Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

Gan, Chao; Cao, Weihua; Wu, Min; Liu, Kang‐Zhi; Chen, Xin; Hu, Yule; Ning, Fulong

doi:10.1016/j.asoc.2019.04.020

Cited by 52 publications

(5 citation statements)

References 37 publications

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“…(drill method) is a teaching method by inviting students to a skill training area to see how to make something, how to use it, what it is made for, what are the benefits and so on (Fitri & Yogica, 2018). The advantages of using the drill method in learning are as follows (Gan et al, 2019): (a) Able to acquire motor skills, such as writing, pronouncing letters, making and using tools. (b) Able to acquire mental skills, such as in multiplication, addition, subtraction, division, signs/symbols, and so on.…”

Section: Drilling Methodsmentioning

confidence: 99%

The Drilling Method Application Using Abacus to Arithmetic Operations Skills in Student with Hearing Impairment at Special School

Rusyani

Ratnengsih

Putra

et al. 2021

Indones. J. Community. Spec. Needs. Educ.

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The purpose of this study was to determine the effect of the drilling method application using abacus in improving arithmetic subtraction operations skills in deaf children. This study used an experimental method with Single Subject Research (SSR), A-B-A design. The drilling method was used in the learning process. In addition, the abacus media was also used in the subtraction arithmetic operation material in learning mathematics. The results showed that there was an effect of applying the drilling method using abacus in improving the arithmetic subtraction operation skills in deaf children with the initials SS. This could be seen from the increase in the mean level of student’s abilities starting from baseline-1 (A1) by 30%, intervention (B) by 65%, and baseline-2 (A2) by 86.66%. The methods and media were suitable to make the lesson easier to understand for students. The drilling method with the abacus media could be used as an alternative for teachers in improving the skills of arithmetic subtraction operations in deaf children. The results of this study are expected to contribute to developing special education related to the application of the drilling method using abacus media to improve arithmetic operations skills in learning next math.

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Section: Drilling Methodsmentioning

confidence: 99%

The Drilling Method Application Using Abacus to Arithmetic Operations Skills in Student with Hearing Impairment at Special School

Rusyani

Ratnengsih

Putra

et al. 2021

Indones. J. Community. Spec. Needs. Educ.

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“…In addition, Yavari et al employed neuro-fuzzy technique to analyze ROP data [39]. In a recent study, Gan et al combined the SVM, mutual information analysis, and wavelet filtering to create a hybrid ROP model [40].…”

Section: Data-driven Rop Modelsmentioning

confidence: 99%

“…Recently, ROP prediction by the AI techniques is one of the most reliable methods delivering acceptable results [29][30][31][32]. Some valuable works related to AI-based ROP models can be found in [33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering

Khalilidermani

Knez

2023

Energies

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In drilling engineering, the rate of penetration (ROP) is a prevalent indicator to evaluate the energy efficiency of drilling operations. Nowadays, ROP prediction has become more critical since the production from deeper hydrocarbon resources is unprecedentedly escalating. So far, a wealth of theoretical and practical investigations has been conducted to develop ROP models; however, the existing models have not been adequately updated with the new technological advancements or geological restrictions. This research strives to integrate the latest advancements, restrictions, and future requirements in ROP prediction. To do this, the existing empirical and data-driven ROP models are elaborated and compared. From the conducted research, it was deduced that four uncontrollable factors, including the rock permeability, wellbore inclination, temperature, and rock hardness, have not been adequately considered in ROP models. Moreover, although data-driven ROP models deliver more accurate results than the empirical models, the determination of the number and type of the input parameters is still challenging. To tackle this issue, it is recommended to develop a formation-based classification system of input parameters for future investigations. This inclusive review can be adopted by the companies and engineers involved in drilling operations to update and reform their drilling strategies.

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“…Mechanical drill strings are installed at the drill site containing drill bits at its bottom tip for crushing the subsurface rock layers. To control and monitor the oil and gas field operations efficiently, various sensors are installed on the drill rig and string to capture five types of variables namely seismic parameters, operational parameters, mud logging parameters, well logging parameters, and formation characteristics parameters [44]. Table 3 contains the statistical description of operational and mud logging parameters utilized for the selection of drill bit types.…”

Section: Data Descriptionmentioning

confidence: 99%

A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry

2021

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Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that the random forest with bootstrap class weighting technique has given the most impressive performance for bit type selection with testing accuracy ranges from 92% to 99%, and G-mean (0.84–0.97) in critical to normal experimental scenarios. This study provides an approach to automate the drill bit selection process over any field, which will minimize human error, time, and drilling cost.

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Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

Cited by 52 publications

References 37 publications

The Drilling Method Application Using Abacus to Arithmetic Operations Skills in Student with Hearing Impairment at Special School

The Drilling Method Application Using Abacus to Arithmetic Operations Skills in Student with Hearing Impairment at Special School

A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering

A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry

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