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The drive to mitigat water production continues to increase the interest in applying conformance-control gel treatments in mature oil fields. Practically, designs of new gel treatments are adopted from previous case histories conducted in analogous reservoirs. This design-by-analogy procedure requires reservoir engineers to have a thorough knowledge about how gel treatments should be designed and have been mostly applied for each reservoir type. This paper presents comprehensive design guidelines for sizes of injection-well gel treatments based on an integrated survey of worldwide field projects. It also provides the typical and most-applied treatment volumes per reservoir type to be used as a starting point in planning of new jobs. The survey includes 61 gel projects compiled from SPE papers and conducted in 11 different countries between 1985 and 2014. Summaries and distributions of five gel-volume parameters were evaluated using the univariate descriptive analysis and stacked histograms. Scatterplots were used to determine effects of the formation temperature and the treatment timing on treatment volumes. The unsuccessful pilots were compared with productive gel projects to indicate the causes of pilots’ failure. The survey showed that the typical treatment size is 10,700 barrels, 310 barrel per perforated foot, or 19% of the moveable-pore-volume (MPV) of problem zones. Overall, gel volumes are greatly affected by the formation type and larger treatment volumes are placed in sandstones than carbonates and in matrix-rock than other formation types. Frequently, matrix-rock formations are treated with gel volumes >100 bbl/ft while treatment sizes

The smart water-assisted foam flooding (SWAF) technique is a novel EOR method that is a synergic combination of smart water and foam-flooding methods. Smart water enables multi-level improvements, such as stabilizing foam-lamella and altering the wettability of the carbonate rock, which results in a desirable relative-permeability behavior. This paper experimentally investigated foam characterization related to enhanced oil recovery (EOR) using the smart water-assisted foam flooding (SWAF) technique including foamability and foam stability. This study aims to identify the foaming agents (surfactants) of the highest efficiency for employing in core flooding tests to establish their performance in porous-medium. Moreover, foamability and foam-stability tests were conducted to categorize the foams of selected anionic and cationic surfactants. The latter helps in developing an optimum surfactant aqueous solution (SAS) with the ability to form stable foams in both the presence and absence of crude oils with varying total acid and base numbers (TAN and TBN). The selected surfactants were Cetyltrimethylammonium bromide (CTAB), Dodecyltrimethylammonium bromide (DTAB), Alpha olefin sulfonate (AOS), and a commercial product termed as Alpha-foamer (Alkyl Ether Sulfates). These selected surfactants were tested in light, medium, and heavy crude oils from the Middle East region. Also, the effect of gases on foamability and foam-stability processes were examined using carbon dioxide (CO2) and nitrogen (N2).The gases were injected at a fixed flow rate (i.e., 25 and 50 mL/min) through surfactant solutions to generate foams. To determine the foam stability, the novel simplified R5 parameter was used, which involves introducing a controlled volume of gas into a finite volume of surfactant solution. Furthermore, in the screening process, it was observed that all the selected surfactants have good foamability with CTAB (e.g., 51 cm foam height in MgCl2 using N2 gas injection) and AOS (e.g., 49 cm foam height in MgCl2 using N2 gas injection) surfactants exhibiting the highest foam-durability when oil was absent. It was also noted that an increase in surfactant concentrations increased foam longevity. Moreover, the foam stability and oil-displacement efficiency were investigated using varying concentrations of CTAB, DTAB, Alfa-foamer, and AOS solutions. It was found that the presence of oil affected the foam columns’ stability. The extent of this effect depends on the surfactant-types, surfactant-concentrations, and the chemical solvation properties where stability decreases in the low concentration ranges of CTAB and all tested concentration ranges of DTAB. For the CTAB solution, it was observed that the oil remained in the lamella skeleton and its plateau borders without any noticeable drain out. Contrariwise, it was observed that DTAB could lift a sizable portion of the oil column, but could not sustain it for a longer duration with a quick drain out of oil. Incorporating CTAB and AOS into the SAS, enhanced its properties and proved to be the most effective foaming agent (i.e., both in the absence and presence of crude oil at R5 of 90 and 80%, respectively) used in core flooding for testing performance in a porous medium. Finally, under optimum SAS and smart water conditions, the proposed SWAF technique has the potential to be a commercially lucrative and environmentally acceptable novel hybrid EOR-method in carbonates.

Background. Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic relapsing disease indicated by inflammation of the gastrointestinal tract. Celiac disease (CeD) is a chronic autoimmune disease of the small bowel. The prevalence of CeD in IBD patients is unknown. Some studies have described the coexistence of the 2 diseases in the same patient. This study aimed to investigate the prevalence of CeD in Saudi Arabian children with IBD. Methods. We used a retrospective study design because data can be collected immediately and is easier to analyze afterward. The study was conducted on IBD patients in the Pediatric Gastroenterology Department at National Guard Hospital, Jeddah, Saudi Arabia. We enrolled Saudi patients aged between 1 and 18 years who had been diagnosed with IBD and CeD based on positive biochemical serology and histology from January 2011 to January 2020. We excluded patients with immunodeficiency disorders. Results. Among the 46 enrolled patients with IBD, CeD was identified in 4, and they did not develop any relapses. We discovered that the weight at IBD diagnosis improved significantly compared to current weight ( P-value

Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

Given the increasing demand for energy globally and depleting oil and gas resources, it is crucial to increase the production from existing reservoirs by introducing new technologies for Improved/Enhanced Oil Recovery (IOR/EOR). This contribution presents a novel hybrid IOR/EOR method, which combines smart water (SW) and foam flooding, known as Smart Water Assisted Foam (SWAF) flooding. The optimal conditions of the SWAF technology will be interpreted using experimental laboratory design (i.e., experimental data). The experimental design was divided into three main steps. The first step is obtaining rock wettability measurements using contact angle measurements. This step aims to select the optimum SW composition that changes the carbonate rock's wettability from oil-wet towards more water-wet and faster oil recoveries. The water-wet condition leads to high residual oil saturations and low end-point permeabilities. This is conductive to favourable mobility ratios and efficient water-oil displacement. However, high residual oil saturations are unfavourable to the high ultimate oil recovery as much oil stays behind. Secondly, the chemical screening follows, where two tests were performed, viz., (i) an Aqueous Stability Test (AST), (ii) and a Foamability and Foam Stability Tests (FT/FST). This step aims to generate a stable foam (i.e., surfactant aqueous solution + gas) in the absence and presence of crude oil with different TAN (Total Acid Number) and TBN (Total Base Number), viz., crude oils Type-A and Type-B. Favourable mobility ratio is achieved by the presence of foam, which leads to excellent displacement efficiency. Thirdly, core flooding tests are performed. This step aims to select the best formulations through SWAF core flooding tests to obtain the ultimate recovery factor under different injection scenarios. The optimal SWAF condition combines high ultimate recovery with the best displacement efficiency. It is shown that the enormous changes in wettability were seen for SW (MgCl2) solution at 3500 (ppm) for both crude oils Type-A and Type-B. It has been shown that the use of a cationic surfactant CTAB (i.e., cetyltrimethylammonium-bromide) in the positively charged carbonates (with an isoelectric point of pH = 9) is more effective than the use of anionic surfactant, e.g., Alpha Olefin Sulfonate (AOS). The aim is to create an optimum surfactant aqueous solution (SAS). The SAS stability is considerably affected by the concentration of both the SW (MgCl2) and surfactant (CTAB). In the absence of oil, the strength of foam (SAS and Gas) is highly dependent on the concentration and composition of the SW in the SAS. In the presence of oil, foam generation and stability are better when the crude oil has a low TAN and high TBN. From the core flooding tests for crude oils Type-A and Type-B, the ultimate residual oil recovery was achieved by the MgCl2 - foam injection combination (i.e., incremental oil recovery of 42%, which is equivalent to a cumulative oil recovery of 92%). In summary, SWAF under the optimum conditions is a promising method to increase the oil recovery from carbonate reservoirs.

The proposed study of combined low salinity foam Injection using DLVO-theory (i.e., Derjaguin, Landau, Verwey, and Overbeek) and surface complexation modeling or SCM, is a follow up of a previous study of a Novel Hybrid Enhanced Oil Recovery Method by Smart Water-Injection and Foam-Flooding in Carbonate Reservoirs (SPE-196407-MS). The method combines the advantages of our new designed "smart-water" (i.e., ionically modified brine or low salinity) injection with foam drive recovery. Our new desined "smart-water" injection has a double enhancement effect. It leads to change the limestone rock (i.e., calcite) wettability from oil or mixed-wet to more water wet (i.e., stable water-film), and helps to improve the stability of the foam-film.In the previous study (SPE-196407-MS) we investigated the impact of our "smart water" or low salinity injection on the surface complexes by simulating one single base case scenario, which equivalent to [NaCl 0.4 mMol/liter]. We use computr program (PHREEQC) to obtain the equilibrium concentrations and zeta-potential (surface potential or electro-kinetic potential), and to invetigate the effect of water-salinity and CO2 pressure for a given choice of the surfactant (i.e. carboxylic acid R-COOH). In addition, for the surface complexation model, we studied the model of Dzombak and Morel, which uses Debye Huckel activity coefficients (i.e., valid up to ionic strength I = 0.3 mol/kilogram of water) (SPE-196407-MS).In this contribution (OTC-30301-MS), we use the DLVO-theory and SCM (surface complexation modeling) to create multiple scenarios of smart water (i.e., ionically modified brine) to study its impact on surface complexes during fluid-rock interaction process (i.e., calcite-water interface and oil-water interface). To be specific, we use PHREEQC to simulate and compare two case scenarios; the case of low salinity (NaCl 0.4 mmol/kg-water) and the case of high salinity [NaCl 8500 mMol/liter]. Also, for better optimization of the factors affecting the surface complex modeling, in this work, we modified the model of Dzombak and Morel, by using more accurately activity coefficient given by Pitzer coefficients above (0.3 mol/kg-water) (i.e., valid up to ionic strength I = 6 mol/kg-water). Additionally, the surface charge and the surface complexes are calculated, implemented and built-in using geochemical code PHREEQC. Further input: fraction of sites that bind the carboxylic acids (R-COOH) and bind the carbonates (CaCO3) surface complex are (1.67×10−6) and (4.1 × 10-6) respectivly, and surface area per gram of solid is (1 m2/g) for both the oleic and calcite interface (Hassan, et al., 2020). Moreover, we applied Gibbs rule to determine the number of chemical degrees of freedom. In our case, we have two numbers of degrees of freedom, and its chosen to be pH and ionic strength. Also, we examined the effect of pH and carbon dioxide (CO2)-pressure on the surface complexes (i.e., surface charge and surface potential) for both scenarios (i.e., low salinity [NaCl 0.4 mmol/kg-water] and high salinity [NaCl 8500 mmol/kg-water]) (Hassan, et al., 2020).Qualitatively we can state that, the stability of a water film between the rock-aqueous phase / oil aqueous phase interfaces is resolute by the active sites on carbonate rock (i.e., calcite) and oil. If they with a charge of the same sign, a water film is usually stable. The foam stability is determined by the double layer (charged surface + counter ions in solution) repulsion, which is electrostatic and attractive the Van der Waals forces, which are determined by the dielectric coefficients of the constituting layers. If the electrostatic forces dominate the foam film is considered stable. It is conjectured that, high carbon dioxide pressures have a destabilizing effect on the film for both cases (i.e., low salinity [NaCl 0.4 mmol/kg-water] and high salinity [NaCl 8500 mmol/kg-water]), as they reduce the surface potential. A decreased surface potential leads to a reducing electrostatic double layer repulsion (EDL) and thus destabilizes the stability of the foam film, whereas low salinity leads to less screening of the surface potential and thus improves the stability of the foam film lamellae. The activity coefficients are more accurately given by the Pitzer coefficients above (0.3 mol/kg-water) (i.e., valid up to 6 [mol/kg-water]). It is shown that, manipulating surface complexes by imposing different salinity and pH can help to obtain mixed-wet or oil-wet behavior, with more favorable residual oil saturations, accepting the occurrence of less favorable mobility ratios. Clearly, the choice of optimal conditions is case dependent; if the mobility ratio is already favorable as to be expected with foam flow, mixed-wet conditions are favored with low residual oil saturations. Thus, an optimal choice of the pH that at the same time leads to a stable brine film on the calcite surface, and a stable foam film requires fine tuning.

