Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
310
result(s) for
"Rezaee, Reza"
Sort by:
Synthesizing Nuclear Magnetic Resonance (NMR) Outputs for Clastic Rocks Using Machine Learning Methods, Examples from North West Shelf and Perth Basin, Western Australia
A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool’s outputs for clastic rocks using typical well-logs as inputs. NMR tool’s outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T2 relaxation time (T2LM), irreducible water saturation (Swirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T2LM, and SWirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy.
Journal Article
Quantifying Natural Hydrogen Generation Rates and Volumetric Potential in Onshore Serpentinization
This study explores the generation of natural hydrogen through the serpentinization of onshore ultramafic rocks, highlighting its potential as a clean energy resource. By investigating critical factors such as mineral composition, temperature, and pressure, the research develops an empirical model using multiple regression analysis to predict hydrogen generation rates under varying geological conditions. A novel five-stage volumetric calculation methodology is introduced to estimate hydrogen production from ultramafic rock bodies. The application of this framework to the Giles Complex, an ultramafic-mafic intrusion in Australia, suggests a hydrogen generation potential of approximately 2.24 × 1013 kg of hydrogen through partial serpentinization. This estimate is based on the assumed mineral composition, depth, and temperature conditions within the intrusion, which influence the extent of serpentinization reactions. The findings demonstrate the significant potential of ultramafic complexes for natural hydrogen production and provide a foundation for advancing natural hydrogen exploration, refining predictive models, and supporting sustainable energy development.
Journal Article
Editorial on Special Issues of Development of Unconventional Reservoirs
The energy transition to renewable energy is inevitable since fossil fuels are a finite source [...]
Journal Article
Fundamentals of Gas Shale Reservoirs
Provides comprehensive information about the key exploration, development and optimization concepts required for gas shale reservoirs: Includes statistics about gas shale resources and countries that have shale gas potential; Addresses the challenges that oil and gas industries may confront for gas shale reservoir exploration and development; Introduces petrophysical analysis, rock physics, geomechanics and passive seismic methods for gas shale plays; Details shale gas environmental issues and challenges, economic consideration for gas shale reservoirs; Includes case studies of major producing gas shale formations.
eBook
The nitrate content of fresh and cooked vegetables and their health-related risks
Vegetables are the most important source of nitrates in the human diet. During various processes in the body, nitrates are converted into nitrites, which causes various diseases, such as blue baby syndrome and cancer. This study aimed to determine the concentration of nitrates in several vegetable farms in Sanandaj city and to evaluate their health-related risks.
This descriptive cross-sectional study was conducted from October 2017 to July 2018. A total of 90 samples were taken from nine farms. Soil and water sampling was also carried out. All stages of sample preparation and extraction were carried out according to Food Standards 2-16721, and the nitrate measurements were performed using ion chromatography (Compact IC Plus 882 Model, Metrohm, Switzerland). A health risk assessment was performed using the non-carcinogenic risk assessment.
This study's results showed that the nitrate concertation in all vegetables was less than National Iranian Vegetable Nitrate Standard. Nitrate levels in leafy vegetables were higher than in root vegetables, and the root vegetables levels were higher than those in Fruit vegetable. The nitrate level in vegetables in autumn was higher than in spring. The cooking process reduced the raw vegetables' nitrate content from 4.094% to 13.407%, while the frying process increased the vegetables' nitrate content from 12.46% to 29.93%. The highest health risk level in raw, cooked and fried vegetables was parsley, parsley and beet leaves, respectively, and the lowest in all categories was tomatoes. Generally, the highest health risk was related to fried beet leaves, and the lowest was raw tomatoes. In addition, each of the abovementioned relationships between vegetables' nitrate levels and the harvest season, type of processing procedure and type of vegetables was significant (p < 0.05). The irrigation water's nitrate concentration in all fields was between 12.36 and 33.14 mg/l. The soil contained nitrate levels of between 4.35 and 9.7 mg/kg.
