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Item Open Access A new generic open pit mine planning process with risk assessment ability(International Journal of Coal Science and Technology, 2016-12-17) Mai, Ngoc Luan; Erten, Oktay; Topal, ErkanConventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value (NPV), are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% (135 M$ to 151 M$). A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine.Item Open Access ANAEROBIC MICROBIAL COMMUNITIES AND THEIR POTENTIAL FOR BIOENERGY PRODUCTION IN HEAVILY BIODEGRADED PETROLEUM RESERVOIRS(Wiley, 2020-03) Rezende, Júlia R.; Oldenburg, Thomas B. P.; Korin, Tetyana; Richardson, William D. L.; Fustic, Milovan; Aitken, Carolyn M.; Bowler, Bernard F. J.; Sherry, Angela; Grigoryan, Alexander; Voordouw, Gerrit; Larter, Stephen R.; Head, Ian M.; Hubert, Casey R. J.Most of the oil in low temperature, non‐uplifted reservoirs is biodegraded due to millions of years of microbial activity, including via methanogenesis from crude oil. To evaluate stimulating additional methanogenesis in already heavily biodegraded oil reservoirs, oil sands samples were amended with nutrients and electron acceptors, but oil sands bitumen was the only organic substrate. Methane production was monitored for over 3000 days. Methanogenesis was observed in duplicate microcosms that were unamended, amended with sulfate or that were initially oxic, however methanogenesis was not observed in nitrate‐amended controls. The highest rate of methane production was 0.15 μmol CH4 g−1 oil d−1, orders of magnitude lower than other reports of methanogenesis from lighter crude oils. Methanogenic Archaea and several potential syntrophic bacterial partners were detected following the incubations. GC–MS and FTICR–MS revealed no significant bitumen alteration for any specific compound or compound class, suggesting that the very slow methanogenesis observed was coupled to bitumen biodegradation in an unspecific manner. After 3000 days, methanogenic communities were amended with benzoate resulting in methanogenesis rates that were 110‐fold greater. This suggests that oil‐to‐methane conversion is limited by the recalcitrant nature of oil sands bitumen, not the microbial communities resident in heavy oil reservoirs.Item Open Access APPLICATION OF ANIONIC SURFACTANT\ENGINEERED WATER HYBRID EOR IN CARBONATE FORMATIONS: AN EXPERIMENTAL ANALYSIS(Petroleum, 2020-10-20) Sekerbayeva, Aigerim; Pourafshary, Peyman; Hashmet, Muhammad RehanA hybrid enhanced oil recovery (EOR) method by combining low salinity water (LSW) and low salinity surfactant (LSS) flooding techniques was designed. Different experiments were done to screen the Caspian seawater (SW) with altered ionic composition and surfactant, for the optimized performance in Kazakhstani carbonate oil fields. Changing to a more water-wet state and creating the middle phase were studied as the main criteria to select the best-engineered brine and anionic surfactant. The largest alteration towards the water-wet condition was recorded at 10 times dilution of the SW with 3- and 6- times spiked calcium and sulfate ions, respectively (10xSW-6SO4, Mg, 3Ca). This combination of anionic surfactants with carbonate formations is considered as a new approach in hybrid EOR methods. Among the anionic surfactants screened, Soloterra-113H (Alkyl Benzenesulfonic acid) showed the best solubilization ratio, aqueous stability, and Winsor type 3 microemulsions. The wettability alteration by the combination of optimized brine and screened surfactant was greater compared to the standalone LSW, which was confirmed by the 10◦ difference in contact angle measurement. The microemulsion phase constituted nearly 40% of the total height of the oil/brine column by the hybrid method. The recovery factor after injecting formation water was 52%, and it increased to 61% after optimized LSW injection. After switching to the engi neered brine/surfactant, the recovery factor reached 70%, which proves the effectiveness of the hybrid method. The proposed combined method works better than either standalone EOR method due to the higher alteration in capillary number by changing wettability and reducing IFT, which leads to higher oil recovery.Item Open Access Application of cellulose‑based polymers in oil well cementing(Springer, 2019-11-22) Irawan, Sonny; Memon, Khalil Rehman; Khan, Javed; Abbas, GhulamCellulose-based polymers have been successfully used in many areas of petroleum engineering especially in enhanced oil recovery drilling fluid, fracturing and cementing. This paper presents the application of cellulose-based polymer in oil well cementing. These polymers work as multifunctional additive in cement slurry that reduce the quantity of additives and lessen the operational cost of cementing operation. The viscosity of cellulose polymers such as hydroxyethyl cellulose (HEC), carboxymethylcellulose (CMC) and hydroxypropyl methylcellulose (HPMC) has been determined at