Background: Magnesium sulphate is used during anaesthesia for its antihypertensive/ antiarrhythmic properties and attenuating the response to endotrachial intubation and as an anticonvulsant for women with eclampsia. At the motor nerve terminal, MgSO4 inhibits acetylcholine release. Thus, it enhances the effect of neuromuscular blocking agents. Aims & Objectives:To determine the effect of magnesium sulphate pre-treatment on the onset, duration and recovery of nondepolarizing muscle relaxant and to quantify the haemodynamic effects of administration of MgSO4 on the arterial blood pressure. Materials and Methods: One year old prospective, randomized, double blinded, controlled clinical study was conducted on randomly selected 45 patients of either sex, aged between 18-55 years, of grade I or II of American Society of Anaesthesiologists, undergoing elective surgery at a Medical College of eastern Uttar Pradesh. Patients were divided into three groups according to the doses of MgSO4 used for pre-treatment. Statistical evaluation was done by using student's 't' test for paired data. Results: The Mean Arterial Blood Pressure response to laryngoscopy, tracheal intubation, was almost abolished in Group A (p<0.05) followed by Group B (p<0.05) and maximum pressure response occurred in Group C where MgSO4 was not used (p<0.05). The speed of onset of neuromuscular block was accelerated by pre-treatment with MgSO4 before non-depolarizing muscle relaxants. The mean onset time was 144.3 ± 12.08 seconds (p<0.001) in Group A, 192.66 ± 19.81 second (p<0.001) in Group B and 286.33 ± 34.20 seconds in Group C. The clinical duration was prolonged in MgSO4 group as compared with control group. Mean value was 52.4 ± 8.97 minutes (p<0.001), 44.86 ± 6.59 minutes (p<0.01), and 34.2±8.05 minutes respectively in Group A, B and C. Pre-treatment with MgSO4 before non-depolarizing muscle relaxant, accelerated speed of onset of neuromuscular block, necessary for intubation of trachea. MgSO4, in the presence of non-depolarizing muscle relaxant, intensified and prolonged the neuromuscular blockade and recovery. Conclusion: Monitoring of neuromuscular function and reduction in dose of vecuronium are required when using these two drugs in combination.Cite this article as: Singh S, Malviya D, Rai S, Yadav B, Kumar S, Sharma A. Pre-treatment with Magnesium sulphate before non-depolarizing muscle relaxants: Effect on speed on onset, induction and recovery. Int

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In today's data-driven economy, operators that integrate vast stores of fundamental reservoir and production data with the highperformance predictive analytics solutions can emerge as winners in the contest of maximizing estimated ultimate recovery (EUR). The scope of this study is to demonstrate a new workflow coupling earth sciences with data analytics to operationalize well completion optimization. The workflow aims to build a robust predictive model that allows users to perform sensitivity analysis on completion designs within a few hours. Current workflows for well completion and production optimization in unconventional reservoirs require extensive earth modeling, fracture simulation, and production simulations. With considerable effort and wide scale of sensitivity, studies could enable optimized well completion design parameters such as optimal cluster spacing, optimal proppant loading, optimal well spacing, etc. Yet, today, less than 5% of the wells fractured in North America are designed using advanced simulation due to the required level of data, skillset, and long computing times. Breaking these limitations through parallel fracture and reservoir simulations in the cloud and combining such simulation with data analytics and artificial intelligence algorithms helped in the development of a powerful solution that creates models for fast, yet effective, completion design. The approach was executed on Eagle Ford wells as a case study in 2016. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy was achieved between the proxy model and the actual results. Hence, the proxy model could predict the wellbore productivity accurately for any given change in completion design. The operators now had a much simpler model, which served as a plug-and-play tool for the completion engineers to evaluate the impact of changes in completion parameters on the future well performance and making fast-tracked economic decisions almost in real time. The approach can be replicated for varying geological and geomechanical properties as operations move from pad to pad. Although the need for heavy computing resource, simulation skillset, and long run times was eliminated with this new approach, regular QA/QC of the model through manual simulations makes the process more robust and reliable. The methodology provides an integrated approach to bridge the traditional reservoir understanding and simulation approach to the new big data approach to create proxies, which allows operators to make quicker decisions for completion optimization. The technique presented in this paper can be extended for other domains of wellsite operations such as well drilling, artificial lift, etc. and help operators evaluate the most economical scenario in close to real time.

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Currently, as oil and gas companies continue to face risk of volatility in oil prices, production optimization and maintenance play a critical role in driving operational excellence for the industry while maintaining good profit margins. E&P companies must maintain a focus on reducing unit cost/barrel. This can be achieved by reducing operating costs, increasing production, and reducing downtime. We propose a recommendation engine driven by artificial intelligence (AI) that seamlessly integrates subsurface information and production characteristics for knowledge extraction needed to optimize production operations across conventional and unconventional assets. We used a three-phase approach to designing and building an advisory system that ingests data, learns patterns, and feeds these learnings from the data into different functional workflows necessary for improving the efficiency and effectiveness of production operations. The system uses these mechanisms of knowledge extraction, statistical learning, and contextual adaptation as it evolves into an autonomous asset optimization system that can proactively recommend actions for effective decision making to lower the unit cost/barrel.