Oil compressibility (co) plays a vital role in vast aspects ranging from upstream to downstream. For reservoir with pressure below bubble point, the effect of co to the fluid flow is insignificant as it is overshadowed by the presence of large gas compressibility (cg). This study aims to increase the range of applicability and accuracy of the formula used for estimating the co by eliminating the limitations of other existing correlations. A new formula for the estimation of oil compressibility below bubble point pressure is devised using Group Method Data Handling (GMDH). The approach is a combination of neural networks and some high-level statistical methods which rely on generating simple relations among the input parameters and the dependent parameter. The relations then result in eliminating some parameters with low impact on the output. A series of consecutive layers with the link is generated, and polynomial terms are created. A total number of 322 data points were collected from different sources from literature. Systematic trend analysis has been conducted to verify that the proposed GMDH model honours the exact physical behaviour. The new proposed model found to follow the correct trend, which implies its soundness. Besides, a comparative study was carried out using the best available correlations to confirm the significance of the results of the oil compressibility prediction using GMDH. Different statistical analyses have been conducted to verify the robustness of the newly developed model. The statistical analyses showed a positive outcome whereby the proposed model obtained the lowest average absolute percentage relative error of 5.17% and the highest correlation coefficient of 96.8%. The best model tested among the other models has five input parameters and an average absolute percentage relative error of 10.955% and a correlation coefficient of 95.6%. The new approach managed to reduce the curse of dimensionality as four input parameters have found to have a strong dependency on co (solution gas-oil ratio, oil density, reservoir temperature, and reservoir pressure). The new proposed model overcome the limitations described by the locality of some correlations as they depend on data from specific locations.

Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.

Recently, the usage of social media platforms, especially Twitter, for sharing views and experiences toward different products and services among users has increased significantly. Accordingly, extracting and analyzing Twitter data has generated great interest from sentiment analysis researchers. Moreover, we noted the high-accuracy results achieved by the deep learning approach compared to the machine learning approach especially in analyzing a massive amounts of social networks data. In this paper, we present a survey about previous studies on sentiment analysis that used the machine learning, lexicon-based and deep learning approaches on Arabic and English tweets. Moreover, we report results based on both, machine learning and deep learning sentiment analysis approaches on Arabic tweets to extract customers' sentiments of the Saudi telecommunication companies. We also evaluate the impact of using Part of Speech (POS) model and Word Embedding on the performance of deep learning techniques. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) have been utilized to detect the sentiment orientation for a dataset consist of 1098 tweets. Results suggest that deep learning technique with Word Embedding method was promising in terms of accuracy (F1=0.81). Moreover, the results show that applying LR, SVM, and RF using unigram language model is significantly better than using the bigram language representation.

The ever-increasing popularity of Online Social Networks (OSNs) sites for posting and sharing photos and videos has led to unprecedented concerns on privacy violation. The available Online social networking (OSNs) sites offer a limited degree of privacy protection solutions. Most of the solutions focus on conditional access control meaning, allowing users to control who can access the shared photos and videos. This research study attempts to address this issue and study the scenario when a user shares a photo and video containing individuals other than himself/herself (public-level photos and videos). For privacy-preserving, the proposed system intends to support an automated human face recognition and filtering for public-level photos and videos. Our proposed approach takes into account the content of a photo and makes use of face filtering as a strategy to increase privacy while still allowing users to share photos. First, the proposed system automatically identifies a person face frame from a digital image or video. Next, it compares the detected face features to each face vectors stored in the application database. After face recognition step completed, the proposed system filters all un-known persons in the image. Conventual Neural Network (CNN) has been used for face detection step, while deep learning facial embedding algorithms has been used for the recognition. Both have shown high accuracy results in addition to the capability of being executed in real-time. For face filtering, Gaussian algorithm has been used for face blurring as it has been considered a very fast real-time algorithm which allow the user to control the blurring degree. Based on the obtained results after testing the system using three different datasets, we can conclude that our system can detect and recognize the faces in photos and videos using the improved Conventual Neural Network (CNN) for face detection with 91.3% accuracy and K-Nearest Neighbor (KNN) for the face recognition with 96.154% accuracy using I-Privacy dataset.