various temperatures to evaluate the thermal degradation. Moreover, polymers are incorporated in cement slurry to evaluate the properties and affect in cement slurry at 90 °C. The API properties like rheology, free water separation, fluid loss and compressive strength of slurries with and without polymer have been determined at 90 °C. The experimental results showed that the viscosity of HPMC polymer was enhanced at 90 °C than other cellulose-based polymers. The comparative and experimental analyses showed that the implementation of cellulose-based polymers improves the API properties of cement slurry at 90 °C. The increased viscosity of these polymers showed high rheology that was adjusted by adding dispersant which optimizes the rheology of slurry. Further, improved API properties, i.e., zero free water separation, none sedimentation, less than 50 ml/30 min fluid loss and high compressive strength, were obtained through HEC, CMC and HPMC polymer. It is concluded that cellulose-based polymers are efficient and effective in cement slurry that work as multifunctional additive and improve API properties and cement durability. The cellulose-based polymers work as multifunctional additive that reduces the quantity of other additives in cement slurry and ultimately reduces the operational cost of cementing operation. The comparative analysis of this study opens the window for petroleum industry for proper selection of cellulose-based polymer in designing of cement slurry.Item Metadata only Application of predictive data mining to create mine plan flexibility in the face of geological uncertainty(Resources Policy, 2017-11-10) Ajak, Ajak Duany; Lilford, Eric; Topal, Erkan; Ajak Duany, AjakAbstract Geological uncertainty represents an inherent threat for all mining projects. Mining operations utilise resource block models as a primary source of data in planning and in decision making. However, such operational decisions are not free from risk and uncertainty. For the majority of iron ore mines, as an example, uncertainties such as clay pods and variability in grades and tonnages can have dramatic impacts on projects’ viability. However, a paradigm shift on how uncertainty is treated and a willingness to invest in areas that create operational flexibility can mitigate potential losses. Data analytics is touted as one of the major disruptions in the 21st century and operations that properly utilise data can create real opportunities in the face of an uncertain future. Since organisations have abundant definite geological data, a combination of data mining and real options can provide a competitive advantage. In the present study, predictive data mining algorithms were applied to a real case mine operation to predict the probability of encountering problematic ore in a mining schedule. The data mining model outputs were used to generate possible real options that the operations could exercise to deal with clay uncertainty. The most suitable data mining algorithm chosen for this task was the classification tree, which predicted the occurrence of clay with 78.6% precision. Poisson distribution and Monte Carlo simulations were applied to analyse various real options. The research revealed that operations could minimise unscheduled losses in the processing plant and could increase a project's present value by between 12% and 21% if the predictive data mining algorithm was applied to create real options.Item Open Access Application of several optimization techniques for estimating TBM advance rate in granitic rocks(SCIENCE PRESS, 2019-08) Armaghani, Danial Jahed; Koopialipoor, Mohammadreza; Marto, Aminaton; Yagiz, SaffetThis study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite. For this purpose, extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang – Selangor raw water transfer tunnel in Malaysia. Rock properties consisting of uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock mass rating (RMR), rock quality designation (RQD), quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization. Accordingly, to estimate the advance rate of TBM, two new hybrid optimization techniques, i.e. an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were developed for mechanical tunneling in granitic rocks. Further, the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices including coefficient of determination (R2), root mean square error (RMSE) and variance account for (VAF) were utilized herein. The values of R2, RMSE, and VAF ranged in 0.939–0.961, 0.022–0.036, and 93.899–96.145, respectively, with the PSO-ANN hybrid technique demonstrating the best performance. It is concluded that both the optimization techniques, i.e. PSO-ANN and ICA-ANN, could be utilized for predicting the advance rate of TBMs; however, the PSO-ANN technique is superior.Item Open Access APPLICATION OF SOFT COMPUTING TECHNIQUES TO ESTIMATE CUTTER LIFE INDEX USING MECHANICAL PROPERTIES OF ROCKS(Applied Sciences, 2022) Massalov, Timur; Yagiz, Saffet; Adoko, Amoussou