Relevant bibliographies by topics / OPTIMIZED RECOMMENDER SYSTEM

Academic literature on the topic 'OPTIMIZED RECOMMENDER SYSTEM'

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Published: 11 September 2023

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Journal articles on the topic "OPTIMIZED RECOMMENDER SYSTEM"

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Sumariya, Shrey, Shreyas Rami, Shubham Revadekar, Vidhan Shah, and Sudhir Bagul. "Hospital Recommender System." BOHR International Journal of Engineering 2, no. 1 (2023): 1–6. http://dx.doi.org/10.54646/bije.011.

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Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.
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Sumariya, Shrey, Shreyas Rami Rami, Shubham Revadekar, Vidhan Shah, and Sudhir Bagul. "Hospital Recommender System." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 1, no. 1 (2022): 99–103. http://dx.doi.org/10.54646/bijiam.016.

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Elderly patients require more medical effort. It is clear that early-stage disease diagnosis can support timely and appropriate treatment. But if you don’t pay attention in a timely manner, it can lead to different kinds of health problems that can lead to death. Take advantage of our recommendation system to recommend hospitals. A recommender system uses algorithms to provide product or service recommendations to users. By combining blockchain technology and machine learning models, we provide users with highly accurate recommendations. This whitepaper describes how sophisticated machine learning models and blockchain can be connected to improve recommendations, providing hospitals with higher performance and more accurate recommendations. An optimized model for recommending hospitals in a better manner is the main goal behind this paper.
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Yuan, Weiwei, and Donghai Guan. "OPTIMIZED TRUST-AWARE RECOMMENDER SYSTEM USING GENETIC ALGORITHM." Neural Network World 27, no. 1 (2017): 77–94. http://dx.doi.org/10.14311/nnw.2017.27.004.

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Verma, Sandhya, and Amit Kumar Manjhvar. "Optimized Ranking Based Recommender System for Various Application Based Fields." International Journal of Database Theory and Application 9, no. 1 (February 28, 2016): 137–44. http://dx.doi.org/10.14257/ijdta.2016.9.2.15.

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Li, Jian Yang, Xiao Ping Liu, and Rui Li. "Optimized RBF for CBR-Recommendation System." Applied Mechanics and Materials 214 (November 2012): 568–72. http://dx.doi.org/10.4028/www.scientific.net/amm.214.568.

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Recommendation systems are widely used in E-commerce to help their customers find products to purchase, with which an important problem is to efficiently search the contents with their demands, and have been attracting attention from quite a few researchers and practitioners from different fields. This paper proposes the CBR-recommender (Case-Based Reasoning) which is a comprehensive expression of human sense, logics and creativity, and can automatically acquire the user’s preferences from the process of adaptation or revision to satisfy the personalized needs; and we deploy radial basis function network (RBF) to control the system scale caused by the large amounts of data with high dimensions, whose performance is also superior with respect to the total time for satisfying a query Our experiments indicate that our mechanism is efficient since it is bounded by the number of neighbors and scalable because no global knowledge is required to be maintained.
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Loukili, Manal, Fayçal Messaoudi, and Mohammed El Ghazi. "Machine learning based recommender system for e-commerce." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (December 1, 2023): 1803. http://dx.doi.org/10.11591/ijai.v12.i4.pp1803-1811.

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<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>
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Herce-Zelaya, Julio, Carlos Porcel, Álvaro Tejeda-Lorente, Juan Bernabé-Moreno, and Enrique Herrera-Viedma. "Introducing CSP Dataset: A Dataset Optimized for the Study of the Cold Start Problem in Recommender Systems." Information 14, no. 1 (December 29, 2022): 19. http://dx.doi.org/10.3390/info14010019.

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Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.
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Bahrami, N., M. Argany, N. N. Samani, and A. R. Vafaeinejad. "DESIGNING A CONTEXT-AWARE RECOMMENDER SYSTEM IN THE OPTIMIZATION OF THE RELIEF AND RESCUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 171–77. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-171-2019.

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Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.
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Gupta, Shalini, and Veer Sain Dixit. "A Meta-Heuristic Algorithm Approximating Optimized Recommendations for E-Commerce Business Promotions." International Journal of Information Technology Project Management 11, no. 2 (April 2020): 23–49. http://dx.doi.org/10.4018/ijitpm.2020040103.

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To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.
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Muruganandam, Kishore, and Shaphan Manipaul S. "A Real Time Tourism Recommender System using KNN and RBM Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 357–62. http://dx.doi.org/10.22214/ijraset.2023.51527.

