Forecasting of production in unconventional prospects has gained a lot of attention in the recent years. The key challenges in unconventional reservoirs have been the requirement to put online a) a large number of wells in a short period of time, b) well productivity significantly driven by completion characteristics and that c) the physics of fluid flow in these prospects still remain uncertain. In this paper, machine learning algorithms are used to forecast production for existing and new wells in unconventional assets using inputs like geological maps, production history, pressure data and operational constraints. One of the most popular Machine Learning methods – Artificial Neural Network (ANN) is employed for this purpose. ANN can learn from large volume of data points without assuming a predetermined model and can adapt to newer data as and when it becomes available. The workflow involves using these data sets to train and optimize the ANN model which, subsequently, is used to predict the well production performance of both existing wells using their own history and new wells by using the history of nearby wells which were drilled in analogous geological locations. The proposed technique requires users to do less data conditioning and model building and focus more on analyzing what-if scenarios and determining the well performance.

Purpose In the recent times social media is considered as the most popular tool of communication among the students in India. Based on the assumption that the usage of social media is going to reinforce the academic performance among the medical students, the purpose of this paper is to investigate the mediating effect of student engagement on the use of SM and AP of medical students of India. Design/methodology/approach The students were selected from the top three public-funded medical colleges of India. Almost 250 medical students took part in the survey. A self-administered structured questionnaire was used for the collection of the data. Structural equation modelling was used for the analysis of the final data. Findings The results of the study show that student engagement is a multi-dimensional construct. It was found that the behavioural and emotional engagement did not mediate the relationship between usage of social media and academic performance, whereas, the cognitive engagement did mediate the relationship. The outcome of the study depicts that the usage of the social media has a potential impact on the learning environment and enhances the cognitive engagement among the medical students and eventually their academic performance. Research limitations/implications This paper contributes to the existing body of knowledge on the effectiveness of social media in higher education learning among medical students. Furthermore, the study also looks at the mediating effect of Student engagement between usage of social media and academic performance. This will be helpful for the educator to know how social media can be useful for conducive learning. Originality/value The usage of the social media is claimed to enhance learning among the students but there is hardly any empirical evidence of the same. Therefore, the present paper looks at the combined effect of two distinct sets of literature, i.e., the influence of usage of social media on student engagement, and student engagement and academic performance. Linking the two studies the present paper looks at the usage of the social media, student engagement and academic performance among the medical students of India.

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