CoffiThe wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2 ) and lower Mean square error (MSE).Item Open Access ASPHALTENE PRECIPITATION PREDICTION DURING BITUMEN RECOVERY: EXPERIMENTAL APPROACH VERSUS POPULATION BALANCE AND CONNECTIONIST MODELS(ACS Omega, 2022) Yerkenov, Turar; Tazikeh, Simin; Tatar, Afshin; Shafiei, AliDeasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data preprocessing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.Item Open Access ASSESSING HETEROTOPIC SEARCHING STRATEGY IN HIERARCHICAL COSIMULATION FOR MODELING THE VARIABLES WITH INEQUALITY CONSTRAINTS(Academie des sciences, 2021) Abulkhair, Sultan; Madani, NasserA hierarchical sequential Gaussian cosimulation method is applied in this study for modeling the variables with an inequality constraint in the bivariate relationship. An algorithm is improved by embedding an inverse transform sampling technique in the second simulation to reproduce bivariate complexity and accelerate the process of cosimulation. A heterotopic simple cokriging (SCK) is also proposed, which introduces two moving neighborhoods: single and multiple searching strategies in both steps of the hierarchical process. The proposed algorithm is tested over a real case study from an iron deposit where iron and aluminum oxide shows a strong bivariate dependency as well as a sharp inequality constraint. The results showed that the proposed hierarchical cosimulation with a multiple searching strategy provides satisfying results compared to the case when a single searching strategy is employed. Moreover, the proposed algorithm is compared to the conventional hierarchical cosimulation, which does not implement the inverse transform sampling integrated into the second simulation. The proposed methodology successfully reproduces inequality constraint, while conventional hierarchical cosimulation fails in this regard. However, it is demonstrated that the proposed methodology requires further improvement for better reproduction of global statistics (i.e., mean and standard deviation).Item Metadata only Bayesian prediction of TBM penetration rate in rock mass(Engineering Geology, 2017-08-30) Adoko, Amoussou Coffi; Gokceoglu, Candan; Yagiz, Saffet; Amoussou Coffi, AdokoAbstract One of the essential tasks in the excavation of tunnels with TBM is the reliable estimation of its performance needed for the planning, cost control and other decision making on the feasibility of the tunneling project. The current study aims at predicting the rate of penetration (RoP) of TBM on the basis of the rock mass parameters including the uniaxial compressive strength (UCS), intact rock brittleness (BI), the angle between the plane of weakness and the TBM driven direction (α) and the distance between planes of weakness (DPW). To this end, datasets from the Queens Water Tunnel No. 3 project, New York City, are compiled and used to establish the models. The Bayesian inference approach is implemented to identify the most appropriate models for estimating the RoP among eight (8) candidate models that have been proposed. The selected TBM empirical models are fitted to field data. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and Markov chain Monte Carlo (MCMC) techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. Meanwhile, the deviance information criterion (DIC) is used as the main prediction accuracy indicator and therefore, to rank the models taking into account both their fit and complexity. Overall, the results indicate that the proposed RoP model possesses satisfactory predictive performance.Item Open Access CAPILLARY DESATURATION CURVE: DOES LOW SALINITY SURFACTANT FLOODING SIGNIFICANTLY REDUCE THE RESIDUAL OIL SATURATION?(Springer, 2021-01-02) Zivar, Davood; Pourafshary, Peyman; Moradpour, NikooDifferent oil displacement experiments conducted on sandstone and carbonate samples show that low salinity water (LSW) injection can reduce the residual oil saturation (ROS). Recently, surfactant flooding (SF) in combination with low salinity water (known as low salinity surfactant (LSS) flooding) is proposed as a potentially promising hybrid enhanced oil recovery (EOR) process. A lower ROS is reported for a LSS process compared to that seen in SF or with LSW at the same capillary number. The capillary desaturation curve (CDC) is a well-known tool to study the effect of viscous and capillary forces on ROS for different EOR techniques. In this study, ROS data of various LSW, SF, and LSS flooding experiments at different capillary numbers are collected to develop a CDC to analyze the performance of the hybrid LSS method. This can help to analyze the effect of the hybrid method on an extra improvement in sweep efficiency and reduction in residual oil. A lower ROS is observed for LSS compared to LSW and SF in the same capillary number range. Our study shows different behaviors of the hybrid method at different ranges of capillary numbers. Three regions are identified based on the capillary