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Drilling cost optimization for extended reach deep wells using artificial neural networks
Global Petroleum reserves are currently getting depleted. Most of the newly discovered oil and gas fields are found in unconventional reserves. Hence there has arisen a need to drill deeper wells in offshore locations and in unconventional reservoirs. The depth and difficulty of drilling terrains ha...
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| Format: | Article |
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2021
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/9371 | ||
| 042 | |a dc | ||
| 720 | |a Akintola, S. |e author | ||
| 720 | |a Toheeb, T. O. |e author | ||
| 260 | |c 2021 | ||
| 520 | |a Global Petroleum reserves are currently getting depleted. Most of the newly discovered oil and gas fields are found in unconventional reserves. Hence there has arisen a need to drill deeper wells in offshore locations and in unconventional reservoirs. The depth and difficulty of drilling terrains has led to drilling operations incurring higher cost due to drilling time. Rate of Penetration is dependent on the several parameters such as: rotary speed(N), Weight-On-Bit, bit state, formation strength, formation abrasiveness, bit diameter, mud flowrate, bit tooth wear, bit hydraulics e.t.c. Given this complex non-linear relationship between Rate of Penetration and these variables, it is extremely difficult to develop a complete mathematical model to accurately predict ROP from these parameters. In this study, two types of models were developed; a predictive model built with artificial neural networks for determining the rate of penetration from various drilling parameters and an optimization model based on normalized rate of penetration to provide optimized rate of penetration values. The Normalized Rate of Penetration (NROP) more accurately identifies the formation characteristics by showing what the rate should be if the parameters are held constant. Lithology changes and pressure transition zones are more easily identified using NROP. Efficient use of Normalized Penetration Rate (NROP) reduces drilling expenses by: Reducing the number of logging trips, minimizing trouble time through detection of pressure transition zones, encouraging near balanced drilling to achieve faster penetration rate. | ||
| 024 | 8 | |a 2415-6272 | |
| 024 | 8 | |a 2415-6264 | |
| 024 | 8 | |a ui_art_akintola_drilling_2021 | |
| 024 | 8 | |a Saudi Journal of Engineering and Technology, 6(6), pp. 118-129 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/9371 | |
| 653 | |a Artificial Neural Networks | ||
| 653 | |a Extended Reach Drilling Normalized Rate of Penetration | ||
| 653 | |a Optimization model | ||
| 653 | |a Rate of Penetration | ||
| 245 | 0 | 0 | |a Drilling cost optimization for extended reach deep wells using artificial neural networks |