Is Support Vector Regression method suitable for predicting rate of penetration?

Kor, Korhan; Altun, Gürşat

doi:10.1016/j.petrol.2020.107542

Cited by 33 publications

(9 citation statements)

References 15 publications

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“…It is used for the prevention of over-fitting of a model, which is often caused through fitting noise into the model. It is also used for the prevention of bias selection when training a set of data [96][97][98]. Cross-validation method comes in different types.…”

Section: Cross-validationmentioning

confidence: 99%

“…The procedure is repeated by selecting another dataset from the k folds as validation dataset and the remainder as training dataset until every fold is used once as validation dataset [98,[100][101][102]. The widely used k value is 10, which is termed as "tenfold cross-validation" [96]. The same value of k was used in the present study.…”

Section: Cross-validationmentioning

confidence: 99%

See 1 more Smart Citation

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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Prediction methods for the estimation of the resilient modulus( ) of unbound granular materials continues to be fraught with challenges associated with the in situ dependency of the existing methods, which makes them unreliable when considering the spatial variability of soil properties. Moreover, the artificial intelligence methods, which prove to be superior for prediction, indicate that more recent and hybrid machine learning methods are more apt. Consequently, a comparative analysis was executed in the present study using advanced statistical approaches and a simple routine method for the prediction of the of coarse-grained soils used as unbound granular materials in highway pavements. This study considered also the spatial variability of the routine soil properties based on the long-term pavement performance database. The result of the descriptive analysis conducted using principal component analysis revealed that the variation in is due to three principal factors, which include the effect of moisture on the fines content, effect of coarse particles on the compaction characteristics and the effect of the soil stress state. Subsequently, prediction of the using multiple linear regression was unreliable based on residual analysis and multi-collinearity problems. Thus, predictive modeling of was then executed using artificial neural network (ANN) and ensemble techniques (random forest and gradient boosting machine), which resulted in very high R 2 values above 0.9, considered apt for MEPD. The ANN model was found to provide a superior prediction performance than the ensemble techniques.

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Section: Cross-validationmentioning

confidence: 99%

Section: Cross-validationmentioning

confidence: 99%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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show abstract

“… 18 For example, Ahmed et al 19 used an artificial neural network (ANN) to predict ROP. Kor et al 20 compared different prediction methods based on a statistics viewpoint and proved the effectiveness of support vector machine regression in ROP prediction. Ashrafi et al 21 obtained the weights of each neuron connection of the ANN through an optimization algorithm, thus replacing the backpropagation algorithm.…”

Section: Adaptation Function Setting Based On the Mlp Neural Networkmentioning

confidence: 99%

Improving the Drilling Parameter Optimization Method Based on the Fireworks Algorithm

Yang

Ren

et al. 2022

ACS Omega

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The rate of penetration (ROP) is a manifestation of drilling efficiency, and optimizing drilling parameters is an important way to improve it. To achieve a low ROP for a Permian formation in a certain oil and gas field, three single wells in this formation were selected for optimization. An improved fireworks optimization algorithm was proposed for drilling parameter optimization. We first established the objective function that predicted the ROPs for the three wells. The objective function employed a multilayer perceptron neural network as the optimization adaptation function. We then optimized four controllable parameters (weight on bit, rotary speed, pump discharge, and pump pressure) and improved the fireworks algorithm with an adaptive number of various factors. This improvement enhanced the debugging performance of the fireworks algorithm during optimization. The results indicated that the improved fireworks algorithm has significantly enhanced search performance, and the optimum ROPs for the three wells were increased by 38.55, 78.30, and 60.15%, which provides a reference for the controllable parameter setting in the area.

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“…Mendes 12 also presented a methodology based on a neural network model for ROP and a neuro-genetic adaptive controller to address the problem that relationships between operational variables affecting ROP are complex and not easily modeled. In addition, with the boom in ML algorithms approximately 2010, more and more ML methods are being used for ROP prediction, including Moran 13 , Arabjamaloei 14 , Esmaeili 15 , Ning 16 , Zare 17 , Bodaghi 18 , Hegde 19 , Mantha 20 , Hegde 21 , Anemangely 22 , Soares 7 , Sabah 23 , Felipe 2 , Korhan 24 , Li 25 , Mohammad 26 , Gan 27 , Hazbeh 28 , Salaheldin 29 , Zhang 30 , Ren 31 , Zhang 32 , Brenjkar 33 , Riazi 34 , Song 35 , Wang 36 , Mohammad 37 , Kaveh 38 and so on. Judging from the increasing number of articles published each year in recent years on the use of machine learning for ROP prediction, it can be amply demonstrated that ML methods are well suited for application in the field of ROP prediction.…”

Section: Introductionmentioning

confidence: 99%

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

Jiao,

Li,

et al. 2024

Sci Rep

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Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models’ advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction.

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Is Support Vector Regression method suitable for predicting rate of penetration?

Cited by 33 publications

References 15 publications

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Improving the Drilling Parameter Optimization Method Based on the Fireworks Algorithm

Hybrid physics-machine learning models for predicting rate of penetration in the Halahatang oil field, Tarim Basin

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