Based on this study, we can conclude that the amount of nitrates in raw vegetables was lower than the standard limit's level and that this level does not cause health problems for consumers.
Journal Article
Comparative Porosity and Pore Structure Assessment in Shales: Measurement Techniques, Influencing Factors and Implications for Reservoir Characterization
Porosity and pore size distribution (PSD) are essential petrophysical parameters controlling permeability and storage capacity in shale gas reservoirs. Various techniques to assess pore structure have been introduced; nevertheless, discrepancies and inconsistencies exist between each of them. This study compares the porosity and PSD in two different shale formations, i.e., the clay-rich Permian Carynginia Formation in the Perth Basin, Western Australia, and the clay-poor Monterey Formation in San Joaquin Basin, USA. Porosity and PSD have been interpreted based on nuclear magnetic resonance (NMR), low-pressure N2 gas adsorption (LP-N2-GA), mercury intrusion capillary pressure (MICP) and helium expansion porosimetry. The results highlight NMR with the advantage of detecting the full-scaled size of pores that are not accessible by MICP, and the ineffective/closed pores occupied by clay bound water (CBW) that are not approachable by other penetration techniques (e.g., helium expansion, low-pressure gas adsorption and MICP). The NMR porosity is largely discrepant with the helium porosity and the MICP porosity in clay-rich Carynginia shales, but a high consistency is displayed in clay-poor Monterey shales, implying the impact of clay contents on the distinction of shale pore structure interpretations between different measurements. Further, the CBW, which is calculated by subtracting the measured effective porosity from total porosity, presents a good linear correlation with the clay content (R2 = 0.76), implying that our correlated equation is adaptable to estimate the CBW in shale formations with the dominant clay type of illite.
Journal Article
Nuclear Magnetic Resonance (NMR) Outputs Generation for Clastic Rocks Using Multi Regression Analysis, Examples from Offshore Western Australia
A large database of nuclear magnetic resonance (NMR) logging data from clastic rocks of offshore oil and gas fields of Western Australia was used to assess the performance of multi regression analysis (MRA) to calculate NMR log outputs from conventional well logs. This short paper introduces a set of MRA equations for the calculation of the NMR log outputs using conventional well logs as inputs. This study shows that unlike machine learning methods the MRA approach fails to predict most of the NMR log outputs with acceptable accuracy but can provide Coates and SDR permeabilities with R2 of more than 0.75.
Journal Article
Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia
This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R2) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R2 of more than 90%.
Journal Article
Dual immobilization of magnetite nanoparticles and biosilica within alginate matrix for the adsorption of Cd(II) from aquatic phase
The adsorption of cadmium ions by magnetite (Fe
3
O
4
)@biosilica/alginate (MBA nano-hybrid) was the main aim of the present investigation. Herein, MBA nano-hybrid was synthesized via chemical precipitation technique. As-synthesized MBA nano-hybrid was characterized using FT-IR, FESEM and XRD analyzes. Based on the results, the maximum adsorption capacity of the adsorbent for the removal of Cd(II) was obtained at the initial pH of 7.0. At the initial Cd(II) concentration of 40 mg/L, increasing the reaction time to 180 min led to the Cd adsorption of 35.36 mg/g. Since the removal of Cd(II) after the reaction time of 60 min was insignificant, the reaction time of 60 min was considered as optimum reaction time for performing the experimental runs. According to the results, Langmuir isotherm and pseudo-second order kinetic models were the best fitted models with high correlation coefficients (R
2
> 0.99). The results of thermodynamic study indicated exothermic (positive ΔH°) and spontaneous nature (negative ΔG°) of the adsorption of Cd(II) on the surface of MBA nano-hybrid. Negligible reduction in the adsorption capacity of the nano-hybrid was observed (16.57%) after fifth experimental runs, indicating high reusability potential of the as-synthesized nano-hybrid adsorbent.
Journal Article