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Abstract: Tourism has become a significant contributor to both the industry and national economy. Over the years, the desire to travel and explore new places has led to a substantial increase in the number of tourists. This boom in tourism has resulted in many businesses, both global and local, as well as governments investing heavily in the industry. However, while governments invest in maintaining and promoting tourist attractions, there is often no emphasis on improving the overall experience of tourists.To address this issue, a real-time recommender system can be implemented. This system will provide tourists with recommendations based on their preferences, rather than just providing them with information. As the environment for recommender systems has become increasingly complex and dynamic, with diverse information available in real-time, it is necessary to develop an effective touring recommender system based on real-time characteristics. This system will provide tourists with real-time recommendations, thus enhancing their experience.To achieve this, a recommendation framework that provides comprehensive information for tourists is needed. This framework will guide tourists from the initial stage of exploring which country to visit, to providing a complete comprehensive guide for them once they arrive at their vacation destination. The goal of this solution is to give tourists the best possible experience, eliminating the need to depend on the help of others.Several features will be implemented in this application, such as comprehensive information on places to stay, dine, and visit, using the current location and budget, popularity, and other characteristics. The system will also create a timetable for tourists based on their duration of stay and provide real-time assistance. Overall, the system will provide a personalized and optimized touring experience for the tourist.With the implementation of this recommender system, tourists will be able to make more informed decisions about where to go, what to do, and where to stay. By using real-time data and personalized recommendations, tourists can enjoy a more engaging and satisfying vacation experience. This system will also benefit the tourism industry as a whole, by increasing customer satisfaction and promoting repeat visits
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Book chapters on the topic "OPTIMIZED RECOMMENDER SYSTEM"

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Sandesara, Mudita, Prithvi Sharan, Deepti Saraswat, and Rupal A. Kapdi. "An Optimized Search-Enabled Hotel Recommender System." In Lecture Notes in Electrical Engineering, 487–501. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9876-8_37.

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Behera, Gopal, and Neeta Nain. "Collaborative Recommender System (CRS) Using Optimized SGD - ALS." In Communications in Computer and Information Science, 627–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_55.

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Kumar, Akshi, Nitin Sachdeva, and Archit Garg. "Analysis of GA Optimized ANN for Proactive Context Aware Recommender System." In Hybrid Intelligent Systems, 92–102. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_10.

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Kadıoğlu, Serdar, Bernard Kleynhans, and Xin Wang. "Optimized Item Selection to Boost Exploration for Recommender Systems." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 427–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78230-6_27.

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Bansal, Saumya, and Niyati Baliyan. "Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique." In Design and Applications of Nature Inspired Optimization, 133–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17929-7_7.

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Avram, Anca M. "Radioiodine Theranostics of Differentiated Thyroid Carcinoma." In Integrated Diagnostics and Theranostics of Thyroid Diseases, 111–27. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35213-3_7.

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AbstractCurrent management of patients with thyroid cancer requires a risk-adapted approach to treatment and multidisciplinary cooperation. Routine use of 131-I administration after total thyroidectomy is no longer recommended as this approach has been challenged by evidence that remnant ablation does not improve survival in low-risk patients. However, therapeutic 131-I administration in intermediate- and high-risk patients demonstrated significant benefits in improving overall survival. Radioiodine (131-I Na-I) is the classic agent used for the diagnosis and treatment of differentiated thyroid cancer (DTC) based on sodium–iodine symporter expression in normal and neoplastic thyroid tissues permitting the integration of diagnostic imaging and therapeutics using the same radionuclide (i.e., theranostics) targeting specific characteristics of tumor biology. Radioiodine theragnostics involves the acquisition of pre-ablation diagnostic scans (Dx Scans) to guide patient-individualized targeted 131-I therapy with goal of maximizing the benefits of the first therapeutic 131-I administration. Current imaging technology with hybrid SPECT/CT gamma camera systems has improved the capability of diagnostic radioiodine scintigraphy for identifying regional and distant metastatic disease and this imaging information can be used for 131-I treatment planning and delivery of activity-adjusted 131-I therapy for achieving intended treatment goals (e.g., remnant ablation, adjuvant treatment, and treatment of known disease). The goal of radioiodine theragnostics is to optimize the balance between 131-I therapeutic efficacy and potential side effects on non-target tissues. This chapter summarizes the new concepts and essential information at the core of multidisciplinary DTC management, which emphasizes individualization of 131I therapy according to the patient’s risk for tumor recurrence to maximize benefit and minimize morbidity.
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Garosi, Ehsan. "Nurses Work System Optimization: Macroergonomics Perspective." In New Research in Nursing - Education and Practice [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110400.

