A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering
Abstract
:1. Introduction
2. Contributing Factors on the ROP
2.1. Formation Characteristics
2.2. Mud Properties
2.3. Bit Operating Conditions
2.4. Bit Hydraulics
2.5. Personal Efficiency
2.6. Rig Efficiency
3. The ROP Models
3.1. Empirical ROP Models
3.1.1. Review on Empirical ROP Models
3.1.2. Analysis of the Empirical ROP Models
3.1.3. Shortcomings and Limitations of Existing Empirical ROP Models
- Rock permeability represents the capacity of rock to allow fluid to move through the rock pores. In nature, such fluid is water, brine, oil, etc. During drilling operations, the bit cutters break the rock, and, simultaneously, the mud pressure prevents the pore fluid from flowing into the well space. In a microscopic perspective, the bit pushes the pore fluid back through two mechanisms [74]. In the first mechanism, the mud pushes back the pore fluid directly, thereby driving it to flow backwards. This mechanism is dominantly governed by the bit-rotation speed. In the second mechanism, the pores are compressed by bit cutters, and, consequently, the pore fluid is squeezed away from the bottom hole. This mechanism is mainly governed by rock diffusivity which is directly related to the rock permeability. Therefore, since the rock permeability changes, the ROP is altered [74]. Thus, it is suggested that more investigations on the relations between rock permeability and the ROP are conducted to improve the previous empirical models or to develop new ones;
- The second less-frequent factor is the coefficient of cuttings concentration which can be utilized as an indicator of the cuttings volume accumulated at the bottom hole. This factor is especially determining in the ROP during drilling of directional wells. As mentioned in the previous section, with the increasing demands for production from unconventional reservoirs, directional (horizontal and inclined) drilling has markedly increased; however, some features of past ROP models were considered only for vertical wells. Thus, it is essential to account for those features in ROP models developed for directional wellbores. The coefficient of cuttings concentration is such an essential factor.
- The third parameter is rock hardness. Rock hardness can be defined as the rock resistance to drilling. In other words, rock hardness is reciprocal of drillability. Some researchers have linked hardness with drilling speed; other researchers related the hardness to the amount of energy required for cutting a unit volume of rock [76]. Rock hardness is mainly dependent on the hardness of the minerals, grain size, grain shape, grain distribution, and cementation material. The silica content of the rock greatly affects the rock hardness. Although other resistive features, such as compressive strength and rock abrasiveness, have been adopted more frequently in ROP models, the inclusion of rock hardness into ROP models seems to be necessary;
- Temperature is another factor influencing the penetration rate. The bit brecks the rock under a thermo–hydro–mechanical condition. During the drilling operation, the bit penetrates the deeper formations with different thermal conditions. The heat changes the poroelastic properties of the rocks as well as the characteristics of the pore fluid [63,64]. One of those important poroelastic parameters is Biot’s coefficient. When the temperature changes, Biot’s coefficient varies and has an impact on the effective stress applied on the rock at the bottom hole. As a matter of consequence, the rock compression or shear strength changes, thereby influencing the ROP. Hence, the impact of temperature should be regarded in ROP models, especially using inclusion of Biot’s coefficient as a temperature-dependent factor.
