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The development of economic activities and social progress index leads to the governmental considerations for the environmental challenge's issues. The Green Credit Policy (GCP) in China for manufacturing, as a part of a sustainable finance package, initiatives restrictions with suppliers to reduce harmful pollution for the environment. The study mainly validates the impact of GCP on manufacturing for diminishing the emerged pollution to the environment. The study develops Neutrosophic Multiple-Criteria Decision-Making Framework (N-MCDMF) according to neutrosophic theory and various MCDM methods of grey relational analysis (GRA), analytic network process (ANP), the Decision-Making Trial and Evaluation Laboratory technique (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to support the decision-makers with highly systematic procedures in the uncertain and inconsistent environmental conditions. The N-MCDMF evaluates the conditions of GCP and recommends the optimal Supply Chain Management (SCM) in manufacturing alternatives. A case study is presented for the validation of the issues of applicability and flexibility for the proposed N-MCDMF. The results obtained from the implementation of the N-MCDMF indicates the applicability and flexibility of the proposed approach. In addition, results show that SCM in manufacturing can provide more cooperation for the environment to reduce harmful pollution and to attain sustainability for achieving motivations under the restrictions of GCP.

The growth of Multiprocessing Systems (MPS) has become a necessity for dealing with complex tasks and speeding up their execution. Increasing the number of processing cores on a single chip produces a vast processing power, but the biggest obstacle is the energy generated from these cores. The traditional techniques guarantee to get the optimal schedule, but they are costly in terms of time and memory storage. In this paper, we propose an Improved Whale Algorithm (IWA) to allocate the dependent tasks in MPS with two objectives minimizing the energy consumption and the makespan. The processing cores are assumed to support Dynamic Voltage and Frequency Scaling (DVFS) as an effective technique to reduce energy. The allocation of tasks in MPS is an NP-hard problem. Inadequate scheduling of tasks can result in consuming energy. Also, the failure to complete the tasks before their predetermined deadlines is a critical issue for real-time applications. We consider three different sources of energy coming from the communication, idle, and active states of the processing cores. We use an initialization procedure to produce a population of candidate schedules that respects the precedence among tasks. In IWA, we employ two different discretization methods to map the continuous values into discrete ones. Two specialized crossover operations are adopted to boost the quality of the candidate schedules while respecting the dependencies among the tasks. IWA implements the Load Balancing Improvement (LBI) strategy to alleviate the load of tasks from heavy cores. LBI plays a vital role in decreasing the static energy consumption. We compare IWA with the other algorithms, and the results show the superiority of IWA.

The trend of considering supply chain sustainability with an absence of attention to sustainability risks may disturb the business future. The role of risk management is concentrated in identifying and analyse the influence of loss to business, social and environment, get ready by coverage budget, and derive strategies to protect supply chain sustainability against these risks. Risk management assists the company’s performance to be more confident in supply chain sustainability decisions. The extent of the risk is based on the organization’s magnitude, so the sustainable supply chain risk management strategies of large firms require to be more advanced. The purpose of this research is the estimation of sustainable supply chain risk management (SSCRM). The proposed methodology in this paper is a combination of plithogenic multi-criteria decision-making approach based on the Technique in Order of Preference by Similarity to Ideal Solution (TOPSIS) and Criteria Importance Through Inter-criteria Correlation (CRITIC) methods. In order to evaluate the proposed model, we present a real-world case study of the Telecommunications Equipment Company. The results show the importance of each criterion to evaluate SSCRM and the ranking of the three telecommunications equipment categories.

Scheduling projects are very important topic in project management science. Constrained resources project scheduling problem CRPSP is a problem of the purpose of allocating the available resources to specific tasks or activities for achieving specific objectives or purposes such as minimising the makespan or time of the projects, minimising the execution cost of the project, or any other specific objective or more than one objective at the same time (multi-objectives resource constrained project scheduling problems). This paper introduces a survey for procedure scenarios, techniques, and models that are considered the main context history of CRPSP and multi-mode constrained resource project scheduling problems MMCRPSP. It aims to exhibits, highlights, and update the recent CRPSP surveys. The current state of art for recent researches is evaluated and the potential research directions and orientations are pointed. Also a new framework is proposed for the researchers of interest for this domain of research.

