شماره ركورد كنفرانس :
2953
عنوان مقاله :
Correction of Drill-off Test Error Using Artificial Neural Network and Mechanical Specific Energy
عنوان به زبان ديگر :
Correction of Drill-off Test Error Using Artificial Neural Network and Mechanical Specific Energy
پديدآورندگان :
Yavari Hossein نويسنده , Fazaelizadeh Mohammad نويسنده , Khosravanian Rasool نويسنده , Hassani Vahab نويسنده
كليدواژه :
Rate of Penetration , Artificial neural network , CCS , Weight on Bit , mechanical efficiency , Mechanical Specific Energy
عنوان كنفرانس :
دومين كنفرانس ملي ژئومكانيك نفت : كاهش مخاطرات اكتشاف و توليد
چكيده لاتين :
Drill-off test is used to determine the optimum WOB and RPM during drilling. In this test the WOB that gives the highest ROP is considered as the optimal value. In 1991, Bourgoyne et.al introduced the maximum rate of penetration point as a non-optimal point because at this point available hydraulic cant clean underneath the bit and the bit crushes cuttings from previous step for another time, causing over wearing of the bit, bit balling and vibrations. The point that has the minimum MSE and maximum mechanical efficiency is the optimal point. This point is called founder point
شماره مدرك كنفرانس :
4411868