Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
- Ahmed K. Abbas (Iraqi Drilling Company) | Usama Alameedy (University of Baghdad) | Mortadha Alsaba (Australian College of Kuwait) | Salih Rushdi (University of Al-Qadisiyah)
- Document ID
- Society of Petroleum Engineers
- SPE International Heavy Oil Conference and Exhibition, 10-12 December, Kuwait City, Kuwait
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 1.12.6 Drilling Data Management and Standards, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.6 Drilling Operations, 1.6.9 Coring, Fishing
- Wellbore Stability Analysis, Wellbore Trajectory Optimization, Rate of Penetration
- 53 in the last 30 days
- 76 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation pore pressure, and in-situ stresses of the studied area were included as inputs. The second step was by optimizing the process using a genetic algorithm (GA), as a class of optimizing methods for complex functions, to obtain the maximum ROP along with the related wellbore trajectory (AZI and INC). Finally, the suggested azimuth (AZI) and inclination (INC) are premeditated by considering the results of wellbore stability analysis using wireline logging measurements, core and drilling data from the offset wells.
The results showed that the optimized wellbore trajectory based on wellbore stability analysis was compatible with the results of the genetic algorithm (GA) that used to reach higher ROP. The recommended orientation that leads to maximum ROP and maintains the stability of drilling deviated wells (i.e., inclination ranged between 40°—50°) is parallel to (140°—150°) direction. The present study emphasizes that the proposed methodology can be applied as a cost-effective tool to optimize the wellbore trajectory and to calculate approximately the drilling time for future highly deviated wells.
|File Size||1 MB||Number of Pages||11|
Abbas, A. K., Flori, R. E, and Alsaba, M., 2018b. Estimating Rock Mechanical Properties of the Zubair Shale Formation Using a Sonic Wireline Log and Core Analysis. J. Nal. Gas Sci. Eng., 53, 359-369. https://doi.org/10.1016/j.jngse.2018.03.018.
Abbas, A. K., Flori, R. E., Alsaba M., Dahm, H., and Alkamil, E. H., 2018c. Integrated Approach Using Core Analysis and Wireline Measurement to Estimate Rock Mechanical Properties of the Zubair Reservoir, Southern Iraq. J. Pet. Sci. Eng., 166, 406-419. https://doi.org/10.1016/j.petrol.2018.03.057.
Amer, M. M., Dahab, A. S., and El-Sayed, A. H., 2017. An ROP Predictive Model in Nile Delta Area Using Artificial Neural Networks. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 24-27 April. http://dx.doi.org/10.2118/187969-ms.
Arabjamaloei, R., and Shadizadeh, S., 2011. Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field). Petrol. Sci. Technol., 29 (16), 1637-1648. https://doi.org/10.1080/10916460902882818.
Arabjamaloei, R., Edalatkhah, S., and Jamshidi, E., 2011. A New Approach to Well Trajectory Optimization Based on Rate of Penetration and Wellbore Stability. Petrol. Sci. Technol., 29 (6), 588-600. https://doi.org/10.1080/10916460903419172.
Aslannezhad, M., Khaksar, A., and Jalalifar, H., 2015. Determination of A Safe Mud Window and Analysis of Wellbore Stability to Minimize Drilling Challenges and Non-productive Time. J. Pet. Expl. Prod. Tech., 6 (3), 493–503. http://dx.doi.org/10.1007/s13202-015-0198-2.
Batyrshin, I., Sheremetov, L., Markov, M., and Panova, A. 2005. Hybrid Method for Porosity Classification in Carbonate Formations. J. Pet. Sci. Eng., 47 (1–2): 35–50. http://dx.doi.org/10.1016/j.petrol.2004.11.005.
Bourgoyne, A., and Young, F., 1974. A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection. Society of Petroleum Engineers Journal, 14 (04), 371-384. https://doi.org/10.2118/4238-pa.
Eaton, B. A., 1969. Fracture Gradient Prediction and Its Application in Oilfield Operations. J. Petrol. Tech. 21 (10), 1353-1360. http://dx.doi.org/10.2118/2163-pa.
