Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field

نور الهدى كاظم  محمد; غانم مديح  فرمان

doi:10.52716/jprs.v14i1.777

المؤلفون

نور الهدى كاظم محمد Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
غانم مديح فرمان Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.52716/jprs.v14i1.777

الكلمات المفتاحية:

Machine learning, Random forest, Multiple linear regression, Permeability prediction.

الملخص

يعتبر تقييم النفاذية خطوة حيوية في هندسة المكامن بسبب تأثيرها على كل من توصيف المكامن والتخطيط للتثقيب والكفاءة الاقتصادية للمكمن. يعتمد توقع نفاذية المكمن على بيانات سجل البئر و تحليل اللباب. هناك طرق متعددة للتنبؤ بالنفاذية مثل الطرق الكلاسيكية، الطرق التجريبية والطرق الإحصائية باستخدام التعلم الآلي. في هذا البحث تمت استخدام و مقارنة خوارزمية التعلم الالي (الغابات العشوائية Random Forest) و (الانحدار الخطي المتعدد Multiple Linear Regression) للتنبؤ بالنفاذية في مكمن مودود في حقل (BH) شمال العراق. كانت خوارزمية الغابات العشوائية Random Forestهي الأكثر دقة ولديها خطأ تنبؤ مربع متوسط الجذر (RMSPE) أقل من خوارزمية الانحدار الخطي المتعددة مع قيمة معامل ترابط احصائي عالية تبلغ 0.965 في مجموعة بيانات التدريب و 0.962 في مجموعة بيانات الاختبار. نتيجة لذلك، توفر خوارزمية Random Forest طريقة مثالية للتنبؤ بالنفاذية في المناطق غير مأخوذ فيها لباب non-core interval و توزيعها في النموذج الجيولوجي.

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