Because of their high dimensionality, dealing with large datasets can hinder the data mining process. Thus, the feature selection is a pre-process mandatory phase for reducing the dimensionality of datasets through using the most informative features and at the same time maximizing the classification accuracy. This paper proposes a new Grey Wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods. The sigmoid function is used to transform the continuous search space to the binary one in order to match the binary nature of the feature selection problem. The two-phase mutation enhances the exploitation capability of the algorithm. The purpose of the first mutation phase is to reduce the number of selected features while preserving high classification accuracy. The purpose of the second mutation phase is to attempt to add more informative features that increase the classification accuracy. As the mutation phase can be time-consuming, the two-phase mutation can be done with a small probability. The wrapper methods can give high-quality solutions so we use one of the most famous wrapper methods which called k-Nearest Neighbor (k-NN) classifier. The Euclidean distance is computed to search for the k-NN. Each dataset is split into training and testing data using K-fold cross-validation to overcome the overfitting problem. Several comparisons with the most famous and modern algorithms such as flower algorithm, particle swarm optimization algorithm, multi-verse optimizer algorithm, whale optimization algorithm, and bat algorithm are done. The experiments are done using 35 datasets. Statistical analyses are made to prove the effectiveness of the proposed algorithm and its outperformance.

This paper demonstrates the production optimization methodology being used by Kuwait Oil Company to increase production in one of its giant and mature fields by 18% over 5.0 years. Production optimization is a continuous iterative process to improve production, especially in mature fields. The North Kuwait Redevelopment team has adopted an integrated enhanced and structured process to identify opportunities for production optimization with a pro-active approach focusing on flowing wells and rig-less interventions to tackle production challenges and achieve production targets Typical mature-field challenges are present. These include water flooding, produced water management, artificial lift, with more than 1000 (vertical, deviated and horizontal) active wells, coupled with high structure complexity and stratigraphic heterogeneity, tight sandstone and carbonate reservoirs, shortage of work-over rigs. These challenges make the field appropriate to apply the production optimization methodology outlined here. The Heterogeneity Index (HI) process is utilized to rapidly demonstrate production gain opportunities, for a giant mature North Kuwait Sabria field of approximately 1000 wells produce from different reservoirs. The HI process provided a quick screening method of identifying preliminary candidate wells with anomalous behavior (over/under performance) for further analysis and most importantly, provided the foundation for the overall structured production approach. The results from this screening tool were utilized to identify the families of type productivity problems at field and well levels with solution categories for production enhancement. Representative wells were selected for detailed diagnostics based on the relevance and size of productivity impact and the potential of its well deliverability. Once a few "top potential" wells were identified, production engineering workflows were implemented to assess and forecast the potential of production increase and to determine and evaluate the best intervention action. This production optimization workflow is done in a consistent cycled process considering the ageing condition of the mature field, and the aforementioned challenges. Approximately 35% of the wells have been selected for further analysis over multiple production optimization cycles spanning approximately 5.0 years. A detailed production engineering workflow provided recommendations of various remedial intervention solutions to improve well production potential via productivity enhancement, water shut-off/conformance, stimulation, additional and/or re-perforations, and Artificial lift optimization. Other advanced technologies were applied to improve various strategies, including completions, perforation, stimulation, and injection/production control. To date (Febrauary, 2018), recommendations for approximately 30% (300) wells have been executed, with a significant oil gain of approximately 18 % of the total field production.