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The hospital work system as a complex sociotechnical system has been an interesting research environment for human factor/ergonomic researchers. In this chapter dimensions of nurses’ work system elements were presented and Macroergonomics approaches were pointed out to optimize the interaction between nurses and other system elements. From Macroergonomics perspectives, human factor researchers would be able to identify and categorize health and performance issues through a systematic approach. Researchers are believed that this approach was not shown positive results initially, therefore a low-hanging fruit strategy is recommended. Decomposing work system elements is a potential opportunity to track the balance in the hospital nurse work system by considering these elements for redesigning work systems and applying appropriate interventions.
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Chawla, Suruchi. "Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 656–75. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch034.

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The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.
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Khalid, Saifullah. "Application of Adaptive Tabu Search Algorithm in Hybrid Power Filter and Shunt Active Power Filters." In Sustaining Power Resources through Energy Optimization and Engineering, 276–308. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9755-3.ch012.

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A novel hybrid series active power filter to eliminate harmonics and compensate reactive power is presented and analyzed. The proposed active compensation technique is based on a hybrid series active filter using ATS algorithm in the conventional Sinusoidal Fryze voltage (SFV) control technique. This chapter discusses the comparative performances of conventional Sinusoidal Fryze voltage control strategy and ATS-optimized controllers. ATS algorithm has been used to obtain the optimum value of Kp and Ki. Analysis of the hybrid series active power filter system under non-linear load condition and its impact on the performance of the controllers is evaluated. MATLAB/Simulink results and Total harmonic distortion (THD) shows the practical viability of the controller for hybrid series active power filter to provide harmonic isolation of non-linear loads and to comply with IEEE 519 recommended harmonic standards. The ATS-optimized controller has been attempted for shunt active power filter too, and its performance has also been discussed in brief.
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Talbot, Patricia A., and Jennifer Jones. "Engaging Heads, Hands, and Hearts to Optimize Study Abroad Outcomes." In Teacher Education, 1438–56. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0164-0.ch070.

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This chapter poses an innovative framework that can be utilized as a model for study abroad experiences, particularly those situated in developing countries. The model enhances a service learning structure by grounding both classroom study and related field work in the theoretical foundations of critical pedagogy, transformational learning theory, ecological systems theory and critical theory in a manner that sets the stage for success for study abroad students and in-country community members alike. The chapter concludes with a recommended plan for implementation of the framework as well as suggestions for optimizing sustainable outcomes for teachers as they begin work in classrooms of their own.
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Conference papers on the topic "OPTIMIZED RECOMMENDER SYSTEM"

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Kloucha, Chakib K., Bassem S. El Yossef, Imad Al Hamlawi, Muzahidin M Salim, Wiliem Pausin, Anik Pal, Hussein Mustapha, Soumil Shah, and Ahmad Naim Hussein. "Machine Learning Model for Drilling Equipment Recommender System for Improved Decision Making and Optimum Performance." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211731-ms.

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Abstract The oil industry, in its constant strive to maximize gains out of operational data is constantly exploring new horizons where to combine the latest advances in data science and digitalization, into the areas where key decisions to drive economical and operational decisions reside with an aim at optimizing the capital expenditure through sound decision making. High volume operational data has been recognized as hiding many opportunities where the captured details these repositories that include real time logs and bit run summaries, provide a clear opportunity where to extract insights to support optimized decisions in terms of equipment selection to achieve the desired operational objectives. Current possibilities within data science have opened the possibilities through viable solutions, which in this case, aims at providing advise on which equipment in terms of BHA and Bits to select, that would yield the desired outcome for a drilling run. The whole exercise being based on evidence gathered from previous runs where the details for the equipment, the relevant well characteristics, and the observed rates of penetration and the used parameters, are taken into consideration to provide the optimum combination to be implemented in new runs. The present study describes the methodology in terms of data utilization, data science method development and solution deployment, with the associated issues that had to be addressed in order to provide a viable solution in terms of data utilization, technical validity and final user utilization, as well as a series of recommendations to be addressed within any such endeavors to assure the value addition.
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Yap, Ghim-Eng, Ah-Hwee Tan, and Hwee-Hwa Pang. "Dynamically-optimized context in recommender systems." In the 6th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1071246.1071289.

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Iizuka, Kojiro, Takeshi Yoneda, and Yoshifumi Seki. "Greedy optimized multileaving for personalization." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3298689.3347008.

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Mallia Milanes, Mario, and Matthew Montebello. "Crowdsourced Recommender System." In Fourth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/head18.2018.8020.