Disproportionate Permeability Reduction (DPR) is often used as a water-shutoff treatment in production wells when conventional solutions such as mechanical isolations are difficult to perform. Although this property has been well documented by different investigators, the performance of DPR treatments in field applications has varied between success and failure without understandable reasons. This work investigated the DPR performance in different scenarios to see when, where and at which conditions DPR treatments can give better results. Numerical simulation methods were used to simulate different scenarios happening in oil and gas fields such as five-spot pattern system and linear-system, with different number of layers, with and without crossflow. The possibility of using DPR treatment in hydraulically-fractured reservoirs was also investigated since many reports indicated that there is an increase in the water production for some oil and gas reservoirs after being hydraulically-fractured. Moreover, the physical reasoning behind the variations in the DPR performance for different scenarios has been extensively discussed. The results explored that the DPR performance was excellent in both of water-cut reduction and oil-recovery improvement when the flow regime was viscous dominated (viscous-gravity number 10), the effective period of DPR treatment was a short-term remedy. Secondly, when the high-K layer is existed at the lower-zone of oil or gas reservoir, the reservoir would be a good candidate for the DPR treatment as compared with the reservoirs that have the high-K layer located at the upper zone. Furthermore, selecting the correct time to perform DPR treatments generally could have a significant role to mitigate water production. Finally, the dimensions of the treated fracture are the key components to get a successful DPR-treatment in the hydraulically fractured reservoirs.

Summary Fractures and oil-wet conditions significantly limit oil recovery in carbonate reservoirs. Gel treatment has been applied in injector wells to modify the prevailing reservoir streamlines and significantly reduce fracture permeability, whereas low-salinity waterflooding has been applied experimentally to modify rock wettability toward water-wet for improved oil recovery. However, both processes have limitations that cannot be resolved using a single method. The objective of this study was to test whether low-salinity water could enable gel particles to move deeply into fractures to efficiently increase oil recovery and control water production. A semitransparent fracture model of carbonate cores and acrylic plates was built to study the effect of low-salinity waterflooding, fracture width, gel-injection volume, and fracture uniformity on oil recovery and to redirect the flow path to unswept zones. Preformed partial gel (PPG) and brine movements were visible through the model's transparent acrylic plate. Seawater was used for brine flooding and to prepare swollen particles; the seawater was diluted 100 times to create low-salinity water. A light crude oil was used, with 10-cp viscosity. Low-salinity water was injected after gel placement to test the gel-plugging efficiency. The results showed that the low-salinity water could improve gel propagation into the fracture and increase oil recovery because the gel strength (apparent viscosity) decreased as the brine concentration decreased. The gel-injection volume had a significant effect on the oil-recovery factor when seawater flooding followed the gel-injection process, although there was less of an effect when the gel was followed by low-salinity waterflooding. Moreover, the effect of low-salinity waterflooding on gel propagation decreased as the fracture width decreased. In addition, the resulting fracture uniformity illustrates a viable effect of the in-depth water-diversion treatment.

A significant amount of water used during hydraulic fracturing operation in shale reservoirs remains trapped in the formation mainly due to capillary forces. In this paper, the effect of Alpha Foamer® surfactant in enhancing the efficiency of the fracturing fluid was investigated in the presence and absence of Betain C60 as a co-surfactant at various fracturing water salinities. The performance of Alpha Foamer® surfactant was examined by measuring the surface tension and contact angle between the surfactant solutions and methane gas at different pressures and temperatures. The result indicates that Alpha Foamer® has significantly reduced the surface tension between water and methane gas from 69 mN/m to 33.2 mN/m at critical micelle concentration of 0.25 wt%. The addition of Betain C60 as a co-surfactant has slightly reduced the surface tension to 26.6 mN/m. It has also been observed that the increase in the pressure and temperature would be an advantage with additional reduction in the surface tension. In addition, the selected surfactant and co-surfactant concentrations have shown a significant reduction in the contact angle that makes the shale formation strongly water wet. The shifting of the wetting phase due to receding contact angle would aid in desorption of methane gas from shale matrix. The desorption is further enhanced with the reduction of surface tension between fracking fluid and methane gas by surfactant. The findings from this research can be used to optimize fracturing fluid, and to improve the overall sweep efficiency and shale gas productivity as well as water flowback.

Introduction Orthodontic tooth movement results from increased inflammation and osteoclast activation. Since patients of all ages now routinely seek orthodontics treatment, we investigated whether age-dependent biologic responses to orthodontic force correlate with the rate of tooth movement. Methods We studied 18 healthy subjects, adolescents (11-14 years) and adults (21-45 years), with Class II Division 1 malocclusion requiring 4 first premolar extractions. Canines were retracted with a constant force of 50 cN. Gingival crevicular fluid was collected before orthodontic treatment and at days 1, 7, 14, and 28 after the canine retraction. Cytokine (IL-1β, CCL2, TNF-α) and osteoclast markers (RANKL and MMP-9) were measured using antibody-based protein assays. Pain and discomfort were monitored with a numeric rating scale. The canine retraction rate was measured from study models taken at days 28 and 56. Results Although the cytokine and osteoclast markers increased significantly in both age groups at days 1, 7, and 14, the increases were greater in adults than in adolescents. Interestingly, the rate of tooth movement in adults was significantly slower than in adolescents over the 56-day study period. Adults also reported significantly more discomfort and pain. Conclusions Age is a significant variable contributing to the biologic response to orthodontic tooth movement. Adults exhibited a significantly higher level of cytokine and osteoclasts activity but, counterintuitively, had a significantly slower rate of tooth movement.

This paper presents the rheological behaviour of supercritical CO2 (sCO2) foam at reservoir conditions of 1 500 psi and 80 °C. Different commercial surfactants were screened and utilized in order to generate a fairly stable CO2 foam. Mixed surfactant system was also introduced to generate strong foam. Foam rheology was studied for some specific foam qualities using a high pressure high temperature (HPHT) foam loop rheometer. A typical shear thinning behaviour of the foam was observed and a significant increase in the foam viscosity was noticed with the increase of foam quality until 85%. A desired high apparent viscosity with coarse texture was found at 85% foam quality. Foam visualization above 85% showed an unstable foam due to extremely thin lamella which collapsed and totally disappeared in the loop rheometer. Below 52%, a non-homogenous and unstable foam was found having low viscosity with some liquid accumulation at the bottom of the circulation loop. This research has demonstrated rheology of sCO2 foams at different qualities at HPHT to obtain optimal foam quality region for immiscible CO2 foam co-injection process.