The quadratic assignment problem (QAP) has considered one of the most significant combinatorial optimization problems due to its variant and significant applications in real life such as scheduling, production, computer manufacture, chemistry, facility location, communication, and other fields. QAP is NP-hard problem that is impossible to be solved in polynomial time when the problem size increases, hence heuristic and metaheuristic approaches are utilized for solving the problem instead of exact approaches, because these approaches achieve quality in the solution in short computation time. The objectives of this paper are to describe QAP in details showing its types, nature of the problem, complexity of the problem, importance, and simple example. QAP formulations, problems related with QAP, solution techniques, QAP benchmark instances, applications of QAP, survey of QAP researches are also illustrated.

Metaheuristic algorithms are computational intelligence paradigms especially used for sophisticated solving optimization problems. This chapter aims to review of all metaheuristics related issues. First, metaheuristic algorithms were divided according to metaphor based and non-metaphor based in order to differentiate between them in searching schemes and clarify how the metaphor based algorithms simulate the selected phenomenon behavior in the search area. The major algorithms in each metaphor subcategory are discussed including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Water Waves Optimization (WWO), Clonal Selection Algorithm (CLONALG), Chemical Reaction Optimization (CRO), Harmony Search (HS), Sine Cosine Algorithm (SCA), Simulated Annealing (SA), Teaching–Learning-Based Optimization (TLBO), League Championship Algorithm (LCA), and others. Also, some non-metaphor based metaheuristics are explained as Tabu Search (TS), Variable Neighborhood Search (VNS). Second, different variants of metaheuristics are categorized into improved metaheuristics, adaptive, hybridized metaheuristics. Also, various examples are discussed. Third, a real-time case study “Welded Beam Design Problem” is solved with 10 different metaheuristics and the experimental results are statistically analyzed with non-parametric Friedman test in order to estimate the different performance of metaheuristics. Finally, limitation and new trends of metaheuristics are discussed. Besides, the chapter is accompanied with literature survey of existing metaheuristics with references for more details.

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules.

Every organization seeks to set strategies for its development and growth and to do this, it must take into account the factors that affect its success or failure. The most widely used technique in strategic planning is SWOT analysis. SWOT examines strengths (S), weaknesses (W), opportunities (O) and threats (T), to select and implement the best strategy to achieve organizational goals. The chosen strategy should harness the advantages of strengths and opportunities, handle weaknesses, and avoid or mitigate threats. SWOT analysis does not quantify factors (i.e., strengths, weaknesses, opportunities and threats) and it fails to rank available alternatives. To overcome this drawback, we integrated it with the analytic hierarchy process (AHP). The AHP is able to determine both quantitative and the qualitative elements by weighting and ranking them via comparison matrices. Due to the vague and inconsistent information that exists in the real world, we applied the proposed model in a neutrosophic environment. A real case study of Starbucks Company was presented to validate our model.

One of the most significant competitive strategies for organizations is sustainable supply chain management (SSCM). The vital part in the administration of a sustainable supply chain is the sustainable supplier selection, which is a multi-criteria decision-making issue, including many conflicting criteria. The valuation and selection of sustainable suppliers are difficult problems due to vague, inconsistent and imprecise knowledge of decision makers. In the literature on supply chain management for measuring green performance, the requirement for methodological analysis of how sustainable variables affect each other, and how to consider vague, imprecise and inconsistent knowledge, is still unresolved. This research provides an incorporated multi-criteria decision-making procedure for sustainable supplier selection problems (SSSPs). An integrated framework is presented via interval-valued neutrosophic sets to deal with vague, imprecise and inconsistent information that exists usually in real world. The analytic network process (ANP) is employed to calculate weights of selected criteria by considering their interdependencies. For ranking alternatives and avoiding additional comparisons of analytic network processes, the technique for order preference by similarity to ideal solution (TOPSIS) is used. The proposed framework is turned to account for analyzing and selecting the optimal supplier. An actual case study of a dairy company in Egypt is examined within the proposed framework. Comparison with other existing methods is implemented to confirm the effectiveness and efficiency of the proposed approach.

