geometry.

A stochastic mixed integer linear programming framework for oil sands mine planning and waste management in the presence of grade uncertainty

ABSTRACT The primary purpose of oil sands mine planning and waste management is to provide ore from the mine pit to the processing plant while containing the tailings in an efficient manner in-pit. Incorporating waste management in the mine plan is essential to maximize the economic potential of the mineral reserve and minimize waste management costs. However, spatial variability such as grade uncertainty results in ore tonnage variations, which leads to fluctuations in the quantity of ore to be processed and waste to be managed. This paper investigates the application of a stochastic mixed integer linear programming (SMILP) on oil sands mine planning to integrate bitumen grade uncertainty and waste management. Sequential Gaussian simulation is employed to quantitatively model the spatial variability of bitumen grade in the oil sands deposit. Multiple simulated orebody models are used as inputs for the SMILP model to generate optimal mine plans in the presence of grade uncertainty. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in net present value compared to the E-type and ordinary kriging schedules, respectively. These results indicate that the SMILP model is a robust tool for optimizing stochastic integrated oil sands production schedules and waste management.