Objectives/Scope. Well completion is an important step for every well to undergo in order to prepare it for oil and gas extraction. Based on the nature and characteristics of an oil and gas reservoir, appropriate well completion practices are selected to enhance the production. Hydraulic fracturing is one such technique. It frequently involves horizontal drilling and injecting fluids under high pressure to fracture the rock. The larger fractures along with the injected fluid enable high amounts of trapped natural gas and crude oil to flow out of the formation to the producing well bore. In well completion, a variety of chemicals are employed to leverage oil production, and the goal of this study is to determine how such chemicals impact performance rate in several unconventional wells in the Bakken Shale. Methods, Procedures, Process. In this approach, two Completion and Production datasets from North Dakota (the Bakken Shale) and fracFocus were processed and combined accordingly which resulted in some of the following parameters, type of chemical and amount of chemical, and true vertical depth of the wells. And the dataset that was produced was analyzed based on the stimulation treatment. The proposed workflow utilizes supervised machine learning algorithms to train different predictive models to estimate the amount of the produced oil; including but not limited to neural Random Forest, CATboost and XGboost. Additionally, by quantifying each chemicals’ importance on oil production, this investigation was able to determine each chemical's influence. Results, Observations, Conclusions. This study examined the impact of more than 2500 different completion chemicals on the oil production of unconventional reservoir and discovered the chemicals with the highest significance on the oil production, given that, the predictive models were able to estimate the oil production accurately after feeding it with the type and measures of the most influencing chemicals. Novel/Additive Information. The most important pillar of this framework is that it expedites the workflow of hydraulic fracturing jobs in the unconventional reservoir by providing an accurate model that optimizes its parameters to maximize the oil production rate. This solution offers an automated decision-making process for the selection of chemical types to be used in the hydraulic fracking jobs. The choice of chemicals in fracturing fluids is affected by many variables, including its compatibility with the target rock formation to be hydraulically fractured, the geology of the rock formations being drilled through, the pressure and temperature measurements in the target formation, cost, operator preference, and possible interactions between chemicals in the treatment fluid.

Trump declared his intention to build a wall along the U.S.-Mexico border during his presidential campaign in 2016. On 4 January 2019, President Trump sent a letter to members of the U.S. Congress on the need to build a long wall to secure the U.S. border. This led local news reporters to discuss this political issue and its effects on the American and Mexican populations. Since Trump's decision of building the wall is one of the global issues that have been widely discussed in social media and American media news agencies, it is pertinent to analyze thematic choices in Trump's tweets on the U.S.-Mexico border wall issue. The present study aims to investigate Trump's thematic choices, employing Halliday's systemic functional linguistics' approach. UAM Corpus Tool software was employed in the annotation of Theme types. The results of the study showed that topical Theme was the most frequent Theme type in Trumps' tweets, followed by the textual Theme. Interpersonal Theme was rarely employed. Trump tends to use a simple, direct, and spontaneous language to make the communication style with his audience more natural and less complex. The study contributes to our understanding of Theme types in political discourse on social media. ‫اليوسف‬ ‫سليمان‬ ‫هشام‬ ‫العتيبي‬ ‫عماد‬ ‫نجد‬ ‫و‬ (. 2020 .) ‫حول‬ ‫ترامب‬ ‫دونالد‬ ‫تغريدات‬ ‫في‬ ‫املبتدأ‬ ‫أنواع‬ ‫دراسة‬ ‫االجتماعي.‬ ‫التواصل‬ ‫وسائل‬ ‫في‬ ‫ي‬ ‫السياس‬ ‫الخطاب‬ ‫املكسيك:‬ ‫مع‬ ‫الحدودي‬ ‫الجدار‬ ‫واإلدارية،‬ ‫اإلنسانية‬ ‫العلوم‬ ‫فرع‬ ‫فيصل،‬ ‫امللك‬ ‫لجامعة‬ ‫العلمية‬ ‫املجلة‬ (‫املجلد‬ ‫إلكتروني‬ ‫نشر‬ (‫العدد‬ ،) ‫إلكتروني‬ ‫نشر‬

Research on intradisciplinary variations in self-mention marker use in research articles (RAs) in dentistry subdisciplines is lacking. The present study investigates self-mention markers used in each of the seven dentistry subdisciplines (oral sciences, periodontics, endodontics, pediatrics, prosthodontics, oral and maxillofacial surgery, and orthodontics), sections of RAs that employ more self-mention devices in each of the seven dentistry subdisciplines, and common rhetorical realizations of first-person pronouns in the seven dentistry subdisciplines. The analytical framework was primarily based on Hyland’s (2003) four rhetorical functions of self-mentions in RAs. The findings showed the lack of qualitative and quantitative intradisciplinary variations across six of the seven dentistry subdisciplines. The first-person plural pronouns “we” and “our” were the most frequently employed self-mention devices in the Discussion section of RAs. Authors in the periodontics subdiscipline preferred to retain an objective stance through the use of passive constructions, abiding by the conventional norms of academic writing that restrict them. The findings also revealed that explaining a procedure and stating findings/claims were the most frequent realizations associated with the use of self-mention devices, with the exception of periodontics RAs that employed passive constructions instead. The findings contribute to the fields of discourse and genre studies as well as ESP/EAP courses. They may have implications for dentistry RA writing and teaching. An awareness of more frequently used self-mentions in dentistry RAs and their rhetorical functions can help English dentistry scholars successfully produce RAs in line with the academic writing norms of each subdiscipline.