Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches.

Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity-permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.

A comprehensive review of the epidemiology and clinical features of 91 cases with Buerger's disease.

BACKGROUND: Thromboangiitis Obliterans (TAO) is a disease of small and medium-sized arteries with an unclear natural course. This study aims to establish a national registry of the disease to gain a better understanding of its epidemiology and clinical course. METHOD: This study was a cohort study of 242 patients with a high probability of TAO admitted to Mashhad University of Medical Sciences (MUMS) hospitals from 2000 to 2015. Of these, 91 patients with a confirmed diagnosis were included in the study (90 males and 1 female) with a mean age of 35 ± 7.8 years. RESULTS: The most common symptom upon onset of the disease was paresthesia (29.7%), followed by cold sensitivity and paresthesia (93.4%) during the progression of the disease and Raynaud syndrome or vasospasm (93.9%) in the active phase. The right lower limb was the most commonly affected limb (46.2%), and presenting ischemic symptoms in 48.4%.Statistics indicated a positive correlation between the duration of Burger's disease and the number of affected limbs (p = 0.001). There was no effect of disease duration on the likelihood of amputations (p = 0.28). CONCLUSION: Some patients may experience mild, subtle symptoms for years before the initial signs and symptoms appear, which can be severe and rapidly progress to the point of requiring amputation.We suggest that the diagnostic criteria for Buerger's disease should be revised in light of the presence of atherosclerosis and its associated risk factors, which present a challenge in terms of diagnosis and treatment. Clinical experience will be of great importance in this regard.

Crashworthiness Assessment of Carbon/Glass Epoxy Hybrid Composite Tubes Subjected to Axial Loads.

The crashworthiness of composite tubes is widely examined for various types of FRP composites. However, the use of hybrid composites potentially enhances the material characteristics under impact loading. In this regard, this study used a combination of unidirectional glass-carbon fibre reinforced epoxy resin as the hybrid composite tube fabricated by the pultrusion method. Five tubes with different length aspect ratios were fabricated and tested, in which the results demonstrate "how structural energy absorption affects by increasing the length of tubes". Crash force efficiency was used as the criterion to show that the selected L/D are acceptable of crash resistance with 95% efficiency. Different chamfering shapes as the trigger mechanism were applied to the tubes and the triggering effect was examined to understand the impact capacity of different tubes. A finite element model was developed to evaluate different crashworthiness indicators of the test. The results were validated through a good agreement between experimental and numerical simulations. The experimental and numerical results show that hybrid glass/carbon tubes accomplish an average 25.34 kJ/kg specific energy absorption, average 1.43 kJ energy absorption, average 32.43 kN maximum peak load, and average 96.67% crash force efficiency under quasi-static axial loading. The results show that selecting the optimum trigger mechanism causes progressive collapse and increases the specific energy absorption by more than 35%.

A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection.

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas-oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (R 2: 0.793, RMSE: 7.5) and the SVM models (R 2: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements.

Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models.

Deasphalting 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 pre-processing 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.

A systematic and critical review of application of molecular dynamics simulation in low salinity water injection.

Low Salinity Water Injection (LSWI) has been a well-researched EOR method, with several experimental and theoretical scientific papers reported in the literature over the past few decades. Despite this, there is still an ongoing debate on dominant mechanisms behind this complex EOR process, and some issues remain elusive. Part of the complexity arises from the scale of investigation, which spans from sub-pore scale (atomic and electronic scale) to pore scale, core scale, and reservoir scale. Molecular Dynamics (MD) simulation has been used as a research tool in the past decade to investigate the nano-scale interactions among reservoir rock (e.g., calcite, silica), crude oil, and brine systems in presence of some impurities (e.g., clay minerals) and additives (e.g., nanoparticles). In this paper, fundamental concepts of MD simulation and common analyses driven by MD are briefly reviewed. Then, an overview of molecular models of the most common minerals encountered in petroleum reservoirs: quartz, calcite, and clay, with their most common types of potential function, is provided. Next, a critical review and in depth analysis of application of MD simulations in LSWI process in both sandstone and carbonate reservoirs in terms of sub-pore scale mechanisms, namely electrical double layer (EDL) expansion, multi-ion exchange (MIE), and cation hydration, is presented to scrutinize role of salinity, ionic composition, and rock surface chemistry from an atomic level. Some inconsistencies observed in the literature are also highlighted and the reasons behind them are explained. Finally, a future research guide is provided after critically discussing the challenges and potential of the MD in LSWI to shed more light on governing mechanisms behind LSWI by enhancing the reliability of MD outcomes in future researches. Such insights can be used for design of new MD researches with complementary experimental studies at core scale to capture the main mechanisms behind LSWI.

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs.

Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RFf) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80-20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca2+ in the connate water, Na+ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RFf. The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoirs.

Bioinspired buckling of scaled skins.

