تطبيق استخدام التعلم الآلي على إدارة الرمال والخصائص الجيوميكانيكية: دراسة حالة في تكوين نهر عمر، جنوب العراق

المؤلفون

  • علي محمود عيال جامعة بغداد / كلية الهندسة
  • ضفاف جعفر صادق Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
  • Dennis Delali Kwesi Wayo Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan, and Universiti Malaysia Pahang Al-Sultan Abdullah, Faculty of Chemical and Process Engineering Technology, Kuantan, Malaysia.

DOI:

https://doi.org/10.52716/jprs.v15i1.927

الكلمات المفتاحية:

Sand production, CDDP, Sand Management Model, Machine Learning, Wellbore Stability.

الملخص

يشكل إنتاج الرمال تحديات كبيرة في صناعة النفط والغاز الطبيعي، مما يتطلب تحليلًا جيوميكانيكيًا شاملاً لتقليل تأثيره في الحقول القائمة. يمثل غياب سجلات الآبار الحاسمة تحديًا أساسيًا في صناعة النفط والغاز، مما يستلزم من الجيولوجيين والمهندسين الاعتماد على المعادلات التجريبية للتنبؤ بغياب تسجيلات الابر.

لقد أجرينا تحليلاً شاملاً للنمذجة الجيوميكانيكية باستخدام جميع السجلات التي تم الحصول عليها من بئرين يقعان في تكوين نهر عمر في جنوب العراق. كان هدفنا تحديد الخصائص الجيوميكانيكية والتنبؤ بحدوث إنتاج الرمال. بالإضافة إلى ذلك، قمنا بالتحقق من صحة المعلمات الجيوميكانيكية المستخدمة في نموذجنا التنبؤي من خلال قياسات. caliper استخدمنا تقنية التعلم الآلي للتنبؤ بغياب السجل الصوتي في Well-5 كبديل للمعادلات التجريبية. علاوة على ذلك، قمنا بإنشاء ومقارنة نموذجين لإدارة الرمال - أحدهما يستخدم معادلة Gardner التجريبية والآخر يطبق تقنيات التعلم الآلي (ML) . نموذج إدارة الرمال المبني على معادلة Gardner تنبأ بإنتاج الرمال منذ البداية. ومع ذلك، فإنها لم تتطابق مع بيانات الإنتاج الفعلية التي تمت ملاحظتها في الحياة الواقعية. ومن ناحية أخرى، أشار النموذج القائم على التعلم الآلي إلى عدم وجود احتمال لتوليد الرمال، وهو ما يتماشى مع بيانات الإنتاج المرصودة.

توضح نتائج هذه الدراسة مزايا استخدام التعلم الآلي على المعادلات التجريبية التقليدية للدراسات الجيوميكانيكية في المنطقة المحددة قيد الدراسة. وتشير هذه النتائج أيضًا إلى إمكانية تطبيق تقنيات التعلم الآلي على المزيد من الأحواض في جنوب العراق. يعمل البحث الحالي على تحسين فهمنا لتأثير التعلم الآلي على إدارة الرمال وكذلك التوصيف الجيوميكانيكي. تتمتع هذه الدراسة بالقدرة على تعزيز إجراءات اتخاذ القرارات في صناعات النفط والغاز الطبيعي والمساهمة بمعارف قيمة في تحسين طرق التعامل مع مشاكل إنتاج الرمال.

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التنزيلات

منشور

2025-03-21

كيفية الاقتباس

(1)
Ayal, A. M. .; Sadeq, D. J. .; Kwesi Wayo, D. D. تطبيق استخدام التعلم الآلي على إدارة الرمال والخصائص الجيوميكانيكية: دراسة حالة في تكوين نهر عمر، جنوب العراق. Journal of Petroleum Research and Studies 2025, 15, 74-93.