Ionic liquid monomers and polymers have recently attracted attention as chemical agents for enhanced oil recovery. Poly (ionic liquids) (PILs) combine both the unique thermal characteristics of ionic liquids and the flexibility and durability of macromolecular architectures creating novel materials with superior characteristics and applications. In this work new hydrophilic, thermal stable and flexible PILs based on 2-acrylamido-2-methylpropane sulfonic acid (AMPS) copolymers with methacrylic acid (MAA), acrylamide (AAM) and N-vinyl pyrrolidone (VP) were prepared, characterized and tested as EOR chemicals. The interaction mechanism of the prepared PILs with crude oil, seawater and reservoir rocks at elevated temperature and/or pressure was explored with different techniques. Among these are the PILs chemical structures, thermal stability, contact angle, zeta potential, adsorption and interfacial tension measurements. All measurements point out to wettability alteration as main recovery mechanism. 2-acrylamido-2-methylpropane sulfonic acid (AMPS) copolymer with MMA was found to be the most efficient among the other PILs and it was used in multiple flooding runs in secondary and tertiary modes. Clearly, recovery was improved with PIL solution and all flooding scenarios provided close recoveries. However, secondary flooding of 0.4 pore volume of PIL followed by continuous seawater was the most feasible since limited quantity is needed to provide the ultimate recovery.

Oil recovery from heavy oil resources has always been a challenging task. This work is aimed at investigating the recovery efficiency of polymer-augmented low salinity waterflooding in heavy oil reservoirs. The main recovery mechanism behind low salinity waterflooding is wettability alteration to more water-wet state. On the other hand, polymer flooding is performed to control fluids mobility and hence improve displacement efficiency. Combining both recovery methods is expected to add to recovery efficiency obtained by individual methods, and the aim of this work is to explore this experimentally. In this study, several laboratory experiments were conducted using Berea and Bentheimer sandstone cores starting with base runs of continuous secondary seawater and tertiary twice and 10 times diluted seawaterflooding (low salinity). Significant incremental oil recovery was obtained when flooded with low salinity water, and 10 times diluted seawater was determined to be used as low salinity water. Contact angle and Zeta potential measurements indicate wettability alteration due to clay detachment as the main recovery mechanism. Synergy of water secondary flooding and polymer tertiary flooding at different water salinity levels proved the efficiency of hybrid low salinity polymer flooding process in Berea sandstone. Low water salinity during secondary injection mode played a major role on ultimate recovery with less contribution to tertiary polymer slug injection. High salinity waterflooding provided lower secondary recovery leaving more residual oil for polymer slug to act on at that cycle. Smaller polymer slug of 0.1 pore volume was found to be sufficient in tertiary flooding with low salinity water but with slightly slower recovery rate. Bentheimer sandstones known for its low clay content were subjected to polymer-augmented waterflooding at high and low salinity levels. Close secondary and tertiary recoveries were obtained for the two salinity levels with slower recovery rate for low salinity run. Minute clay content and water-wet characteristic as determined by contact angle measurements may explain the lack of water salinity effect on recovery. The lack of salinity role and the two shock fronts with connate water bank in between known to exist during low salinity flooding may explain the slower recovery rate encountered. Comparison of both sandstones indicates that less ultimate recovery from Berea rocks, and this can be attributed to their initial intermediate wet state in contrast to the water-wet Bentheimer sandstone rocks.

In the last few years, Twitter becomes the most popular platform for individuals to share their experiences and viewpoints towards different products and services. Therefore, it attracts a lot of researchers to use it as a body for sentiment analysis and opinion mining research studies. Most of the previous research studies in this area have been using the traditional machine learning-based and lexicon-based approaches more compared to the deep learning approach to classify the emotional states of English tweets. Also, there is a shortage of research studies that categorize the opinion orientations of tweets in other languages such as Arabic. Recently, deep learning approach has achieved remarkable results over the traditional machine learning algorithms in analyzing a massive amount of data as the case with social networks data. In this research study, we seek to discuss the state-of-the-art of sentiment analysis methodologies used to classify tweets' sentiment orientation and challenges that need to be addressed. Also, this paper provides an overview of deep learning approach and question if this approach can be adopted to improve the classification accuracy of sentiment analysis for both English and Arabic tweets.

The recovery from fractured reservoirs is usually low. The areal heterogeneity is one result of the fractured reservoir. Low salinity waterflooding (LSWF) and preformed particle gel (PPG) have recently drawn great interest from the oil industry. LSWF can only increase displacement efficiency, and it has little or no effect on sweep efficiency whereas PPG can plug fractures and improve sweep efficiency, but they have little effect on displacement efficiency. The coupled method bypasses the limitations of each method when used individually and improves both displacement and sweep efficiency. The main objective of this study was to determine whether the coupling technologies can improve conformance control in fractured sandstone reservoirs. Before the study was conducted, the effects of low salinity waterflooding, number of fractures, and PPG strength were studied. The PPG was injected into the fracture at a flow rate of 2.0 ml/min. Brine was injected at a different flow rate after PPG placement to test the effect of flow rate on the PPG's plugging efficiency. Laboratory experiments showed that the oil recovery factor and the Frrw increased when the concentration of injected brine changed from conventional salinity to low salinity and the areal sweep efficiency was improved. However, the PPG extruded pressure decreased when the PPG swelled in a low-brine concentration. At a high-flow rate, there was no significant effect on the Frrw. Coupling two different EOR technologies can improve displacement and sweep efficiency and, in turn, enhance conformance control.