Natural flexural armors combine hard, discrete scales attached to soft tissues, providing unique combinations of surface hardness (for protection) and flexibility (for unimpeded motion). Scaled skins are now inspiring synthetic protective materials which offer attractive properties, but which still suffer from limited trade-offs between flexibility and protection. In particular, bending a scaled skin with the scales on the intrados side jams the scales and stiffen the system significantly, which is not desirable in systems like gloves where scales must cover the palm side. Nature appears to have solved this problem by creating scaled skins that can form wrinkles and folds, a very effective mechanism to accommodate large bending deformations and to maintain flexural compliance. This study is inspired from these observations: we explored how rigid scales on a soft membrane can buckle and fold in a controlled way. We examined the energetics of buckling and stability of different buckling modes using a combination of discrete element modeling and experiments. In particular, we demonstrate how scales can induce a stable mode II buckling, which is required for the formation of wrinkles and which could increase the overall flexural compliance and agility of bioinspired protective elements.

Synthesis, Characterization, and Assessment of a CeO2@Nanoclay Nanocomposite for Enhanced Oil Recovery.

In this paper, synthesis and characterization of a novel CeO2/nanoclay nanocomposite (NC) and its effects on IFT reduction and wettability alteration is reported in the literature for the first time. The NC was characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), energy-dispersive X-ray spectroscopy (EDS), and EDS MAP. The surface morphology, crystalline phases, and functional groups of the novel NC were investigated. Nanofluids with different concentrations of 100, 250, 500, 1000, 1500, and 2000 ppm were prepared and used as dispersants in porous media. The stability, pH, conductivity, IFT, and wettability alternation characteristics of the prepared nanofluids were examined to find out the optimum concentration for the selected carbonate and sandstone reservoir rocks. Conductivity and zeta potential measurements showed that a nanofluid with concentration of 500 ppm can reduce the IFT from 35 mN/m to 17 mN/m (48.5% reduction) and alter the contact angle of the tested carbonate and sandstone reservoir rock samples from 139° to 53° (38% improvement in wettability alteration) and 123° to 90° (27% improvement in wettability alteration), respectively. A cubic fluorite structure was identified for CeO2 using the standard XRD data. FESEM revealed that the surface morphology of the NC has a layer sheet morphology of CeO2/SiO2 nanocomposite and the particle sizes are approximately 20 to 26 nm. TGA analysis results shows that the novel NC has a high stability at 90 °C which is a typical upper bound temperature in petroleum reservoirs. Zeta potential peaks at concentration of 500 ppm which is a sign of stabilty of the nanofluid. The results of this study can be used in design of optimum yet effective EOR schemes for both carbobate and sandstone petroleum reservoirs.

Frequency and Risk Factors of Penicillin and Amoxicillin Allergy in Suspected Patients with Drug Allergy.

BACKGROUND: Unconfirmed beta-lactam allergy is a significant public health problem because of the limitations it imposes in drug selection. In this study, we aimed to evaluate patients referred for beta-lactam allergy to determine the frequency of confirmed beta-lactam allergy and identify some risk factors. METHODS: In a prospective cohort study, all referred patients to Immunology, Asthma and Allergy Research Institute in Tehran University of Medical Sciences (between 2007 - 2009) who suspected to have beta-lactam allergy were entered into this study based on having the inclusion criteria. Follow-up was performed 6 - 8 years after the final diagnosis. Diagnosis of beta-lactam allergy relies on thorough history and specific IgE measurements (ImmunoCAP), skin prick testing (SPT), intradermal testing (IDT), patch testing, and oral drug challenge test. RESULTS: Fifty-one patients with mean age of 24.5 (±18.5) years were enrolled in this study. Based on workups, beta-lactam allergy was confirmed in 16 (31.4%) patients, suspicious in 22 (43.1%) patients and ruled out in 13 (25.5%) patients.  During the follow-up, 3 patients with suspicious drug allergy consumed the culprit drug with no reaction so allergy was finally ruled out in 16 (31.4%) patients. Age, sex, atopy and family history of drug allergies were not significantly different between the patients with confirmed or ruled-out diagnosis of penicillin and amoxicillin allergy. CONCLUSION: At least up to one-third of patients with a history of beta-lactam allergy are proven to be safe using the drug. Also, a clear protocol consists of serum sIgE assay and SPT can be helpful to the physicians in the health care system.

Endovascular Repair of Interrupted Aortic Arch: Approach with Hope for Fewer Complications.

Interrupted aortic arch (IAA) is a rare congenital malformation defined as complete discontinuity between ascending and descending parts of aorta. We present a case of IAA, which was referred to us due to dilatation of proximal and mid parts of his thoracic aorta accompanied by narrowing of aorta proximal to the branching of the left subclavian artery. Further evaluation revealed interruption of aorta at the proximal part of descending thoracic aorta by a transverse septum along with several collateral formations. In general, the standard treatment of IAA is open surgical repair. Endovascular repair of IAA is an alternative approach for IAA, which is applied when two distinct parts of aorta are too close to each other. Here, we present a new approach of endovascular transcatheter repair of IAA with implantation of a self-expandable stent that we believe has fewer complications.