| Autor: |
Igor Chebuniaev, Arseniy Gruzdev, Maxim Karpenko, Vitaly Koryabkin, Yuriy Simonov, Vasily Vasilyev, Artyom Semenikhin, Timur Baybolov |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Day 2 Wed, October 30, 2019. |
| Popis: |
Summary In this paper we present a new data-driven methodology for a drilling bit position and direction determination. The model is based on machine learning approach and trained on a data collected in a real-time or near real-time: mechanical parameters of drilling, tool-face data, MWD/LWD data, etc. The proposed methodology might be an interest for directional drilling service companies, operator companies that develop low-thickness productive strata. One of the main advantages of the proposed approach is economic efficiency which it provides due to absence of additional costs associated with payments for additional man hours for precise trajectory and direction monitoring. Methodology allows to predict trajectory at any time of drilling. The methodology is illustrated on the historical data of drilling of one oilfield. At the current stage, the results of the testing show good quality. Blind test on 154 independent sliding episodes shows that median absolute error (MedAE) of depth, inclination and azimuth are 0.26 m, 0.25° and 0.42°. These errors will decrease after adding more wells and steps, which are described in future plans. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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