نشریه ژئومکانیک و ژئوانرژی

نشریه ژئومکانیک و ژئوانرژی

ارائه روش نوترکیب هوش مصنوعی جهت پیش‌بینی نرخ نفوذ با استفاده از نگاره های حفاری

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی معدن ، دانشگاه صنعتی بیرجند ، بیرجند ، ایران
2 باشگاه پژوهشگران جوان و نخبگان ، دانشگاه آزاد اسلامی ، اهواز ، ایران
چکیده
انجام فعالیت‌های حفاری، جهت دسترسی به منابع ارزشمند هیدروکربوری، ناگزیر می‌باشد. چنان که یکی از پارامترهای مهم و پرکاربرد در مبحث حفاری چاه های نفت و گاز، نرخ نفوذ مته حفاری است. در این مطالعه جهت تخمین نرخ نفوذ، از اطلاعات مربوط به سه چاه از یک میدان نفتی واقع در جنوب غرب ایران و ترکیبی از الگوریتم های نزدیک‌ترین همسایه K، الگوریتم زنبورعسل، الگوریتم کرم شب‌تاب و پرسپترون چندلایه استفاده شده است. از قابلیت های شاخص این الگوریتم نو ترکیب، کاهش نویز داده‌ها و افزایش دقت پیش بینی پارامتر موردنظر می‌باشد. نتایج نشان داد که خطای جذر میانگین مربعات برای داده‌های مربوط به آموزش، آزمایش و اعتبارسنجی به ترتیب برابر با 05/1، 52/1 و 48/1 بوده که خود نشان‌دهنده‌ی دقت عملکرد بالای این الگوریتم نو ترکیب می باشد. همچنین با بررسی ضریب پیرسون مشخص گردید که پارامتر اندازه نازل مته حفاری با مقدار نرخ نفوذ رابطه مستقیم و مقادیر چگالی، نقطه تسلیم، ویسکوزیته پلاستیک، انرژی ویژه مکانیکی و جریان ورودی گل با مقدار نرخ نفوذ رابطه عکس داشته‌اند. لذا این تحقیق، علاوه بر ارائه یک مدل نوترکیب، به بررسی پارامترهای تأثیرگذار بر روی نرخ نفوذ حفاری نیز پرداخته است. لذا محققان می‌توانند از الگوریتم معرفی‌شده در این مقاله برای پیش‌بینی سایر پارامترهای کلیدی مخزن، تولید، حفاری و ژئوفیزیک استفاده نمایند.
کلیدواژه‌ها

عنوان مقاله English

Proposing a new artificial intelligence recombination method in order to predict the rate of penetration using drilling log

نویسندگان English

Meysam Rajabi 1
Hamzeh Ghorbani 2
1 Department of Mining Engineering, Birjand University of Technology, Birjand, Iran
2 Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
چکیده English

One of the most crucial and valuable resources accessible to humanity is underground resources. Their value is so significant that they exert control over the world economy. Drilling activities are indispensable for accessing these precious resources. Among the paramount factors in the realm of oil and gas well drilling is the rate of penetration of the drill bit. This study utilizes information from three wells located in an oil field in the southwest of Iran. The algorithms newly combined in this article comprise a fusion of the K-nearest neighbor algorithms, honey bee algorithm, firefly algorithm, and multilayer perceptron. This algorithm represents a novel and unique amalgamation of potent algorithms hitherto unused for predicting this vital parameter. Notably, this method excels at reducing data noise and enhancing the accuracy of parameter predictions. The results demonstrate that the mean square error for the testing and training data sets, as well as the entire dataset, stood at 1.05, 1.52, and 1.48, respectively. These figures underscore the algorithm's high-performance accuracy. Additionally, upon scrutinizing the Pearson's coefficient it was ascertained that drilling bits nozzles significantly influenced the penetration rate. Finally, researchers can use the algorithm introduced in this paper to predict other key reservoir, production, drilling, and geophysical parameters.

کلیدواژه‌ها English

Rate of penetration prediction
Artificial intelligence
Well logs
Hybrid machine learning
Pearson's correlation
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