number values. The difference in ROS is not significant in the first region (capillary number in the range of 10−7–10−5), which is not applicable in the presence of surfactant due to the low interfacial tension value. A significant reduction in ROS is observed in the second region (capillary number in the range of 10−5–10−2) for LSS compared to SF. This region is the most practical range for SF and LSS flooding. Hence, the application of LSS provides a noticeable benefit compared to normal EOR techniques. In the third region (capillary numbers greater than 10−2), where the surfactant flooding is a better performer, the difference in ROS is negligible.Item Open Access CAPILLARY DESATURATION CURVE: DOES LOW SALINITY SURFACTANT FOODING SIGNIFCANTLY REDUCE THE RESIDUAL OIL SATURATION?(Journal of Petroleum Exploration and Production Technology, 2021-01-02) Zivar, Davood; Pourafshary, Peyman; Moradpour, NikooDifferent oil displacement experiments conducted on sandstone and carbonate samples show that low salinity water (LSW) injection can reduce the residual oil saturation (ROS). Recently, surfactant flooding (SF) in combination with low salinity water (known as low salinity surfactant (LSS) flooding) is proposed as a potentially promising hybrid enhanced oil recovery (EOR) process. A lower ROS is reported for a LSS process compared to that seen in SF or with LSW at the same capillary number. The capillary desaturation curve (CDC) is a well-known tool to study the effect of viscous and capillary forces on ROS for different EOR techniques. In this study, ROS data of various LSW, SF, and LSS flooding experiments at different capillary numbers are collected to develop a CDC to analyze the performance of the hybrid LSS method. This can help to analyze the effect of the hybrid method on an extra improvement in sweep efficiency and reduction in residual oil. A lower ROS is observed for LSS compared to LSW and SF in the same capillary number range.Item Open Access CAPILLARY DESATURATION TENDENCY OF HYBRID ENGINEERED WATER BASED CHEMICAL ENHANCED OIL RECOVERY METHODS(Energies, 2021) Shakeel, Mariam; Samanova, Aida; Pourafshary, Peyman; Hashmet, Muhammad RehanSeveral studies have shown the synergetic benefits of combining various chemical enhanced oil recovery (CEOR) methods with engineered waterflooding (EWF) in both sandstones and carbonate formations. This paper compares the capillary desaturation tendency of various hybrid combinations of engineered water (EW) and CEOR methods with their conventional counterparts. Several coreflood experiments were conducted, including EW-surfactant flooding (EWSF), EW-polymer flooding (EWPF), EW-alkali-surfactant flooding (EWASF), EW-surfactant-polymer flooding (EWSPF), and EW alkali-surfactant-polymer flooding (EWASP). Capillary numbers (Nc) and corresponding residual oil saturation (Sor) for each scenario are compared with capillary desaturation curves (CDC) of conventional CEOR methods from the literature. The results indicate that hybrid EW–CEOR methods have higher capillary desaturation tendency compared to conventional methods. The capillary numbers obtained by standalone polymer flooding (PF) are usually in the range from 10−6 to 10−5 , which are not sufficient to cause a significant reduction in Sor. However, the hybrid EW-polymer flooding approach considerably reduced the Sor for the same Nc values, proving the effectiveness of the investigated method. The hybrid EWASP flooding caused the highest reduction in Sor (23%) against Nc values of 8 × 10−2 , while conventional ASP flooding reduced the Sor for relatively higher Nc values (3 × 10−3 to 8 × 10−1 ). Overall, the hybrid methods are 30–70% more efficient in terms of recovering residual oil, compared to standalone EWF and CEOR methods. This can be attributed to the combination of different mechanisms such as wettability modification by EW, ultralow interfacial tension by alkali and surfactant, reduced surfactant adsorption by alkali addition, and favorable mobility ratio by polymer. Based on the promising results, these hybrid techniques can be effectively implemented to carbonate formations with harsh reservoir conditions such as high salinity and high temperature.Item Open Access Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modeling(Natural Resources Research, 2018) Madani, Nasser; Sadeghi, Behnam; John, CarranzaFractal/multifractal modeling is a widely used geomathematical approach to capturing different populations in geochemical mapping. The rationale of this methodology is based on empirical frequency density functions attained from global or local distributions. This approach is quite popular because of its simplicity and versatility; it accounts for the frequency and spatial distribution of geochemical data considering self-similarity across a range of scales. Using this technique for detection of geochemical anomalies in scarce data, however, is problematic and can lead to systematic bias in the characterization of the