Objectives Vibration, in the form of high frequency acceleration (HFA), stimulates alveolar bone formation under physiologic conditions and during healing after dental extractions. It is not known if HFA has an anabolic effect on osteoporotic alveolar bone. Our objective is to determine if HFA has a regenerative effect on osteoporotic alveolar bone. Methods and materials Adult female Sprague-Dawley rats were divided into five groups: 1) Ovariectomized Group (OVX), 2) Sham-OVX Group that received surgery without ovariectomy, 3) OVX-HFA Group that was ovariectomized and treated daily with HFA, 4) OVX+Static Force Group that was ovariectomized and received the same force as HFA, but without vibration, and 5) Control Group that did not receive any treatment. All animals were fed a low mineral diet for 3 months. Osteoporosis was confirmed by micro-CT of the fifth lumbar vertebra and femoral head. HFA was applied to the maxillary first molar for 5 minutes/day for 28 and 56 days. Maxillae were collected for micro-CT, histology, fluorescent microscopy, protein and RNA analysis, and three-point bending mechanical testing. Results Micro-CT analysis revealed significant alveolar bone osteoporosis in the OVX group. Vibration restored the quality and quantity of alveolar bone to levels similar to the Sham-OVX group. Animals exposed to HFA demonstrated higher osteoblast activity and lower osteoclast activity. Osteogenic transcription factors (RUNX2, Foxo1, Osterix and Wnt signaling factors) were upregulated following vibration, while RANKL/RANK and Sclerostin were downregulated. HFA did not affect serum TRAcP-5b or CTx-1 levels. The osteogenic effect was highest at the point of HFA application and extended along the hemimaxillae this effect did not cross to the contra-lateral side. Conclusions Local application of vibration generated gradients of increased anabolic metabolism and decreased catabolic metabolism in alveolar bone of osteoporotic rats. Our findings suggest that HFA could be a predictable treatment for diminished alveolar bone levels in osteoporosis patients.

This paper attempts to model the futures term structures of crude oil and natural gas using the notion of convenience yield in a regime switching framework. Unlike the existing studies, which assume the convenience yield to have either a constant value or to have a stochastic behavior with mean reversion to one equilibrium level, the model of this paper extends the Gibson and Schwartz (1990) model to allow for regime switching in the convenience yield along with the other parameters. A closed form solution for the futures price is derived and the model parameters are estimated using the maximum likelihood method. The results show that the estimated regimes are very close to the contango and backwardation regimes commonly seen in futures markets. The results also show that the transitional probabilities play an important role in shaping the futures term structure implied by the model.

Recently, we proposed a chemical mechanism for the role of clay in sandstone during low salinity water (LS water) flooding using acetate to simulate the oil-bound carboxylic acids with the reservoir (Al-Saedi and Brady 2018). In this study, we propose to quantify the control of mineral composition and water chemistry on water/rock interactions by using oil during low salinity waterflooding of spatially heterogeneous sandstone porous media. A sandstone core and free-clay sand core were prepared in chromatography columns and were water flooded sequentially with high-low salinity water at the reservoir temperature of 90ºC. Brine with high salinity (100,000) ppm was injected to simulate formation water. Then, Inflow LS water (1100) ppm. One of the columns only contains quartz, to investigate the role of the clay in the mechanism of LS water enhanced oil recovery (EOR). The other columns contained quartz and different quantities of clays. The results showed that during flooding, the free-clay column with LS water the oil recovery from the quartz column was as great as that of the clay-containing cores. On the other hand, our previous investigation showed a noteworthy desorption of Ca2+ from the free-clay core surface. That indicates further RCOO- detachment in the absence of clay. The pH also increased significantly during LS water flooding for the free-clay column.

The oil recovery from fractured reservoirs is usually low and the early water breakthrough is one result of the fractured reservoir. Two enhanced oil recovery (EOR) methods, low salinity water flooding (LSWF) and micro-gel, have recently drawn great interest from the oil industry. We integrated both methods into one process. The objective of this study was to test how the combining method could reduce production rates from the fractured layer and improve the production rate from the non-fractured layer through laboratory experiments. The parallel non-crossflow models, which were made of sandstone cores and two core holders, were built. The gel strength, water salinity, gel particle size, and sandstone permeability effects on oil recovery, production rate, and gel penetrations were studied. Sodium chloride (NaCl) was used for brine flooding and preparing swollen PPG. Two brine concentrations (1.0, and 0.01 wt. % NaCl) were used for the experiments. A light mineral oil was used. A flow rate of 0.5 ml/min. was used to inject the brine into the parallel non-crossflow models before and after the gel was placed into the fracture. The gel was injected into Outlet 1 to fill out the fracture at a flow rate of 2.0 ml/min until the gel injection pressure reached 500 psi. The optimum water salinity for the application of the coupled method was determined through laboratory experiments to be 0.01 wt. % NaCl because this gave the highest gel penetration into the partially fractured layer. However, the best water shut-off was obtained when the water salinity was 1.0 wt. % NaCl. The incremental oil recovery was almost the same in both 1.0 and 0.01 wt. % NaCl water salinity. When the gel particle size decreased, the water shut-off from the partially- closed fracture layer and the incremental oil recovery from the matrix were increased. This application had no significant effect when the core permeability was low because there was not much of an effect on the production rate ratio after gel treatment. From the laboratory experiments, we concluded that the PPG application might be more successful it is swelled in a low brine concentration.

