• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.204-206
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• 2021

Real-time drilling parameters prediction is a considerably important study from the viewpoint of maximizing drilling efficiency. Among the methods of maximizing drilling, the method of improving the drilling speed is common, which is related to the rate of penetration, drillstring rotational speed, weight on bit, and drilling mud flow rate. This study proposes a method of predicting the drilling rate, one of the real-time drilling parameters, using a recurrent neural network-based deep learning model, and compares the existing physical-based drilling rate prediction model with a prediction model using deep learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1032-1038
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• 2021

Rate of penetration(ROP) is one of the important variables for maximizing the drilling performance. In order to maximize drilling efficiency, it is necessary to increase the drilling speed, and real-time ROP prediction is important so that the driller can identify problems during drilling. The ROP has a high correlation with the drillstring rotational speed, weight on bit, and flow rate. In this paper, the ROP was predicted using a data-driven supervised learning model trained from the drilling efficiency parameters. As a result of comparison through the performance evaluation metrics of the regression model, the root mean square error(RMSE) of the RF model was 4.20 and the mean absolute percentage error(MAPE) was 9.08%, confirming the best predictive performance. The proposed method can be used as a base model for ROP prediction when constructing a real-time drilling operation guide system.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.66-75
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• 2020

Drilling operation is the most important and costly essential work in oil and gas exploration and development. Therefore, the studies about rate of penetration have been carried out continuously to improve drilling efficiency. In recent years, data-driven models have been developed by various researchers to overcome disadvantages of traditional mathematical models. For the data-driven models, selecting proper algorithms and parameters is very important. In addition, data-driven models should be retrained in real-time during continuous drilling operations in order to improve the model performance. In this paper, the latest studies are investigated to provide information about algorithms, drilling parameters and model retraining intervals that used in drilling optimization.

• Tunnel and Underground Space
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• v.18 no.6
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• pp.480-490
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• 2008

The drilling monitoring is a technique to assess rock mass properties by analyzing the mechanical quantities measured by drilling process. Since drilling survey can be conducted on real-time-basis for excavating blast holes or rockbolt holes, it may enables fast and quantitative prediction and evaluation of rock mass. Though a number of studies have been conducted on the drilling data, the selection of drilling parameters and numerical quantification of mechanical quantities or rock mass have not been well established yet. In this study, drilling tests were conducted with homogeneous rock specimen to identify drilling parameters and the relation of the drilling data. As a result, it is verified that above all drilling parameters, the percussion was the most important factor on the excavatability of hydraulic drilling.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.5
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• pp.8-17
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• 2016

While greenhouses have been utilized as a sustainable alternative to traditional soil farming, they are often powered by diesel boilers that necessitate vast amounts of non-renewable energy and emit toxic fumes. Thus, geothermal heat pumps have been proposed as a more energy-efficient substitution for diesel boilers. Currently, most horticultural facilities in the United States use shallow geothermal systems, and are often equipped with horizontal underground heat exchangers as well as heat pump equipment. These shallow geothermal systems require a large drilling site and heat pump to function, which results in high maintenance costs. The heat pump itself consumes a large amount of power, which degrades system performance. Conversely, high temperatures can be attained within a single borehole in deep geothermal vertical closing systems without using a heat pump. This setup can dramatically reduce the power consumption and improve system performance. In this study, we have modeled a circulation simulator after the circulation systems in deep geothermal facilities to analyze a 2000-meter borehole in Naju-Sanpo-myeon. The simulator is operated by manipulating various putative parameters affecting system performance to analyze the system's coefficient of performance.