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Journal ArticleDOI

Prediction of drilling rate of penetration (ROP) using hybrid support vector regression: A case study on the Shennongjia area, Central China

TL;DR: A novel intelligent model to predict the drilling ROP considering the process characteristics and the results demonstrate that the proposed method outperforms eight well-known methods and another three methods without different data preprocessing procedures in prediction accuracy.
About: This article is published in Journal of Petroleum Science and Engineering.The article was published on 2019-10-01. It has received 46 citations till now. The article focuses on the topics: Rate of penetration & Data pre-processing.
Citations
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Journal ArticleDOI
TL;DR: A new hybrid bat algorithm (HBA) is proposed to achieve the maximum ROP accurately by combining the wavelet filtering and optimized support vector regression according to the drilling characteristics and five modifications are combined to further improve the global optimization performance of the bat algorithm.
Abstract: Rate of penetration (ROP) optimization is crucial for drilling processes due to its vital role in increasing efficiency. This article proposes a new hybrid bat algorithm (HBA) to achieve the maximum ROP accurately. A data-driven ROP model is established by combining the wavelet filtering and optimized support vector regression according to the drilling characteristics. After that, five modifications, namely, iterative local search, stochastic inertia weight, pulse rate and loudness improvement, directional echolocation, and modified local random walk are combined to further improve the global optimization performance of the bat algorithm. Extensive experiment results on the IEEE Congress on Evolutionary Computation 2005 standard benchmark problems show that the proposed algorithm has successful outcomes compared with ten conventional algorithms. Additionally, in the application of the HBA to a real-world drilling process in Shennongjia area, Central China, the ROP has been improved by 34.84%, which is the largest compared with the conventional popular ROP optimization methods.

38 citations


Cites background or methods from "Prediction of drilling rate of pene..."

  • ...The data-driven ROP model is built based on the training and validation data [6]....

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  • ...Ninety-five groups of drilling data [6] are collected to optimize the ROP....

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  • ...The distribution of training, validation, and test sets can be founded in [6]....

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  • ...It plays an important role in oil, gas, and geothermal drilling engineering [4]–[6] in reducing costs and increasing drilling efficiency....

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  • ...basis function is the dmey function [6]....

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Journal ArticleDOI
TL;DR: The small difference between the obtained levels of error in the training and testing stages with the LSSVM-COA model, as compared to the other models, revealed that the model can be used to predict the ROP at other wells across the field reliably and accurately provided the model be developed with larger sets of data across theField.

35 citations

Journal ArticleDOI
TL;DR: An advanced control system is developed to mitigate torsional vibration of drill-string, using a measurement-while-drilling (MWD) communication tool to collect downhole information and modeled as a nonlinear time-delay system.

24 citations

Journal ArticleDOI
TL;DR: This two-phase system leverages existing drilling data and real-time adjustments to optimize drilling efficiency through the adjustment of controllable dynamic drilling parameters; weight on bit (WOB), pump flow rate (GPM), and rotary speed (RPM).

23 citations

Journal ArticleDOI
TL;DR: A new empirical equation for predicting the ROP in real-time using different artificial intelligence (AI) techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) is developed.

16 citations

References
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Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations

Journal ArticleDOI
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

10,696 citations

01 Jan 2007
TL;DR: An attempt has been made to review the existing theory, methods, recent developments and scopes of Support Vector Regression.
Abstract: Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields - time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.

1,467 citations

Journal ArticleDOI
TL;DR: The proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions and there is a real application of the proposed method in optical engineering called optical buffer design that evidence the superior performance of BBA in practice.
Abstract: Bat algorithm (BA) is one of the recently proposed heuristic algorithms imitating the echolocation behavior of bats to perform global optimization. The superior performance of this algorithm has been proven among the other most well-known algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). However, the original version of this algorithm is suitable for continuous problems, so it cannot be applied to binary problems directly. In this paper, a binary version of this algorithm is proposed. A comparative study with binary PSO and GA over twenty-two benchmark functions is conducted to draw a conclusion. Furthermore, Wilcoxon's rank-sum nonparametric statistical test was carried out at 5 % significance level to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The results prove that the proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions. In addition, there is a real application of the proposed method in optical engineering called optical buffer design at the end of the paper. The results of the real application also evidence the superior performance of BBA in practice.

549 citations

Journal ArticleDOI
01 Oct 2014
TL;DR: The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data.
Abstract: A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.

364 citations