The main objective of this research is to study the integration of Analytic Hierarchy Process (AHP) into Delphi framework in neutrosophic environment and present a new technique for checking consistency and calculating consensus degree of expert’s opinions. In some pragmatism bearings, the experts might be not able to assign deterministic evaluation values to the comparison judgments due to his/her confined knowledge or the differences of individual judgments in group decision making. To overcome these challenges, we have used neutrosophic set theory to handle the integration of AHP into Delphi framework, where each pairwise comparison judgment is symbolized as a trapezoidal neutrosophic number. The power of AHP is enhanced by adding Delphi technique, since it can reduce noise which result from focusing on group and/or individual interests rather than concentriciting on problem disband and it also increase consensus degree about ideas. Obtaining a consistent trapezoidal neutrosophic preference relation is very difficult in decision making process because in creating a consistent trapezoidal preference relation; each expert should make $$\frac{{n \times \left( {n - 1} \right)}}{2}$$ consistent judgments for $$n$$ alternatives. When the number of alternatives is increasing, the workload of giving judgments for the experts is heavy and it makes them tired and leads to inconsistent judgments. In the proposed model,experts will focus only on $$\left( {n - 1} \right)$$ restricted judgments and this also enhances the performance of AHP over the traditional version that is proposed by Saaty. A real life example is developed based on expert opinions about evaluation process of many international search engines. The problem is solved to show the validation of the suggested method in neutrosophic path.

In recent years, North Kuwait has implemented advanced drilling practices by sidetracking existing wells and drilling long horizontal sections through the reservoir, which are completed with smart completions. These often include up to 20 stages, each of which is isolated using swellable packers. Management of production from these long horizontal sections through these complex completions is highly cost-sensitive and maintaining an optimum production rate is key to the economic success of these type of wells, especially in the current low oil price environment. In this case study the well was completed with 18 compartments with ICD's/sand screens and each isolated with swell packers(Fig-3). Production decline was dramatic and in three months since bringing the well on production, the gross production decreased by 50%. Quick diagnosis of the problem was crucial to understand the decline mechanism especially the production from each compartment along the horizontal section, determine what key operational steps could be taken to improve the production and then implement this as soon as possible. Survey was conducted using memory logging tools, High Precision Temperature and Spectral Noise Logging (HPT-SNL), which is a unique data collection and reservoir analysis service, to evaluate the contributing segments along the horizontal lateral. These tools were conveyed on coiled tubing, and the well was kept producing during the 2-day logging programto prevent any production deferment. A detailed reservoir analysis was completed, with the conclusion that out of the 18 oil production intervals, a total of 5 were either poor contributors or not contributing to flow. Based on the results, a stimulation program was designed and implemented targeting these 5 non-productive ICD intervals. Following the stimulation program, the well was re-tested with significant increase in production. A sustained incremental oil production of 180 BOPD was achieved. Identifying the stimulation opportunity in thisunder-performing low rate horizontal well with smart completion and ESP assisted production and implementing the stimulation has been achived riglessly with the use of HPT/SNL tool. Critical to this identification process, was the installation of Y-tool to facilitate deployment of the Logging suite. Additional benefits gained from this data acquisition were the ability to selectively target zones for stimulation, instead of treating the entire reservoir. Also, the entire operation was conducted with minimum well shut in time. The highlights of the whole operation wereminimum production deferment, major operational cost savings and ultimately return the well to an increased level of production.

In this paper, we introduce the integer programming in neutrosophic environment, by considering coefficients of problem as a triangulare neutrosophic numbers. The degrees of acceptance, indeterminacy and rejection of objectives are simultaneously considered. The Neutrosophic Integer Programming Problem (NIP) is transformed into a crisp programming model, using truth membership (T), indeterminacy membership (I), and falsity membership (F) functions as well as single valued triangular neutrosophic numbers. To measure the efficiency of the model, we solved several numerical examples.

