پیش‌بینی میزان ماسه عبوری از لوله‌ی مشبک‌کاری شده به کمک الگوریتم‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسنده
محمدالیاس خداشناس
دانشجوی کارشناسی ارشد، مهندسی نفت، دانشکده مهندسی شیمی، نفت و گاز، دانشگاه شیراز، شیراز، ایران
20.1001.1/ijge.2026.2088830.1125
چکیده
تولید ماسه از چاه‌های نفت و گاز باعث کاهش بهره‌وری و خرابی تجهیزات تولیدی می‌شود. برای کنترل این پدیده از توری‌های مهارکننده مکانیکی به‌ویژه لوله مشبک‌کاری شده استفاده می‌شود که عملکرد این توری‌ به‌وسیله‌ی تحلیل نتایج آزمون نگهداشت ماسه تعیین می‌شود. در این پژوهش به‌منظور پیش‌بینی میزان ماسه عبوری از واحد سطح توری لوله مشبک‌کاری شده، از الگوریتم‌های یادگیری ماشین استفاده ‌شده است. بدین منظور، نتایج حاصل از ۵۱۹ نمونه آزمایش آزمون نگهداشت ماسه‌ی ذکرشده در مطالعات پیشین به‌عنوان پایگاه داده جمع‌آوری شد که شامل: انواع ساختار و طراحی‌های مختلف توری، توزیع اندازه ذرات ماسه و شرایط آزمایشی است. پایگاه داده‌ی نهایی شامل: ۴۹۹ نمونه داده‌ی پیش‌پردازش شده است. برای ساخت مدل از الگوریتم‌های: GB، XGBoost، RF، DT، KNN، SVM، MLP و Poly Reg استفاده شد. نتایج نشان داد که الگوریتم GB با بیشترین دقت ۰/۹۴=R2) و کمترین خطا، به‌همراه الگوریتم XGBoost بهترین عملکرد را در پیش‌بینی ارائه می‌دهند. همچنین مشخص شد که اندازه عرض شکاف‌های توری و اختلاف فشار و دبی آزمایش مهم‌ترین عوامل تأثیرگذار بر مقدار ماسه تولیدی می-باشند. به‌علاوه، نوع طراحی توری و اندازه عرض شکاف‌های آن باید متناسب با توزیع اندازه ذرات ماسه انتخاب شود. مدل ماشینی این پژوهش، مزایایی همچون: افزایش دقت پیش‌بینی نسبت به روش‌های پیشین، ارائه‌ی یک مدل تفسیرپذیر عملکرد توری و افزایش سرعت انتخاب را به همراه دارد و می‌تواند به کنترل تولید ماسه‌ی چاه‌ها کمک کند.
کلیدواژه‌ها
آزمون نگهداشت ماسه
تکمیل چاه
تولید ماسه
لوله مشبک‌کاری شده
یادگیری ماشین
موضوعات

عنوان مقاله English

Prediction of Sand Passage Rate Through Slotted Liner Using Machine Learning Algorithms

نویسنده English

Mohammadelyas Khodashenas
M.Sc. Student, Petroleum Engineering, School of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran
چکیده English

Sand production from oil and gas wells reduces productivity and causes damage to production equipment. To control this phenomenon, mechanical sand screens, particularly slotted liners, are employed, and the performance of these screens is determined through the analysis of sand retention test results. In this study, machine learning algorithms were utilized to predict the amount of sand passing through the unit surface area of slotted liner coupons. To this end, the results of 519 sand retention test samples reported in previous studies were compiled as a dataset, encompassing various screen structures and designs, sand particle size distributions, and experimental conditions. The final dataset consisted of 499 preprocessed data samples. The models were constructed using the following algorithms: GB, XGBoost, RF, DT, KNN, SVM, MLP, and Polynomial Regression. The results demonstrated that the GB algorithm, with the highest accuracy (R² = 0.94) and the lowest error, alongside the XGBoost algorithm, delivered the best predictive performance. Furthermore, it was found that slot width, pressure differential, and flow rate are the most influential factors affecting the amount of produced sand. Additionally, the screen design type and slot width must be selected in accordance with the particle size distribution of the sand. The machine learning model developed in this research offers advantages such as enhanced prediction accuracy compared to previous methods, providing an interpretable model of screen performance, and increasing the speed of selection, thereby contributing to the control of sand production in wells.

کلیدواژه‌ها English

Sand retention test
Well completion
Sand production
Slotted liner
Machine learning
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  • تاریخ دریافت 26 اردیبهشت 1405
  • تاریخ بازنگری 17 خرداد 1405
  • تاریخ پذیرش 30 خرداد 1405