New Correlations of Dew-Point Pressure for Gas Condensate Reservoirs Using Hybrid Modelling

Salem O. Baarimah; Ghareb Hamada; Khaled Ba-Jaalah; Abdelrigeeb Al Gathe; Wahbi Al-Ameri

doi:10.52716/jprs.v16i2.1233

المؤلفون

Salem O. Baarimah قسم هندسة البترول، كلية الهندسة والبترول، جامعة حضرموت، اليمن.
Ghareb Hamada هندسة النفط والغاز، الأكاديمية العربية للعلوم والتكنولوجيا والنقل البحري، الإسكندرية، مصر.
Khaled Ba-Jaalah قسم هندسة البترول، كلية الهندسة والبترول، جامعة حضرموت، اليمن.
Abdelrigeeb Al Gathe قسم هندسة البترول، كلية الهندسة والبترول، جامعة حضرموت، اليمن.
Wahbi Al-Ameri قسم هندسة البترول، كلية الهندسة والبترول، جامعة حضرموت، اليمن.

DOI:

https://doi.org/10.52716/jprs.v16i2.1233

الكلمات المفتاحية:

Artificial intelligences, Nonlinear multiple regression, Hybrid; PSONN, Dew-point Pressure.

الملخص

يُعد ضغط نقطة الندى (Dew-Point Pressure, DPP) من الخصائص الأساسية في تطوير مكامن الغاز المتكثف وإدارة موائع الغاز المتكثف. وقد استُخدمت العديد من العلاقات الرياضية المنفردة وتقنيات الأنظمة الذكية للتنبؤ بهذه الخاصية بدقة جيدة، إلا أن تطبيق النماذج الهجينة في هذا المجال ما يزال محدوداً نسبياً. لذلك، تقترح هذه الدراسة استخدام أسلوب الانحدار المتعدد غير الخطي (Nonlinear Multiple Regression, NLMR) ونماذج ذكية هجينة للتنبؤ بدقة بضغط نقطة الندى. وتعتمد التقنية الهجينة المقترحة على دمج خوارزمية تحسين سرب الجسيمات (Particle Swarm Optimization, PSO) مع الشبكات العصبية الاصطناعية (Neural Networks, NN)، فيما يُعرف بنموذج (PSONN).

تم استخدام ما يقارب 900 نقطة بيانات جُمعت من مصادر مختلفة لتطوير هذه النماذج الهجينة. وقد استخدمت درجة الحرارة (T)، وتركيب الهيدروكربونات، والكثافة النوعية (SG)، والوزن الجزيئي (Mw) لمركب الهيبتان وما فوقه (+C7) كمتغيرات إدخال للتنبؤ بضغط نقطة الندى (DPP).

كما جرت مقارنة أداء كل من نموذج الانحدار المتعدد غير الخطي (NLMR) والنموذج الهجين (PSONN) مع أداء أبرز العلاقات الارتباطية التجريبية ونماذج الذكاء الاصطناعي المنشورة في الأدبيات العلمية. وأظهرت نتائج التحليل الإحصائي للأخطاء أن نماذج (PSONN) الهجينة المقترحة تفوقت على نموذج (NLMR) وعلى معظم العلاقات الارتباطية التجريبية ونماذج الذكاء الاصطناعي المنشورة سابقاً. كما أكدت النتائج أن نموذج (PSONN) حقق أفضل أداء، حيث سجل متوسط الخطأ النسبي المطلق (APRE) مقداره 2.45%، بالإضافة إلى أعلى معامل ارتباط (CC) بلغت قيمته 0.997، مما يدل على دقة تنبؤية عالية وموثوقية كبيرة في تقدير ضغط نقطة الندى لمكامن الغاز المتكثف.

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