| مشخصات مقاله | |
| ترجمه عنوان مقاله | ارزیابی دینامیکی ریسک نشت حفاری بر اساس نظریه فازی و الگوریتم PSO-SVR |
| عنوان انگلیسی مقاله | Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm |
| انتشار | مقاله سال ۲۰۱۹ |
| تعداد صفحات مقاله انگلیسی | ۲۹ صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه الزویر |
| نوع نگارش مقاله |
مقاله پژوهشی (Research article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | scopus – master journals – JCR |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| ایمپکت فاکتور(IF) |
۵٫۳۴۱ در سال ۲۰۱۷ |
| شاخص H_index | ۸۵ در سال ۲۰۱۹ |
| شاخص SJR | ۰٫۸۴۴ در سال ۲۰۱۷ |
| شناسه ISSN | ۰۱۶۷-۷۳۹X |
| شاخص Quartile (چارک) | Q1 در سال ۲۰۱۷ |
| رشته های مرتبط | مهندسی کامپیوتر |
| گرایش های مرتبط | الگوریتم و محاسبات – هوش مصنوعی |
| نوع ارائه مقاله |
ژورنال |
| مجله / کنفرانس | سیستم های کامپیوتری نسل آینده – Future Generation Computer Systems |
| دانشگاه | Mechanic and Electronic Engineering, Southwest Petroleum University, Chengdu 610500, China |
| کلمات کلیدی | خطر نشت، هوش مصنوعی، PSO-SVR، چند منظوره فازی ارزیابی پویا، الگوریتم هیورستیک |
| کلمات کلیدی انگلیسی | Leakage Risk, Artificial Intelligence, PSO-SVR, Fuzzy Multilevel, Dynamic Evaluation, Heuristic Algorithm |
| شناسه دیجیتال – doi |
https://doi.org/10.1016/j.future.2018.12.068 |
| کد محصول | E12002 |
| وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
| دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
| سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
| فهرست مطالب مقاله: |
| Outline Highlights Abstract Keywords ۱٫ Introduction ۲٫ Related work ۳٫ Establishment of risk assessment model for leakage based on fuzzy multi-level evaluation method ۴٫ Dynamic evaluation of leakage risk based on PSO-SVR algorithm ۵٫ Analysis of case results ۶٫ Conclusion Acknowledgments References |
| بخشی از متن مقاله: |
| Abstract In recent years, artificial intelligence has gradually penetrated into various fields, and has become a research hotspot. The modern industrial upgrades and transformation of the petroleum industry, makes it closer to the direction of intelligence. For the research of drilling risk evaluation, choosing the right evaluation model to achieve real-time risk dynamic evaluation which is important for risk judgment and response time. However, drilling system never considered as a complex system in the research of drilling risk assessment. When the sensor of the well site collects the relevant parameters, the remote monitoring system carries on the real-time data analysis, because of the instrument or transmission process, the drilling parameters appear fuzziness and randomness. To realize real time dynamic evaluation of drilling risk this paper proposed a fuzzy multilevel algorithm based on Particle swarm optimization (PSO) to optimize Support vector regression machine(SVR), and takes drilling leakage risk as an example. And two main objectives has been achieved. The first is to establish a fuzzy multi-level drilling leak risk evaluation system. The second is to use the PSO-SVR algorithm to study the risk evaluation results and realize the real-time dynamic risk evaluation. This paper first summarizes the characterization phenomena and laws of the occurrence of acquisition and loss parameters, and uses this as an indicator to establish a multi-level index system for risk assessment. Second, combined with fuzzy theory, a risk assessment model is established. And in final, the parameters C and g of the SVR model are optimized by using the SVR algorithm improved by PSO, which solves the problem that the parameters such as penalty factor , kernel function and sensitivity coefficient are difficult to select in the traditional SVR model, improves the accuracy of the model, and realizes more accurate real-time dynamic evaluation of risk. The algorithm proposed in this paper achieves two goals. Taking the XX oilfield as an engineering example, the results show that the accuracy of the PSO-SVR model can reach 99.99%, with high convergence degree, which is obviously higher than that of the multilayer perceptron neural network model. |