The 0–1 knapsack problem (0–1KP) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays significant roles in many real life applications. Dragonfly algorithm (DA) a novel swarm intelligence optimization algorithm, inspired by the nature of static and dynamic swarming behaviors of dragonflies. DA has demonstrated excellent performance in solving multimodal continuous problems and engineering optimization problems. This paper proposes a binary version of dragonfly algorithm (BDA) to solve 0–1 knapsack problem. Experimental results have proven the superior performance of BDA compared with other algorithms in literature.

Supply chain sustainability has become one of the most attractive decision management topics. There are many articles that have focused on this field presenting many different points of view. This research is centred on the evaluation of supply chain sustainability based on two critical dimensions. The first is the importance of evaluation metrics based on economic, environmental and social aspects, and the second is the degree of difficulty of information gathering. This paper aims to increase the accuracy of the evaluation. The proposed method is a combination of quality function deployment (QFD) with plithogenic aggregation operations. The aggregation operation is applied to aggregate: Firstly, the decision maker’s opinions of requirements that are needed to evaluate the supply chain sustainability; secondly, the evaluation metrics based on the requirements; and lastly, the evaluation of information gathering difficulty. To validate the proposed model, this study presented a real world case study of Thailand’s sugar industry. The results showed the most preferred and the lowest preferred metrics in order to evaluate the sustainability of the supply chain strategy.

Many companies have observed the significant benefits they can get via using internet. Since then, large companies have been able to develop business transactions with customers at anytime, anywhere, and in relation to anything, so that we now need a more comprehensive concept than the internet. This concept is the Internet of Things (IoT). IoT will influence decision making style in various phases of selling, buying and marketing process. Therefore, every individual and company should know precisely what IoT is, and how and why they should incorporate it in their operations. This motivated us to propose a smart system based on IoT to help companies and marketers make a powerful marketing strategy via utilizing obtained data from IoT devices. Not only this, but the proposed system can also solve the problems which face companies and customers in online shopping. Since there are different types of the same product, and also different criteria for purchasing which can be different between individuals, customers will need a decision support system to recommend them with the best selection. This motivates us to also propose a neutrsophic technique to deal with unclear and conflicting information which exists usually in the purchasing process. Therefore, the smart system and neutrosophic technique is considered as a comprehensive system which links between customers, companies, marketers to achieve satisfaction for each of them.

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series.

In the field of eclipsing binary stars, the relative coordinate positions of each star with respect to the other stars in the system are adapted. Therefore the orbital parameters of the system will be adapted by the criterion of dynamical and physical (photometric) properties. Orbital elements have been determined for binaries from a combination of velocities in the literature. The formulas that are obtained for computing the orbital elements are determined from the directions of the bases of space data. So the present work is a new presented method for the determination of optimal orbital elements of binary stars based on flower pollination algorithm. The proposed algorithm seek to solve the problem of detecting global minimisers as a function instead of problem of finding periodic orbits, to get a best fit of Keplerian and phase curves. The experimental results implemented on star η Bootis of MK type G0 IV. Demonstrate that the flower pollination algorithm (FPA) reaches good results compared to the results of the standard particle swarm optimisation and other optimisation algorithms.

In this paper, a solution procedure is proposed to solve neutrosophic linear fractional programming problem (NLFP) where cost of the objective function, the resources and the technological coefficients are triangular neutrosophic numbers. Here, the NLFP problem is transformed into an equivalent crisp multi-objective linear fractional programming (MOLFP) problem. By using proposed approach, the transformed MOLFP problem is reduced to a single objective linear programming (LP) problem which can be solved easily by suitable linear programming technique. The proposed procedure illustrated through a numerical example.

Symbiotic organisms search algorithm (SOS) is a new meta-heuristic algorithm based on the symbiotic relationship between the biological, which was proposed in recent years. In this paper, we propose new robust and powerful metaheuristic algorithm called an improved version of symbiotic organisms search algorithm integrated with construction algorithm and mutation operators (ISOS) to solve quadratic assignment problems. The quadratic assignment problem (QAP) considers NP-hard combinatorial optimisation problem and has various practical applications. A set of benchmarks from QAPLIB Library are employed to evaluate the performance of proposed algorithm. ISOS is compared to several algorithms in the literature. The comparisons show that the proposed algorithm shows reliable, efficient and promising results.

Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

Upcoming Reservoirs in North Kuwait will play significant role for meeting NK’s strategic production target. Recent full field study of Sabryia Tuba carbonate reservoir makes it the largest Oil in-Place of the Upcoming Reservoirs in NK. Sabriyah Tuba, though discovered in the 60’s, is still relatively under exploited presently with only 13 active wells with very low total production rate compared to other reservoirs. High reservoir heterogeneity, tightness and poor fluid properties necessitate the application of new stimulation technology targeting good frac conductivity and deep penetration for effective productivity and recovery enhancement. With efforts of a multidisciplinary team, new acid frac fluid, diverter and high leak off control technologies have been designed and executed successfully in two stages covering multiple reservoir layers for the first time in Tuba. Tuba is tight multilayer limestone reservoir. Owing to the low reservoir transmissibility, even low average well production assisted by Artificial Lift causes near-wellbore (not regional) rapid decline in pressure. Full-scale commercial exploitation of the Tuba reservoir therefore requires advanced technologies for productivity enhancement. The well under study is vertical, drilled in 2012 and completed in Upper and Lower Tuba with conventional matrix acid stimulation which had a very steep post-treatment decline rate. It was selected for first application of acid fracturing due to its excellent reservoir development in higher structure position with high initial pressure and medium oil quality, but low production rate. The job was designed to implement a 3-stage acid frac. The first stage was successfully executed. The second and third stages were combined into one stage due to the vertical communication between all 4 Upper Tuba layers during the data fracture treatment. Many technical and operational challenges were faced and properly handled through integrated communication between different participating teams with lessons learned are to be considered in next designs and executions. Post acid Frac, a production log (PLT) was recorded and showed greater contribution from the Upper Tuba intervals. The well has been monitored closely where stabilization was observed in short time period represented in stabilized Pump Intake pressure and production rate that has been tripled post stimulation with an estimated reservoir productivity improvement of about ~3 folds from nodal analysis. The positive result of first acid frac reinforces the action plan to proceed with such stimulation technology. It is expected that the oil gain potential will be significantly more in horizontal completions. Therefore, acid frac stimulation for 2 Horizontal Tuba’s wells is under study. The success of such application will formulate the drilling, completion and stimulation development strategy for the entire Tuba reservoir including early stimulation to improve initial productivity that will contribute significantly to the NK production target. This paper describes the design, implementation and production impact of an advanced fiber based acid fracturing treatment that was successfully executed across the cased-hole of a pilot vertical well that is shaping the production enhancement strategy for Sabriya’s Tuba tight reservoir in North Kuwait field. The paper also focuses on the acid fracturing technique that delivers hydraulic acid fractures with deep, infinitely conductive channel compared to conventional acid stimulation techniques previously employed and their immense impact on production.

