An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling

Table 1

Summary of ROP prediction models with artificial intelligence.

Reference	Model	Input number	Input layer	Output layer
Zhang et al. [22]	AHP and BPANN	9	UCS, bit size, bit type, drillability coefficient, gross hours drilled, WOB, RPM, drilling mud density, and AV (Apparent Viscosity)	ROP
Jahanbakhshi et al. [13]	ANN	20	Differential pressure, hydraulics, hole depth, pump pressure, density of the overlying rock, equivalent circulating density, hole size, formation drillability, permeability and porosity, drilling fluid type, plastic viscosity of mud, yield point of mud, initial gel strength of mud, 10 min Gel strength of mud bit type and its properties, weight on the bit and rotary speed, bit wear, and bit hydraulic power	ROP
Bahari and Seyed [11]	Fuzzy	4	UCS, rock quality designation, bit Load, and bit rotation	ROP
Amar and Ibrahim [25]	Radial-basis function and ELM	7	Depth, bit weight, rotary speed, tooth wear, Reynolds number function, ECD, and pore gradient	ROP
Bilgesu et al. [12]	ANN	9	Formation drillability, formation abrasiveness, bearing wear, tooth wear, pump rate, rotating time, rotary torque, WOB, and rotary speed	ROP
Bataee and Mohseni [2]	ANN	4	Bit diameter, WOB, RPM, and mud weight	ROP
Moran et al. [26]	ANN	6	Rock strength, rock type, abrasion, WOB, RPM, and mud weight	ROP and wear
Monazami et al. [27]	ANN	13	Drill collar outside diameter, drill collar length, kick of point, azimuth, inclination angle, weight on bit, flow rate, bit rotation speed, mud weight, solid percent, plastic viscosity, yield point, and measured depth	ROP