underlying populations. In this paper, an innovative technique is presented that provides good results without a priori assumptions. A simulation approach is adopted for fractal analysis by generating different possible distribution scenarios for the variable under study to reveal the underlying populations that are frequently hidden due to lack of data. The proposed technique is called the global simulated size–number method, and it is validated in a case study with two synthetic datasets and another case study with real dataset from the Ushtagan gold deposit in northeast Kazakhstan.Item Open Access CASE REPORT: FIRST TWO IDENTIFIED CASES OF FABRY DISEASE IN CENTRAL ASIA(Frontiers Media S.A., 2021-04-27) Cainelli, Francesca; Argandykov, Dias; Kaldarbekov, Dauren; Mukarov, Murat; Tran Thi Phuong, Liên; Germain, Dominique P.Background: Fabry disease (FD, OMIM #301500) is a rare, progressive, X-linked inherited, genetic disease due to the functional deficiency of lysosomal α-galactosidase (α-GAL) that leads to the accumulation of glycosphingolipids (mainly globotriaosylceramide or Gb3) and its derivative globotriaosylsphingosine or lyso-Gb3. Classic FD is a multisystem disorder which initially presents in childhood with neuropathic pain and dermatological, gastrointestinal, ocular, and cochleo-vestibular manifestations. Over time, end-organ damage such as renal failure, cardiac arrhythmia and early stroke may develop leading to reduced life expectancy in the absence of specific treatment. Case presentation: We describe two Kazakh patients who presented in adulthood with a delayed diagnosis. We conducted also a family screening through cascade genotyping. Conclusion: This is the first description of cases of Fabry disease in Central Asia. An extensive family pedigree enabled the identification of ten additional family members. Patients with rare genetic diseases often experience substantial delays in diagnosis due to their rarity and non-specific symptoms, which can negatively impact their management and delay treatment. FD may be difficult to diagnose because of the non-specificity of its early and later-onset symptoms and its X-linked inheritance. Raising awareness of clinicians is important for earlier diagnosis and optimal outcome of specific therapies.Item Open Access CATALYTIC EFFECTS OF TEMPERATURE AND SILICON DIOXIDE NANOPARTICLES ON THE ACCELERATION OF PRODUCTION FROM CARBONATE ROCKS(MDPI AG, 2021-06-23) Salaudeen, Ibraheem; Hashmet, Muhammad Rehan; Pourafshary, PeymanThe use of engineered water (EW) nanofluid flooding in carbonates is a new enhanced oil recovery (EOR) hybrid technique that has yet to be extensively investigated. In this research, we investigated the combined effects of EW and nanofluid flooding on oil-brine-rock interactions and recovery from carbonate reservoirs at different temperatures. EW was used as dispersant for SiO2 nanoparticles (NPs), and a series of characterisation experiments were performed to determine the optimum formulations of EW and NP for injection into the porous media. The EW reduced the contact angle and changed the rock wettability from the oil-wet condition to an intermediate state at ambient temperature. However, in the presence of NPs, the contact angle was reduced further, to very low values. When the effects of temperature were considered, the wettability changed more rapidly from a hydrophobic state to a hydrophilic one. Oil displacement was studied by injection of the optimised EW, followed by an EW-nanofluid mixture. An additional recovery of 20% of the original oil in place was achieved. The temperature effects mean that these mechanisms are catalytic, and the process involves the initiation and activation of multiple mechanisms that are not activated at lower temperatures and in each standalone technique.Item Open Access Categorization of Mineral Resources Based on Different Geostatistical Simulation Algorithms: A Case Study from an Iron Ore Deposit(Natural Resources Research, 2019-03) Battalgazy, Nurassyl; Madani, Nasser; Carranza, JohnMineral resource classification plays an important role in the downstream activities of a mining project. Spatial modeling of the grade variability in a deposit directly impacts the evaluation of recovery functions, such as the tonnage, metal quantity and mean grade above cutoffs. The use of geostatistical simulations for this purpose is becoming popular among practitioners because they produce statistical parameters of the sample dataset in cases of global distribution (e.g., histograms) and local distribution (e.g., variograms). Conditional simulations can also be assessed to quantify the uncertainty within the blocks. In this sense, mineral resource classification based on obtained realizations leads to the likely computation of reliable recovery functions, showing the worst and best scenarios. However, applying the proper geostatistical (co)-simulation algorithms is critical in the case of modeling variables with strong cross-correlation