Artificial Intelligence for the Online Prediction of the Cool-down Time in a Subsea Pipeline After an Unplanned Shutdown

Hydrates formation in subsea pipelines is one of the main reliability concerns for flow assurance engineers. A fast and reliable assessment of the Cool-Down Time (CDT), the period between a shut-down event and possible hydrates formation in the asset, is of key importance for the safety of operations. Existing methods for the CDT prediction are highly dependent on the use of very complex physics-based models that demand large computational time, which hinders their usage in an online environment. Therefore, this work presents a novel methodology for the development of surrogate models that predict, in a fast and accurate way, the CDT in subsea pipelines after unplanned shutdowns. The proposed methodology is, innovatively, tailored on the basis of reliability perspective, by treating the CDT as a risk index, where a critic CDT threshold (i.e. the minimum time needed by the operator to preserve the line from hydrates formation) is considered to distinguish the simulation outputs into high-risk and low-risk domains. The methodology relies on the development of a hybrid Machine Learning (ML) based model using datasets generated through complex physics-based model’ simulations. The hybrid ML-based model consists of a Support Vector Machine (SVM) classifier that assigns a risk level (high or low) to the measured operating condition of the asset, and two Artificial Neural Networks (ANNs) for predicting the CDT at the high-risk (low CDT) or the low-risk (high CDT) operating conditions previously assigned by the classifier. The effectiveness of the proposed methodology is validated by its application to a case study involving a pipeline in an offshore western African asset, modelled by a transient physics-based commercial software. The results show outperformance of the capabilities of the proposed hybrid ML-based model (i.e., SVM + 2 ANNs) compared to the classical approach (i.e. modelling the entire system with one global ANN) in terms of enhancing the prediction of the CDT during the high-risk conditions of the asset. This behaviour is confirmed applying the novel methodology to training datasets of different size. In fact, the high-risk Normalized Root Mean Square Error (NRMSE) is reduced on average of 15% compared to the NRMSE of a global ANN model. Moreover, it’s shown that high-risk CDT are better predicted by the hybrid model even if the critic CDT, which divides the simulation outputs in high-risk and low-risk values (i.e. the minimum time needed by the operator to preserve the line from hydrates formation), changes. The enhancement, in this case, is on average of 14.6%. Eventually, results show how the novel methodology cuts down by more than one hundred seventy-eight times the computational times for online CDT predictions compared to the physics-based model.

Holistic Customized Approach to Address Practical Challenges for the Large Scale Implementation of Limited Entry Liners Completions at ADNOC Onshore Fields

Limited entry liners (LEL) implementation strategy is one of the key solutions to to improve the well productivity by maximizing the reservoir contact and matrix stimulation carbonate reservoirs. This strategy requires conducting high rate and high volume acid stimulation with high pressure pump after the installation of limted entry liner, that poses practical concerns to be addressed for adopting conventional well completions and existing resources. In addition, implementation of the LEL completion and stimulation for a large scale application within the minimum time frame and limited resources is a challenge. This paper provides the detail of challenges faced and solutions adopted to implement the LEL completions amd stimulation at onshore fields. Challenges including suitable candidate selection, completion design, limited materials for well construction to handle high-volume acid stimulation, limited well head injection pressure, contractual limitations for securing the tools and pumping equipment. Further, this paper discusses about the temporary solutions adopted for executing the LEL implementation in the best economical way within near future and provide the long term solutions for LEL implementation in the next 5 years business. The first three LEL completion wells that were successfully installed and stimulated at ADNOC Onshore are currently producing at more than 2 times higher PI (productivity index) compared to the pre-stimulation rate. The same apply to the injector wells, in which significant improvement on the injection rate of up to 18bbl/min (26000 bwpd) was observed. Currently ADNOC Onshore is planning to execute the LEL completion and stimulation in additional 15 wells during 2021 along with plan for up to 300 LEL completions during the next 5 years. The LEL technology is a key technology to support ADNOC lower completion strategy which aim to minimize the bare foot completions in order to increase the horizontal wellbore accessibility and effective stimulation. Overall, the first LEL installation and stimulation completed within 8 months from the candidate finalization using the existing resources available in ADNOC Onshore. This paper describes an economical solution for implementation of LEL completion strategy at large scale for major fields within minimum time frame by utilizing the existing resources while adhering to HSE rules.

Features of using a two-point crossover for solving an inhomogeneous minimax problem modified by the Goldberg model

The relevance of the topic of this work is the strong growth of multiprocessor systems, for which it is important to solve a large volume of tasks in a minimum time. There are various algorithms for solving such a problem, which can be divided into classes of exact and approximate. The representative of approximate algorithms is the algorithm of the Goldberg model, which gives acceptable results, the modifications of the crossovers of which are studied in this paper.

Joint Medicines Regulatory Procedure in the East African Community: Registration Timelines and Way Forward

A review of the East African Community (EAC) joint regulatory review process was conducted, registration timelines analyzed and key milestones, challenges and opportunities documented for the period of July 2015 to January 2020. A total of 113 applications were submitted for joint scientific review. Among these, 109 applications were assessed, 57 were recommended for marketing authorisation, 52 applications had queries to applicants and four applications were under review. A total median approval time for all products ranged from 53 to 102 days. The maximum time taken by a regulator to review the dossier was 391 days and the minimum time was 44 days. For applicants, the maximum time to respond to queries was 927 days and the minimum time was nine days. The total median time for granting marketing authorisation by the National Medicines Regulatory Authorities (NMRA) decreased from 174 to 39 working days in 2015 and 2019 respectively. However, not all EAC NMRA has granted marketing authorisation to all 57 products due to non-payment of applicable fees by applicants. Long regulatory approval timelines were contributed by limited capacity for timely scientific review of dossier by some NMRA, lack of online portal to share dossiersand assessment reports, delay in responding to queries by applicants and deficiencies in dossier. The metric tool and register of medical products submitted for joint scientific review had incomplete data. Challenges were identified and actions recommended to ensure regional regulatory system optimization, efficiency, transparency, sustainability and accountability.