A New Approach for the Prediction of Rate of Penetration (ROP) Values

Bilgesu, H.I.; Tetrick, L.T.; Altmis, U.; Mohaghegh, S.; Ameri, S.

doi:10.2523/39231-ms

Cited by 16 publications

(6 citation statements)

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“…All these properties affect the Rate Of Penetration (ROP), that is the speed at which a drill bit breaks the rock in order to deepen the petroleum borehole [6]. There are other parameters that also affect the ROP and that can be controlled by a drilling operator [5]: weight on bit (WOB), revolutions per minute (RPM), bit type, bit diameter and drilling fluid pressure.…”

Section: Application In Offshore Oil Drillingmentioning

confidence: 99%

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Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Rodrigues

Moura

Jacinto

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given offshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understanding the operation of the system by a human expert.

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Section: Application In Offshore Oil Drillingmentioning

confidence: 99%

“…To optimize this work many systems using artificial neural networks (ANN) were proposed in the past [5] and even choose automatically some parameters such as RPM and WOB [11].…”

Section: Application In Offshore Oil Drillingmentioning

confidence: 99%

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Rodrigues

Moura

Jacinto

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Many experts introduced their studies on using the ANNs to predict and estimate the rate of penetration with different oil fields and cases. Bilgesu et al [6] introduced a new approach and methodology for the prediction of ROP values at a drill site by using the drilling recorded data and the neural networks. They concluded that if the drilling rate falls below the expected values, a new bit can be selected based on the network predictions.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation â€“ Ahdeb Oil Field

Zirej

Hadi

2020

eijs

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The main objective of this study is to develop a rate of penetration (ROP) model for Khasib formation in Ahdab oil field and determine the drilling parameters controlling the prediction of ROP values by using artificial neural network (ANN). An Interactive Petrophysical software was used to convert the raw dataset of transit time (LAS Readings) from parts of meter-to-meter reading with depth. The IBM SPSS statistics software version 22 was used to create an interconnection between the drilling variables and the rate of penetration, detection of outliers of input parameters, and regression modeling. While a JMP Version 11 software from SAS Institute Inc. was used for artificial neural modeling. The proposed artificial neural network method depends on obtaining the input data from drilling mud logging data and wireline logging data. The data then analyzes it to create an interconnection between the drilling variables and the rate of penetration. The proposed ANN model consists of an input layer, hidden layer and outputs layer, while it applies the tangent function (TanH) as a learning and training algorithm in the hidden layer. Finally, the predicted values of ROP are compared with the measured values. The proposed ANN model is more efficient than the multiple regression analysis in predicting ROP. The obtained coefficient of determination (R2) values using the ANN technique are 0.93 and 0.91 for training and validation sets, respectively. This study presents a new model for predicting ROP values in comparison with other conventional drilling measurements.

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“…Different previous studies have suggested the use of artificial intelligence (AI) to improve the predictability of different parameters related to the oil industry [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Bilgesu et al [ 28 ] suggested the use of AI techniques for ROP prediction. They developed two artificial neural networks (ANN) models for predicting ROP, while drilling through various nine formations in different vertical wells.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

Elkatatny

2020

Sensors

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Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.

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A New Approach for the Prediction of Rate of Penetration (ROP) Values

Cited by 16 publications

References 0 publications

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

An Artificial Neural Network for Predicting Rate of Penetration in AL- Khasib Formation â€“ Ahdeb Oil Field

Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

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