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The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal. The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.
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Hammar, Mikael, Robin Karlsson, and Bengt J. Nilsson. "Using maximum coverage to optimize recommendation systems in e-commerce." In RecSys '13: Seventh ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2507157.2507169.

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Khandelwal, Hitesh, Viet Ha-Thuc, Avishek Dutta, Yining Lu, Nan Du, Zhihao Li, and Qi Hu. "Jointly Optimize Capacity, Latency and Engagement in Large-scale Recommendation Systems." In RecSys '21: Fifteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460231.3474606.

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Ur Rehman, Faizan, Ahmed Lbath, Bilal Sadiq, Md Abdur Rahman, Abdullah Murad, Imad Afyouni, Akhlaq Ahmad, and Saleh Basalamah. "A constraint-aware optimized path recommender in a crowdsourced environment." In 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2015. http://dx.doi.org/10.1109/aiccsa.2015.7507185.

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Zhang, Lei, Xuan Liu, Yidi Cao, and Bin Wu. "O- Recommend: An Optimized User-Based Collaborative Filtering Recommendation System." In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2018. http://dx.doi.org/10.1109/padsw.2018.8644910.

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Zou, Lixin, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. "Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330668.

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Ferrari Dacrema, Maurizio, Paolo Cremonesi, and Dietmar Jannach. "Methodological Issues in Recommender Systems Research (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/650.

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The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems. Over the years, the research community has developed widely-agreed best practices for comparing algorithms and demonstrating progress with offline experiments. Unfortunately, we find this accepted research practice can easily lead to phantom progress due to the following reasons: limited reproducibility, comparison with complex but weak and non-optimized baseline algorithms, over-generalization from a small set of experimental configurations. To assess the extent of such problems, we analyzed 18 research papers published recently at top-ranked conferences. Only 7 were reproducible with reasonable effort, and 6 of them could often be outperformed by relatively simple heuristic methods, e.g., nearest neighbors. In this paper, we discuss these observations in detail, and reflect on the related fundamental problem of over-reliance on offline experiments in recommender systems research.
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Reports on the topic "OPTIMIZED RECOMMENDER SYSTEM"

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Powell. L52196 Practical Guidelines for Conducting an External Corrosion Direct Assessment (ECDA) Program. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 2008. http://dx.doi.org/10.55274/r0011369.

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The purpose of integrity assessments of gas transmission or liquids pipelines is to minimize hazards to the general public, minimize pipeline leaks and spills, ensure continuous operations of the pipelines, optimize expenditures for reducing risk, and satisfying governmental regulatory requirements. The purpose of this document is to provide a bridge to help pipeline professionals follow NACE RP 0502-2002 (Standard Recommended Practice Pipeline External Corrosion Direct Assessment Methodology) and conduct pipeline integrity assessments for their pipeline systems. This document presents the four-step ECDA process, with an appropriate level of details to facilitate pipeline professionals in conducting integrity assessments using the ECDA process. Numerous figures and tables are presented to emphasize salient points related to integrity assessments for pipelines, using the ECDA process. Attachments include a questionnaire for collecting relevant data as part of the pre-assessment step and a worksheet to be used during direct examinations.
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Torrijos, Ivan Dario Pinerez, Tina Puntervold, Skule Strand, Panagiotis Aslanidis, Ingebret Fjelde, and Aleksandr Mamonov. Core restoration: A guide for improved wettability assessments. University of Stavanger, November 2021. http://dx.doi.org/10.31265/usps.198.

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The initial wetting of a reservoir sets a limit for the EOR potential during water-based recovery operations and “Smart Water” injection. For this reason, an improved understanding of the factors influencing the wetting can help to control and better forecast oil production during water-based floods. To preserve and reproduce the original reservoir wettability is a challenging task and wrong cleaning and core restoration procedures can lead to incorrect wettability estimations and thus induce serious errors when evaluating the initial wettability of a reservoir system or its EOR potential by water-based methods. Thereby, there is a need to improve the chemical knowledge on interactions among the rock, brine and fluids present in reservoir systems. This will help to understanding the influence of the parameters affecting wettability during cleaning and core restoration processes. Understanding which are the main parameters influencing oil recovery processes is of great relevance. The objective of this document is to provide suggestions for added-value experiments, complementing and challenging the standard RCA and SCAL procedures, prior to performing experimental research in which wettability and wettability alteration processes are important. Lessons learned will be highlighted and new ideas to optimize core restoration protocols to preserve and closely reproduce wettability are put forward. These recommended practices target core restoration procedures after the core material has been received in the laboratory. The target audience for this document is engineers and scientists with an interest in core preparation for wettability studies.
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