structures. In this context, enhanced approaches such as projection pursuit multivariate transforms (PPMTs) are highly desirable. In this paper, the mineral resources in an iron ore deposit are computed and categorized employing the PPMT method, and then, the outputs are compared with conventional (co)-simulation methods for the reproduction of statistical parameters and for the calculation of tonnage at different levels of cutoff grades. The results show that the PPMT outperforms conventional (co)- simulation approaches not only in terms of local and global cross-correlation reproductions between two underlying grades (Fe and Al2O3) in this iron deposit but also in terms of mineral resource categories according to the Joint Ore Reserves Committee standard.Item Open Access CFD VALIDATION FOR ASSESSING THE REPERCUSSIONS OF FILTER CAKE BREAKERS; EDTA AND SIO2 ON FILTER CAKE RETURN PERMEABILITY(Applied Artificial Intelligence, 2022) Wayo, Dennis Delali Kwesi; Irawan, Sonny; Khan, Javed; Fitrianti, N. H.Drill-in-fluids create what are known as filter cakes. Filter cakes, in some cases, lead to well abandonment because they prevent hydrocarbons from flowing freely from the formation into the wellbore. Cake removal is essential to avoid formation damage. A previous study on filter cake breakers was considered for computational fluid dynamic (CFD) validation. Matlab-CFD and Navier-Stokes equations aimed at predicting and validating visual, multiphase flow under finite element analysis (FEA). The interactions of separate chemical breakers and drill-in-fluid such as ethylenediaminetetraacetic acid (EDTA), silica-nanoparticle (SiO2), and biodegradable synthetic-based mud drill-in-fluid (BSBMDIF) were monitored under a particle size distribution, viscosity, density, and pressure. Predicting return permeability of filter cake was considered under a simple filtration process. The particles’ deposition created pore spaces between them; barite 74 μm, nano-silica 150 nm, and EDTA 10 μm generally closed up the pores of the filtration medium. Under extreme drilling conditions, barite formed thicker regions, and EDTA chemical properties easily disjointed these particles, while SiO2 entirely did not. The experimented results of (EDTA) and SiO2 for return permeability were in full force agreeable with the 2D simulation. A hybrid computational analysis considering CFD under discrete element analysis and neural network can be employed for further research validations.Item Open Access Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies(Natural Resources Research, 2020-02-01) Madani, Nasser; Carranza, JohnIdentification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of exploratory data analysis, such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical elements. The latter workflow involves analysis of 2D/3D produced maps, which can be mostly obtained by geostatistical algorithms. There are two challenging issues for such an analysis. The first one corresponds to handling the cross-correlation structures among the data, and the second one relates to the compositional nature of data. To tackle these problems, this paper investigates the application of Gaussian co-simulation for modeling the cross-correlated compositional data in order to recognize the multivariate geochemical anomalies in integration with fractal analysis. In this context, an innovative algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The compositional nature of data is addressed by additive log-ratio transformation of original data while the Gaussian co-simulation handles the reproduction of cross-correlation among the components. The co-simulated outputs are then taken into account for capturing different geochemical populations, showing different levels of backgrounds and anomalies. The algorithm is illustrated via a real case study located in Philippine wherever seven geochemical components are required to be considered. The accuracy of results is examined by statistical validation techniques, indicating the capability of the CoSS-N algorithm for multivariate identification of geochemical anomalies.Item Open Access COMPARATIVE STUDY OF NATURAL CHEMICAL FOR ENHANCED OIL RECOVERY: FOCUS ON EXTRACTION AND ADSORPTION AT QUARTZ SAND SURFACE(Petroleum, 2022) Hashim Abbas, Azza; Abd Alsaheb, Ramzi A.; Kamil Abdullah, JaafarIn chemical enhanced oil recovery (CEOR), it is very important to utilize the excessive usage of chemicals. A great opportunity lies in adopting natural surfactants, since it is cheaper, ecosystem friendly, and less toxic than their counterpart synthetic surfactants. Despite the availability of natural surfactant sources, it is yet very early to decide on their applicability. Therefore, this research focuses on natural-saponin extracted from different raw materials available in the Middle East and their interaction with quartz-sand. A special focus was given to the adsorption isotherm models to describe the interaction with the reservoir rocks.