Optimization of drilling penetration rate through the optimal design of drilling leg and mechanical and hydraulic parameters of drilling using energy characteristic method (case study: South Pars gas field)
MohammadReza Nouri
1
(
Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran
)
Mojtaba Rahimi
2
(
Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran
)
Ali Mokhtarian
3
(
Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran
)
الکلمات المفتاحية: Rate of penetration (ROP), Optimal design, Mechanical and hydraulic parameters, Energy characteristic method, South Pars gas field.,
ملخص المقالة :
The purpose of this study was to describe the existing drilling techniques and deal with the optimization method for drilling a new well in the South Pars gas field. First, a well plan was presented to start the optimization, and then, its final three holes (including 16, 12 ¼, and 8 ½-inch holes) were modeled in Landmark software. During modeling in Landmark, the well profile, BHA (Bottom Hole Assembly), drilling fluid properties, drilling hydraulics, effective drag, and surface torque were simulated and then optimized based on operational constraints (such as pump capacity) and mechanical constraints. Furthermore, the mechanical energy characteristic method was used to study the correlations between the actual rate of penetration (ROP) obtained based on field data and theory, and the artificial neural network was used to optimize the drilling process. Landmark results indicated that the drilling of 16, 12 ¼, and 8 ½-inch holes was limited by the selection of mud characteristics, so the optimal values of plastic viscosity (PV), yield point (YP), revolutions per minute (RPM), and mud pumping volume per minute (GPM) were calculated. For each hole, the results from the modeling and optimization of the artificial neural network showed an excellent correlation between drilling parameters and ROP for the 12 ¼ and 8 ½-inch sections, while the correlation was very good for the 16-inch section. The results of this study can be applied to a real drilling process to maximize drilling efficiency.
