Predictive Maintenance in the Oil and Gas Industry: A Literature-Based Analysis of Techniques and Their Impact on Equipment Reliability

Abaas Abdulmaged

doi:10.52716/jprs.v16i3.1117

المؤلفون

Abaas Abdulmaged وزارة النفط، الشركة العامة لتعبئة وخدمات الغاز.

DOI:

https://doi.org/10.52716/jprs.v16i3.1117

الكلمات المفتاحية:

Accuracy, Machine Learning, Oil and Gas, Predictive Maintenance, Reliability.

الملخص

يعتمد الإمداد العالمي للطاقة بشكل كبير على صناعة النفط والغاز، ومع ذلك، تتسبب الأعطال غير المتوقعة للمعدات في عواقب مالية كبيرة إلى جانب تعطيل العمليات التشغيلية. لقد اكتسبت الصيانة التنبؤية (PdM) شعبية بسبب قدرتها على تعزيز الموثوقية مع خفض التكاليف من خلال استخدام مستشعرات إنترنت الأشياء (IoT) وتطبيقات تعلم الآلة للبيانات في الوقت الفعلي. تظهر الأبحاث الأكاديمية الحالية محدودية فعالية هذه الطرق في المجالات الصناعية بسبب الظروف التشغيلية القاسية وتعقيد طرق جمع البيانات وتكاليف التنفيذ المرتفعة. يعتمد هذا التحليل على مراجعات منهجية لتقنيات الصيانة التنبؤية المستخدمة في صناعة النفط والغاز لتقييم فائدتها وتحديد التحديات التشغيلية أثناء التطبيق. تمثل الإنجاز الأساسي لهذه الدراسة في تقديم تقييم شامل لأساليب الصيانة التنبؤية، حيث تم قياس دقتها وقدرتها على توفير التكاليف، إلى جانب إظهار التحسينات في موثوقية المعدات. اتبع فريق البحث إرشادات PRISMA لإجراء مراجعة منهجية شملت 1,842 عملًا أكاديميًا خضع لمراجعة الأقران خلال الفترة من 2015 إلى 2023، وتم اختيار 1,325 دراسة بعد استبعاد المكررات. يكشف تطبيق الصيانة التنبؤية في شركات مثل أرامكو وشركة شل وإكسون موبيل أن هذه الشركات يمكنها تحقيق توفير سنوي يقدر بـ 2.8 مليون دولار، بالإضافة إلى تحسين متوسط الوقت بين الأعطال (MTBF) بنسبة 28%. تظهر الدراسات أن خوارزمية Random Forest حققت دقة بنسبة 85.7%، بينما تكتشف شبكة LSTM الأعطال مبكرًا بنسبة 22% مقارنة بالأنظمة التقليدية. تؤكد نتائج الاختبارات الإحصائية (بما في ذلك تحليل التباين ANOVA ومنحنيات ROC) أن الصيانة التنبؤية المدعومة بالذكاء الاصطناعي تقدم نتائج أفضل مقارنة بالأساليب التقليدية. كما يوضح توزيع ويبل (Weibull) تحسنًا في الموثوقية، حيث تظهر المعدات فرصة بنسبة 63.2% للتعطل بعد 2000 ساعة من التشغيل

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