In a high-perm/strong-aquifer environment, comparatively low-perm/heavy-oil/solution-gas-drive reservoirs inevitably get sidelined. Flow-Channel Hydraulic Fracturing, however, with infinite conductive channels for fluid flow rather than intra-granular flow, as in conventional fracturing techniques, offers a tangible solution for increased productivity. The channels in the proppant pack are created through a patented technique that combines special pumping protocol, perforation scheme, fracturing fluid design and fiber technology. These conductive channels accelerate fracture cleanup, and achieve longer effective fracture half-lengths, thereby enhancing productivity, injectivity& recovery. In North Kuwait, 90% of wells drilled are vertical, penetrating a wide range of reservoir types consisting of high and low permeability sandstones and carbonates, with varying crude types from medium heavy to condensate. Flow-Channel Hydraulic Fracturing technology, with its infinite conductive flow-channels and reduced risk of premature screen-outs, was selected as the optimum stimulation technology for the most challenging low-permeability/low API gravity candidates where conventional stimulation techniques are not providing the aimed results. Conventional propped hydro-frac treatments were attempted previously with marginal benefit and operational failures. This was the first application of this technology in Kuwait. Multi-disciplinary candidate screening and selection for the initial pilot wells, followed by pre-job logistics and operational planning with the service provider, resulted in a flawless execution with text-book results. Post-job production results showed a stabilized flow rates with more than 85% production gain compared to the flow rates expected from conventional completion techniques. Incremental production paid out stimulation expenses in 35 days. This very successful introduction for Flow-Channel Fracturing application opened up opportunities in low API reservoirs where even the flow through the proppant pack from conventional fracture stimulations is limited. Injectors associated with IOR & EOR projects, and Carbonate completions traditionally responding to repeated periodic acid fracture stimulation, may require only single channel proppant fracture during their life cycle.

The Heterogeneity Index (HI) process was utilized in order to demonstrate production gain opportunities in a very short period of time, in a large mature ME field with around 500 wells producing from different reservoirs. The HI process provided a quick screening method of identifying preliminary candidate wells with anomalous behavior (over/under performance) for further analysis and most importantly, provided the foundation for the overall Structure Production Approach. This process; HI, can be calculated by utilizing OFM. A Cross Hair Plot has also been utilized to show the comparison of the HI of two variables in the same plot, creating an easy way to identify wells behaving differently from the average. The cross hair plot can be combined with X-Y Coordinated plot which reproduces the location of the wells. The results from this screening tool were utilized to identify the families of productivity problems at field level, and additional fast screening was done at well level to identify candidates for production enhancement. Representative Wells were selected for detailed diagnostics based on the relevance and size of productivity impact and, the potential of its production rate or well deliverability. Once a few "top potential" wells were identified, production engineering workflows were implemented in order to assess and forecast the potential of production incremental and try to determine and evaluate the best probable action. Some of the key innovate workflows used to complement production enhancement were: Time- lapse nodal analysis (honoring production history and neighboring wells), rate transient analysis (to consume sporadic/low frequency production data), single wellbore modeling (based on logs and flow units), among others. This paper will demonstrate the Production Enhancement Technologies Methodology, in particular the HI process with real examples; pending on data release approval from the owner and the progress of the operations.

The knapsack problem is one of the most studied combinatorial optimisation problems with various practical applications. In this paper, we apply an elite opposition-flower pollination algorithm (EFPA), to solve 0-1 knapsack problem, an NP-hard combinatorial optimisation problem. The performance of the proposed algorithm is tested against a set of benchmarks of knapsack problems. Computational experiments with a set of large-scale instances show that the EFPA can be an efficient alternative for solving 0-1 knapsack problems.

This paper explains how the real-time surveillance helps improving runlife, uptime and reducing production deferment of Electrical Submergible Pumps (ESP) of SG-300 offshore platform wells of Gulf of Suez Petroleum Company (GUPCO). The implementation of a robust suerveillance workflow together with real-time surveillance and alarm notification system has shown a good contribution to increase ESP runlife via preventing the ESP from being misoperated outside its recommended operating conditions and experiencing excessive stress, reduce start/stops and downtime, reduce production deferment, and improve downhole chemical injection program for scale inhibition. This is being done via classification of alarms and on-fly diagnostics of main root causes that helps to promptly identify remedial actions and/or recommendations.