The latest oil price decline simply increases the demand for enhanced oil recovery (EOR) and pushes research developers to keep improvements in oil recovery. The goal is always to recover as much oil as possible at the lowest possible cost. Low-salinity water flooding (LSWF) is an EOR method that operates at a lower cost than other EOR methods, which makes it a preferred area of interest for oil industry economists, who continue to call for EOR costs to come down. The objective of this study was to test the ability of low-salinity waterflooding to improve oil recovery from low permeability sandstone reservoirs. Four types of tests were conducted: imbibition, interfacial, core flooding, and zeta potential tests. Three key factors were studied: salinity of the injected water, type of salt, and aging time. Their influence on the amount of oil recovery, stabilized injection pressure, pH, and permeability reduction was determined. Berea sandstone was used in all experiments. Sodium chloride (NaCl) and calcium chloride (CaCl2) were used to prepare the brine. The imbibition test and core flooding results showed that the oil recovery increased as brine concentration decreased for both sodium chloride and calcium chloride. Sodium chloride resulted in higher oil recovery than calcium chloride at a certain salinity in both imbibition and core flooding tests. The oil recovery factor results during the second water flooding cycle (after aging for 24 hrs.) showed more oil recovered during low salinity waterflooding. The stabilized inaction pressure was higher for CaCl2 than NaCl injection at certain flow rate and brine concentrations. Effluent pH values became more basic during low salinity water flooding for both sodium and calcium chloride. The zeta potential results showed that decreasing the salinity of injected water resulted in a decrease of the zeta potential value for both injection cycles, before and after aging for 24 hours. Results also imply Low- salinity water flooding redistributes the flowing paths by releasing sand particles and some fine minerals causing the flow path to narrow. Thus, low salinity water flooding can create a new streamline (fluid flow diversion) and improve both displacement and sweep efficiency.

Disproportionate Permeability Reduction (DPR) is often used as a water-shutoff treatment in production wells when conventional solutions such as mechanical isolations are difficult to perform. Although this property has been well documented by different investigators, the performance of DPR treatments in field applications has varied between success and failure without understandable reasons. This work investigated DPR performance in different scenarios to see when, where and at which conditions DPR treatments can give better results. Numerical simulation methods were used to simulate different scenarios happening in oil and gas fields such as five-spot pattern system and linear-system, with different number of layers, with and without crossflow. The possibility of using DPR treatment in hydraulically-fractured reservoirs was also investigated since many reports indicated that there is an increase in water production after some oil and gas reservoirs being hydraulically-fractured. Moreover, the physical reasoning behind the variations in DPR performance for different scenarios has been extensively discussed. The results explored that DPR performance was excellent in both of water-cut reduction and oil-recovery improvement when the flow regime was viscous dominated (viscous-gravity number10), the effective period of DPR treatment was short-term remedy. Secondly, when the high-K layer is existed at the lower-zone of oil or gas reservoir is a good candidate for DPR treatment as compared when the high-K layer located at the upper zone. Furthermore, selecting the correct time to perform DPR treatments generally has a significant role to mitigate water production. Finally, the dimensions of treated fracture are the key components to get a successful DPR-treatment in fractured reservoirs.

With the rapid growth of microblogging sites like Twitter, a wide range of spam activities are evolving at an equal pace. Detecting spam is a major problem in these days in social network sites. This paper explores spam URLs detection in Twitter by providing malicious behavior categories, detection evasion tactics, features used for detection, detection techniques and their limitations (if any). We further investigate the best performance exists by machine learning classification based on various published features. Therefore, we utilized four classifiers on a Twitter accounts dataset of 10713 users labeled 5358 benign and 5355 spam with 17 robust features, the feature are content-based and user-based. Our result shows that among the four classifiers the Random Forest classifier with hybrid-based features methods produce the highest evaluation with 96.4% accuracy, while J48 classifiers achieved a slightly different accuracy rate of 94.5%.

Social media platforms such as Twitter, Facebook and Blogs are growing enormously in terms of the number of users. Due to the rapid growth of the data produced from social media users, these platforms are becoming one of the most valuable data sources. Social media became outlets for people to express their thoughts, opinions, and emotions in a real-time manner. Therefore, Social media data have been utilized in literature for many sentiment analysis research. Although there have been many types of research on the sentimental analysis in English, the amount of Arabic-based sentiment analysis studies and tools are still limited. In this paper, we reviewed selected literature related to sentiment analysis in order to present a staged road map of Arabic tweets sentiment analysis in favor of measuring the happiness levels in Saudi Arabia cities. We reported the methodology to numerically represent the happiness of each city by determining the sentiment of 2000 geo-tagged tweets in Saudi Arabia using machine learning techniques. Expected results would not only rank the happiness levels for the cities in Saudi Arabia but certainly identify activities or circumstances that contribute to citizens' happiness. These expected results later can be used by institutions of related interest such as General Entertainment Authority to help planning new activities and attractions.

Numerous quantitative and qualitative methods have been presented to measure wettability. The most well-known methods are Amott-Harvey and U.S. Bureau of Mines. The Amott method describes how the wetting phase displaces the nonwetting phase spontaneously; the main problem with this method insensitivity near neutral wettability. Another problem with this method is that imbibition can take several hours to more than two months to complete. The most important benefit of USBM that differs from the Amott method is the sensitivity close to neutral wettability, but the disadvantage is that USBM cannot recognize if the reservoir has mixed wettability or not, though Amott can. We come up with a method to measure sandstone wettability only by Ca2+ and Br− chromatographic separation according to the method described by Strand et al. (2006) on a chalk core. Three sister cores were pre-aged in formation water without Ca2+ and Br−, and the cores were then aged in oil for three weeks at 95°C. The cores were then flooded with the same formation water until Sor was established. Core#1 was flooded with high salinity water (~117,000 ppm) containing identical concentrations of Ca2+ and Br− (89 μmole). Core#2 was flooded with low salinity water d30HSW. Core#3 was sequentially flooded with HS and LS water to investigate the wettability alteration in the same core. All experiments were conducted at 25 and 70°C to examine the effect of temperature on wettability alteration by the new method. The effluents were collected by a fraction collector for chemical analysis for Ca2+ and Br−, divided by the inlet concentration of Ca2+ and Br−, and then plotted with injected pore volume (PV). The area between the Ca2+ and Br− curves was calculated (Ao). Core#4 was pre-aged in heptane in order to establish water-wet sandstone. The heptane was displaced from the core by the same formation water until residual heptane saturation was reached. The same HS water was injected into Core#3. The effluents were analyzed using the same method as for Core#1, 2. The area between the two curves was also determined using the same method as for Core#1, 2 (AH). The wettability index was then calculated by dividing Ao by AH. The wettability index ranged from 0 for strongly oil-wet to 1 for strongly water-wet and 0.5 for intermediate wettability. Another core was sequentially flooded by HS and LS water to further investigate the wettability alteration by LS water and to verify our new method. The divalent cation Ca2+ was considered as the most potential ion towards sandstone when injecting low salinity water to sandstone. An ion exchange occurred between Ca2+ and H+ during flooding which is the key point for wettability alteration, and in turn, increases oil recovery. Bromine is a tracer that has no potential to the sandstone surface area. Thus, the area between Ca2+ and Br− is proportional directly to the water-wet surface site in sandstone (i.e., both Ca2+ and Br− contact the same water-wet surface area).

