Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.
With the current economic downturn, operators, as well as service providers, are aiming at the best operational and financial performance. This had a significant impact on introducing the concept of minimizing cost in conjunction with production optimization. In this paper we are going to demonstrate that the integration between the advanced technologies, reservoir and operations experts, and the assistance of well modeling software, under the umbrella of real-time operations surveillance, yields superior results in terms of; excellent operations support, troubleshooting, problem identification, and remedial corrective actions. The value of this integration is the achievement of seamless on-site well modeling in real-time. Real-time operations surveillance provides a connection between the field and experts in both companies, leading to proactive well and formation management, therefore, identifying completion and workover problems, suggesting the solutions, implementing, and following up these solutions exhibiting flawless operations. A case study is presented to quantify the value of applying real-time operations surveillance. The case study is an artificially lifted well producing from tow different formations commingled using an ESP equipped with permanent downhole pressure and temperature gauge, and real-time monitoring equipment were installed for data transmission to field office, head quarters, and operations technical support center. Following the standard procedures after the ESP start-up performing initial system diagnosis, a surveillance engineer identified system underperformance that, afterwards, has been proven that formation damage took place while killing the well for workover. After analyzing the surface fluid samples, building digital well models, and performing nodal analysis, we could detect which formation is plugged and suggested the remedial well treatment needed. The actual result was boosting the production from 150 BPD to 550 BPD after the workover, while the average rate before the workover was 350 BPD, which is almost 60% production improvement Introduction Real time surveillance has been a fast emerging technology over the past decade. In order to have better understanding of the issue and its influence in our industry a simple internet based search followed by simple statistical analysis was performed. Referring to the SPE (Society of Petroleum Engineers) as a highly recognized association by the industry, we have utilized the OnePetro which is a multi-society technical library that features numerous technical documents from 9 E&P related organizations, moreover, it provides search access to papers from eight other industry organizations, i.e. American Petroleum Institute, American Rock Mechanics Association, American Society of Safety Engineers, NACE International, Offshore Technology Conference, Society of Petrophysicists and Well Log Analysts, Society of Underwater Technology, and World Petroleum Congress. In other words, we have chosen one of the widest e-library to be the reference for our statistical analysis.
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