Gait recognition has been gaining increased attention in a wide variety of applications. Human identification and authentication from gait information is an extremely challenging task, especially in the Internet of Things (IoT). This can be attributed to the resource-constrained nature of IoT devices and the privacy concerns of humans. With the increasing demand for person identification and verification, human gait information has been demonstrated as an active biometric that is valuable for more human recognition in the era of big data and artificial intelligence. This project aims to automate the identification and authentication of a person based on the reflection of her gait patterns on inertial sensor measurements. Our system uses lightweight deep learning based on temporal convolutions and attention operations to efficiently process and learn gait patterns from noisy inertial measurements. The light nature of our model enables it to be easily trained and deployed on edge devices in a resource-constrained IoT environment. Our system introduces an intelligent federated learning mechanism to train our speech recognition model under mobility constraints without the need to offload the data to a central location, which preserves the data from exposure to privacy attacks. The experiments show that the proposed method achieves higher than 99.17% and 96.75% accuracy for in-person identification and authentication, respectively, on inertial data. The ubiquitousness and low cost of gait sensors present an excellent opportunity for developing long-term gait analysis systems capable of identifying and authenticating people in outdoor environments, especially in smart cities., ITAC GP Support Program Round #17 - (ID:GP2022.R17.11)

The World Health Organization (WHO) reported that cardiovascular diseases account for 40% of total deaths in Egypt; cardiovascular diseases are a major public health concern with significant social and economic implications in terms of healthcare -needs, lost productivity, and premature death. The disease burden caused by cardiovascular diseases is further fueled by the adoption of unhealthy lifestyles and eating habits. In fact, the incidence of cardiovascular diseases is fast shifting to the youth; a trend that’s specifically prevalent in the capital city of Cairo and underserved urban communities where fast food and sedentary lifestyles are an increasing reality. This in turn makes cardiovascular diseases a great threat to health infrastructure locally and internationally. According to Egyptian vision 2030, healthcare is one of the main pillars to achieve sustainable development and to improve Egyptians' life quality. Therefore, cardiovascular diseases can prohibit the sustainable development process in the Egyptian healthcare system. In recent years, artificial intelligence (AI), especially deep learning (DL), has demonstrated an exciting potential to detect abnormalities in the heart from particular biomarkers. For example, in clinical medicine, the electrocardiogram (ECG) is a critical tool for diagnosing a wide range of arrhythmias. Every year, more than 300 million ECGs are collected around the world, making them a vital tool in the everyday practice of clinical medicine. The existing DL solutions for the diagnosis of cardiovascular diseases from such biomarker data suffer from the black-box nature that limits its applicability for high-risk decisions in real-world healthcare. This in turn makes the doctors unable to trust such a solution. The Internet of Health Things (IoHT) provides a great shift toward automated collection and diagnosis of patients’ data making computer-aided diagnosis (CAD) smarter and more efficient. Coping with Egypt Vision 2030, this project is to take the advantage of advanced AI and IoHT technologies to address the challenges of meeting the diagnosis of cardiovascular diseases threatening the lives of cardiac patients in Egypt. The proposed research project aims to take the advantage of recent advances in DL and IoHT to develop a smart CAD for cardiovascular diseases in IoHT. In the following, we explain the details of the research plan of this project through different stages. first, deep investigation and analysis are performed for electrocardiography and echocardiogram data and its associated features, then, we provide an analytical review and comparatively analyze the previously designed DL models for efficient diagnosis. Second, a solution to the black-box nature of DL approaches is given by introducing an interpretable DL detector that can provide a visual explanation of its detection decisions, which can help doctors to depend on them to make a decision. To ensure perfect interpretations, a human-in-the-loop strategy is followed to involve medical experts to assess and evaluate the quality of interpretations according to the medical properties of the cardiovascular data. Moving away from centralized learning, the project's third stage emphasizes designing a federated approach for preserving the privacy of interpretable DL during the training by applying lightweight key encryption and decryption methods to avoid leaking private patient information. finally, we shift from simulation to physical design by building a small-scale prototype for the IoHT system using edge devices, wireless networks, routers, cloud servers, and fog nodes. Then, we deploy the proposed interpretable DL framework (along with the privacy preservation methods) on this system and their performance is evaluated using the real world samples., This work is funded by ITAC GP Support Program Round #17 - (ID : GP2022.R17.9)