Gholami, R., Aadnoy, B., Foon, L. Y., and Elochukwu, H., 2017. A Methodology for Wellbore Stability Analysis in Anisotropic Formations: A Case Study from the Canning Basin, Western Australia. J. Nal. Gas Sci. Eng. 37, 341-360. http://dx.doi.org/10.1016/j.jngse.2016.11.055.
Kahraman, S., Bilgin, N., and Feridunoglu, C., 2003. Dominant Rock Properties Affecting the Penetration Rate of Percussive Drills. Int. J. Rock Mech. Min. Sci., 40 (5), 711-723. https://doi.org/10.1016/s1365-1609(03)00063-7.
Kidambi, T., and Kumar, G. S., 2016. Mechanical Earth Modeling for A Vertical Well Drilled in A Naturally Fractured Tight Carbonate Gas Reservoir in the Persian Gulf. J. Petrol. Sci. Eng., 141, 38-51. http://dx.doi.org/10.1016/j.petrol.2016.01.003.
Mantha, B., and Samuel, R., 2016. ROP Optimization Using Artificial Intelligence Techniques with Statistical Regression Coupling. Presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE. 26-28 September. http://dx.doi.org/10.2118/181382-ms.
Miranda, T., Sousa, L., Gomes, A., Tinoco, J., Ferreira, C., 2018. Geomechanical Characterization of Volcanic Rocks Using Empirical Systems and Data Mining Techniques. J. Rock Mech. Geotech. Eng., 10 (1): 138-150. http://dx.doi.org/10.1016/j.jrmge.2017.11.003.
Mohiuddin, M., Khan, K., Abdulraheem, A., Al-Majed, A., and Awal, M., 2007. Analysis of Wellbore Instability in Vertical, Directional, and Horizontal Wells Using Field Data. J. Pet. Sci. Eng., 55 (1–2), 83–92. http://dx.doi.org/10.1016/j.petrol.2006.04.021.
Najibi, A. R., Ghafoori, M., Lashkaripour, G. R., and Asef, M. R., 2017. Reservoir Geomechanical Modeling: In-Situ Stress, Pore Pressure, and Mud Design. J. Pet. Sci. Eng., 151, 31-39. http://dx.doi.org/10.1016/j.petrol.2017.01.045.
Osman, E. 2001. Artificial Neural Networks Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow. Proceedings of SPE Middle East Oil Show, Manama, Bahrain, 17-20 March. SPE-68219-MS. http://dx.doi.org/10.2523/68219-ms.
Rahimzadeh, H., Mostofi, M., Hashemi, A., and Salahshoor, K., 2010. Comparison of the Penetration Rate Models Using Field Data for One of the Gas Fields in Persian Gulf Area. Presented at the International Oil and Gas Conference and Exhibition in China, Beijing, China, 8-10 June. https://doi.org/10.2523/131253-ms.
Soares, C., Daigle, H., and Gray, K., 2016. Evaluation of PDC Bit ROP Models and the Effect of Rock Strength on Model Coefficients. J. Nat. Gas Sci. Eng., 34, 1225-1236. https://doi.org/10.1016/j.jngse.2016.08.012.
Thiercelin, M., and Plumb, R., 1994. A Core-Based Prediction of Lithologic Stress Contrasts in East Texas Formations. SPE Form. Eval. 9 (04), 251-258. http://dx.doi.org/10.2118/21847-pa.
Warren, T., 1987. Penetration Rate Performance of Roller Cone Bits. SPE Drilling Engineering, 2 (01), 9-18. https://doi.org/10.2118/13259-pa.
Winters, W. J., Warren, T. M., and Onyia, E. C., 1987. Roller Bit Model with Ductility and Cone Offset. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, 27-30 September. https://doi.org/10.2118/16696-MS.
Zhang, J., 2011. Pore Pressure Prediction from Well Logs: Methods, Modifications, and New Approaches. Earth Sci. Rev. 108 (1–2), 50-63. http://dx.doi.org/10.1016/j.earscirev.2011.06.001.