Machine Learning and Flow Zone Indicator for Permeability Estimation in Uncored Intervals of a Southern Iraqi Oil Field

Mohammed Rajaa; Ayad A. Al-haleem

doi:10.52716/jprs.v16i3.1075

المؤلفون

Mohammed Rajaa جامعة بغداد، كلية الهندسة، قسم هندسة النفط
Ayad A. Al-haleem جامعة بغداد، كلية الهندسة، قسم هندسة النفط

DOI:

https://doi.org/10.52716/jprs.v16i3.1075

الكلمات المفتاحية:

Flow Zone Indicator, hydraulic flow unit, Machine Learning, Permeability.

الملخص

إن التقدير الدقيق للنفاذية مهمة صعبة بسبب طبيعة الخزانات غير المتجانسة. ونظراً للتكاليف العالية لأخذ اللباب وضعف دقة طريقة المعادلة الخطية البسيطة، اعتبرت طريقة مؤشر منطقة التدفق (FZI) واحدة من أكثر التقنيات استخداماً في الحقول العراقية في السنوات الأخيرة للتنبؤ بالنفاذية. ومع ذلك، تتطلب هذه الطريقة التقنيات الإحصائي أو التعلم الآلي للتنبؤ (FZI) في الاعماق غير مستخرجة اللباب. تهدف هذه الدراسة إلى تحديد نموذج التعلم الآلي الأكثر ملاءمة للتنبؤ بال(FZI) في الاعماق غير مستخرجة اللباب باستخدام سجلات الآبار التقليدية كمدخلات. بعد ذلك، يتم تقسيم الخزان إلى وحدات تدفق هيدروليكية (HFUs) باستخدام التعلم غير الخاضع للإشراف. على وجه التحديد، يتم استخدام AutoGluon كإطار عمل تعلم الآلي المؤتمت لإيجاد أفضل خوارزمية ومعلمات فائقة مثالية لتقدير (FZI). تم استخدام خوارزمية k-means لتصنيف الخزان إلى وحدات تدفق هيدروليكية. تم تطبيق هذا النهج المتكامل على دراسة حالة ميدانية في جنوب العراق. استخدمت الدراسة كل من البيانات الأساسية والسجلية من ثلاث آبار. وأظهرت النتائج أن الشبكة العصبية (NN) تفوقت على الخوارزميات الأخرى ضمن الإطار، حيث حققت أكثر من R² بنسبة 90٪ وخطأ جذر متوسط التربيع (RMSE) ب 1.69 على بيانات الاختبار، وأكثر من R² بنسبة 96٪ وRMSE ب 0.493 على بيانات التدريب. من خلال تطبيق الارتباطات المشتقة لكل HFU، حقق التنبؤ بالنفاذية R² بنسبة 85٪ وRMSE بنحو 0.37. وهذا يوضح فعالية إطار التعلم الآلي المؤتمت في تحسين تقدير FZI، وبالتالي التنبؤ بالنفاذية

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