3.2. Data-Driven ROP Models
Reference | AI Technique | Input Parameters |
---|---|---|
Bilgesu, 1997 [97] | ANN | drillability; formation abrasiveness; rotary time; bearing wear; torque; tooth wear; WOB; pump rate; RPM. |
Moran, 2010 [103] | ANN | formation strength; formation abrasiveness; bit weight; RPM; drilling fluid weight; rock gense. |
Jahanbakhshi, 2012 [29] | ANN | mud type; pressure differential; hydraulic power of the bit; hydraulics; bit wear; depth; RPM; pump pressure; WOB; formation density; bit type; ECD; 10 min gel strength of drilling fluid; wellbore diameter; early gel strength of drilling fluid; drillability; drilling fluid’s yield point; permeability; drilling fluid’s plastic viscosity; porosity. |
Amar and Ibrahim, 2012 [79] | ANN | pore fluid pressure; RPM; depth; ECD; Reynolds number function; tooth wear; WOB. |
Alarfaj et al., 2012 [104] | ANN | Reynolds number; depth; WOB; RPM; tooth wear; gradient of pore pressure; ECD. |
Cui et al., 2014 [105] | ANN | apparent viscosity; unconfined compressive strength; mud density; RPM; bit geometry; WOB; bit type; gross hours drilled; drillability constant. |
Bodaghi, 2015 [100] | SVM | viscosity; mud weight; tooth wear; pump rate; bit geometry; formation; deviation of well; RPM; depth. |
Mantha and Samuel, 2016 [36] | Hybrid system | RPM; flow rate; WOB. |
Shi et al., 2016 [99] | ANN, ELM | mud properties; formation; RPM; geomechanical characteristics; WOB; hydraulics. |
Amer, 2017 [106] | ANN | WOB; mud weight; bit gense; mineralogy; IADC codes; drill-pipe pressure; bit size; mud pump; bit condition; torque; depth; RPM; TVD. |
Abdulmalek et al., 2018 [37] | SVM | yield point; WOB; solid; RPM; funnel viscosity; flow rate; plastic viscosity; standpipe pressure; mud density; torque. |
Yavari et al., 2018 [39] | Hybrid system | WOB and RPM. |
Anemangely, 2018 [33] | Hybrid system | RPM; WOB; shear wave slowness; compressional wave slowness; flow rate. |
Ahmed et al., 2018 [99] | ANN, ELM, SVM, and LS-SVR | depth; flow rate; WOB; RPM; torque; standpipe pressure; mud weight; bit diameter. |
Hegde et al., 2018 [38] | Hybrid system | RPM; UCS; flow rate; WOB. |
Gan et al., 2019 [40] | ANN | seismic velocity; depth; torque; WOB; RPM; drillability; depth; mud density. |
Elkatatny, 2021 [107] | Hybrid system | UCS; WOB; drill-pipe pressure; RPM; flow rate; torque; mud density; gamma ray; bit design; total flow area. |
Lawal, 2021 [108] | ANN | density; porosity; point load index. |
Mahdi, 2021 [98] | ANN | flow rate; WOB; RPM; bit diameter; standpipe pressure. |
Sobhi et al., 2022 [35] | Hybrid system | depth; Reynolds number; WOB; tooth wear; ECD; RPM. |
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Reference | Controllable Factors | Uncontrollable Factors | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WOB | RPM | TVD | B | ρf | Q | Pm | S | Ab | E | Pp | T | Pe | Awell | ||||||
Maurer, 1962 [14] | × | × | × | × | |||||||||||||||
Galle and Woods, 1963 [15] | × | ||||||||||||||||||
Bingham, 1965 [16] | × | × | × | × | |||||||||||||||
Bourgoyne and Young, 1974 [3] | × | × | × | × | × | × | × | × | × | × | × | × | |||||||
Warren (perfect model), 1981 [6] | × | × | × | × | |||||||||||||||
Warren (imperfect model), 1984 [7] | × | × | × | × | × | × | × | ||||||||||||
Reza and Alcocer, 1986 [64] | × | × | × | × | × | × | × | × | × | × | |||||||||
Hareland and Rampersad, 1994 [24] | × | × | × | × | × | × | × | × | × | × | |||||||||
Osgouei, 2007 [5] | × | × | × | × | × | × | × | × | × | × | × | × | × | × | × |
Reference | Bit Type | Well Direction | ||
---|---|---|---|---|
Roller-Cone Bit | Fixed-Cutter Bit | Vertical | Directional | |
Maurer, 1962 [14] | × | × | ||
Galle and Woods, 1963 [15] | × | × | ||
Bingham, 1965 [16] | × | × | ||
Bourgoyne and Young, 1974 [3] | × | × | ||
Warren (perfect model), 1981 [6] | × | × | ||
Warren (imperfect model), 1984 [7] | × | × | ||
Reza and Alcocer, 1986 [64] | × | × | × | |
Hareland and Rampersad, 1994 [24] | × | × | ||
Osgouei, 2007 [5] | × | × | × | × |
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Khalilidermani, M.; Knez, D. A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering. Energies 2023, 16, 4289. https://doi.org/10.3390/en16114289
Khalilidermani M, Knez D. A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering. Energies. 2023; 16(11):4289. https://doi.org/10.3390/en16114289
Chicago/Turabian StyleKhalilidermani, Mitra, and Dariusz Knez. 2023. "A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering" Energies 16, no. 11: 4289. https://doi.org/10.3390/en16114289
APA StyleKhalilidermani, M., & Knez, D. (2023). A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering. Energies, 16(11), 4289. https://doi.org/10.3390/en16114289