Gel treatment is often used to control the excessive water production in fractured reservoirs. Combined technology has drawn a great interest in the oil industry over the last decade. Recently, gel treatment combined with low salinity waterflooding brought a significant attention in reservoir engineering. It has been reported that low salinity waterflooding can improve displacement efficiency. Gel treatment has been proved to plug fractures and improve sweep efficiency. The objective of this paper is to provide a deeper understanding of this combined technology by applying experimental and simulation approach (sensitivity analysis). The purpose of sensitivity analysis is to determine how sensitive the oil recovery factor obtained at different conditions to different parameters. The experimental results found that the low-salinity water can improve oil recovery factor by 6% and plugging efficiency was also improved. The sensitivity analysis results showed that the initial oil saturation, oil viscosity, porosity, and matrix permeability were the most important parameters affecting improved oil recovery when combined technology applied. The matrix permeability was the most important parameter impacting the obtained oil recovery, and the oil saturation was the lowest. The performance of using low-salinity water increased when matrix permeability increased, and porosity decreased. Identifying the parameters which have a significant impact on oil recovery would give a good prediction for combined technology success or failure depending on reservoir properties.

This investigation was one part of a study that involved modelling the credibility of Arabic microblogs with disagreed judging credibility labels. We investigated the hypothesis that the most similar and agreed microblogs credibility judges use the same Twitter credibility features to evaluate tweet messages. First, the most similar labelers were identified using different similarity and agreement measures. Then, we used their assigned credibility labels to examine Twitter content and author features distributions. We found that similar and agreed labelers did not continuously correlate with assigning credibility judgments labels harnessing the same Twitter features. Shared similar features between labelers mainly appeared with the most agreed labelers using Krippendorffs alpha coefficient measure resulting in close to 100% features similarity for only the low-credibility class.

The recovery from fractured reservoirs is usually low. The areal heterogeneity is one result of the fractured reservoir. Low-salinity waterflooding (LSWF) and preformed particle gel (PPG) have recently investigated by some reserchers. The main objective of this study was to determine whether the combining technologies can improve conformance control in fractured sandstone reservoirs. Semi-transparent five-spot models made of sandstone cores and acrylic plates were built. The effect of low-salinity waterflooding on oil recovery was studied. Models were designed with three parallel open fractures. Sodium chloride (1.0, 0.1, 0.01, and 0.001 wt. % NaCl) were used for brine flooding and 1.0 wt. % NaCl for preparing swollen PPG. Two microgel concentrations, 2000 PPM and 5000 PPM, with 850 micrometer particle size were used. Three cycles of low-salinity water were injected after the second conventional brine injection was completed. Oil recovery factor, water cut, injection pressure, microgel extruded pressure, fracture pressure (Pf), monitoring pressure (P1 and P2), and water residual resistance (Frrw) were measured. The interwell connectivity also was investigated. Laboratory experiments showed that the oil recovery factor, injection pressure, and the Frrw increased when the concentration of injected brine changed from conventional salinity to low salinity and the areal sweep efficiency was improved. The tortuous wormholes resulted highrer oil recovery than the straight wormholes and the oil recovery decreased as number of fratures and fracture width increased. The microgel concentration had a signicicant effect on plugging effeciecny; therefore, the 5000 PPM Microgel showed higher plugging efficiency than 2000 PPM. The result showed the interwell connectivity between the injector and producer decreased when low salinity waterflooding applied and increased the interwell connectivity between the injector and the monitoring points. The plugging efficiency, stabilized injection pressure, fracture pressure, monitoring pressure, and water residual resistance factor—all increased when the salinity of injected water decreased. Furthermore, the microgel strength decreased as brine concentration decreased. However, lower salinity water caused the incremental oil recovery factor to increase. Thus, there is a limitation in increasing the plugging efficiency under low salinity water. This limitation occurred when the brine injection pressure was less than the PPG’s extruded pressure. Combining two different EOR technologies can improve displacement and sweep efficiency and, in turn, enhance conformance control.

To evaluate credibility of Arabic content in Twitter, a corpus of Arabic microblogging messages was required with its labelled credibility ratings in order to build the credibility model. Since no Arabic dataset existed, we confronted this problem by building a novel human annotated Arabic Twitter corpus that could be used for further research. This paper identifies the collection process and the characteristics of the newly created dataset. It presents basic analysis of submitted credibility rating values and the collected labelers’ data. A number of statistical graphs are exhibited to examine labelers’ traits and its impact on credibility perceptions. Results showed that both: labelers’ data and method of labeling presentation have a slight impact on the perception of credibility. The results presented in this paper covers the first stage from a large project aims at predicting credibility of Arabic Twitter messages in the presence of disagreed judging credibility scores.