Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.

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Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

10.29007/4sdt ◽

2022 ◽

Author(s):

Vu Khanh Phat Ong ◽

Quang Khanh Do ◽

Thang Nguyen ◽

Hoang Long Vo ◽

Ngoc Anh Thy Nguyen ◽

...

Keyword(s):

Network Model ◽

Oil And Gas ◽

Research Approach ◽

Ann Model ◽

Rate Of Penetration ◽

Drilling Parameters ◽

Drilling Operations ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

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Use of Artificial Neural Networks in predicting rate of penetration and optimization weight on bit for several wells in Nam Rong - Doi Moi field, Vietnam

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2021.62(3a).05 ◽

2021 ◽

Vol 62 (3a) ◽

pp. 37-47

Author(s):

Hung Tien Nguyen ◽

Duong Hong Vu ◽

Vinh The Nguyen ◽

Doan Thi Tram ◽

Pham Van Trung ◽

...

Keyword(s):

Research Area ◽

High Accuracy ◽

Ann Model ◽

Rate Of Penetration ◽

Doi Moi ◽

Drilling Parameters ◽

Weight On Bit ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Obtaining the maximum Rate of Penetration (ROP) by optimization of drilling parameters is the aim of every drilling engineer. This helps to save time, reduces cost and minimizes drilling problems. Since ROP depends on a lot of parameters, it is very difficult to predict it correctly. Therefore, it is necessary and important to investigate a solution for predicting ROP with high accuracy in order to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real - time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as weight on bit (WOB), weight of mud (MW), rotary speed (RPM), stand pipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when comparing to actual ROP, therefore it can be recommended as an effective and suitable method to predict ROP of other wells in research area. Besides, base on the proposed ANN model, authors carried out experiments and determine the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in in Nam Rong Doi Moi field.

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Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽

10.3390/pr9061070 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1070

Author(s):

Abdul Gani Abdul Jameel

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Functional Groups ◽

Average Error ◽

Ann Model ◽

Oh Groups ◽

Unseen Data ◽

Learning Capabilities ◽

Artificial Neural ◽

Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

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A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽

10.3390/s20072058 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2058 ◽

Cited By ~ 2

Author(s):

Salaheldin Elkatatny ◽

Ahmed Al-AbdulJabbar ◽

Khaled Abdelgawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mutual Effect ◽

Percentage Error ◽

Drilling Process ◽

Ann Model ◽

Data Set ◽

Individual Parameter ◽

Rate Of Penetration ◽

Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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Prediction of Centrifugal Slurry Pump Head Reduction: An Artificial Neural Networks Approach

Journal of Fluids Engineering ◽

10.1115/1.1523062 ◽

2003 ◽

Vol 125 (1) ◽

pp. 199-202 ◽

Cited By ~ 7

Author(s):

Tahsin Engin ◽

Akif Kurt

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Empirical Correlation ◽

Mean Deviation ◽

Ann Model ◽

Centrifugal Pumps ◽

Pump Head ◽

The Mean ◽

Artificial Neural ◽

Experimental Values

The feasibility of using artificial neural networks (ANN) in the prediction of head reduction of centrifugal pumps handling slurries is examined. An ANN model is proposed and compared with the empirical correlation given by the present authors earlier. The comparison showed that the ANN could successfully be used for the prediction of head reductions of centrifugal slurry pumps. The mean deviation between predicted and experimental values is 5.86% which is reasonable for slurry handling processes.

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Artificial neural networks modelling based on visual analysis of coated cross laminated timber (CLT) to predict color change during outdoor exposure

Holzforschung ◽

10.1515/hf-2020-0193 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Gabrielly S. Bobadilha ◽

C. Elizabeth Stokes ◽

Dercilio Junior Verly Lopes

Keyword(s):

Visual Analysis ◽

Mean Squared Error ◽

Color Change ◽

Visual Assessment ◽

Coefficient Of Determination ◽

Ann Model ◽

Appearance Time ◽

Cross Laminated Timber ◽

Unseen Data ◽

Artificial Neural

AbstractIn this study, an artificial neural network (ANN) model was designed to predict color change based on visual assessment of coated cross laminated timber (CLT) exposed outdoors. Coatings and stains were investigated based on ASTM protocols to assess wood surface visual rating, against checking, flaking, erosion, and mildew growth in the State of Mississippi (USA) during one year (2019–2020). It was hypothesized that accurate ratings would promote precise color prediction by the ANN model. Visual assessment inputs were used to develop the model for predicting total color change (ΔE). The training and validation splits of the network were based on a 10-fold cross-validation technique, and the ANN model performance was assessed on the validation set using mean squared error (MSE), mean average precision (MAE), and coefficient of determination (R2) after permutation feature importance analysis (PFI). Results indicated that coating was the most important feature in color change model. Erosion, checking and flaking achieved similar importance with an approximate difference of 6%. The ANN model was able to effectively predict color change values based on visual ratings with overall accuracy of 95% on truly unseen data. These findings revealed that coating properties, visual appearance, time of exposure, are associated with discoloration. Accurate visual assessment and a well-trained ANN can successfully provide the desired values of ΔE with a smaller number of complex test procedures.

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A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

Journal of Sensors ◽

10.1155/2022/9444076 ◽

2022 ◽

Vol 2022 ◽

pp. 1-14

Author(s):

Ahmad Al-AbdulJabbar ◽

Ahmed Abdulhamid Mahmoud ◽

Salaheldin Elkatatny ◽

Mahmoud Abughaban

Keyword(s):

Oil Field ◽

Training Data ◽

Mechanical Parameters ◽

Drilling Rate ◽

Ann Model ◽

Rate Of Penetration ◽

Artificial Neural ◽

Sandstone Formation ◽

Gas Bearing

This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.

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

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.5.14 ◽

2020 ◽

pp. 1051-1062

Author(s):

Zaher JabbarAttwan AL Zirej ◽

Hassan Abdul Hadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Oil Field ◽

Coefficient Of Determination ◽

Drilling Mud ◽

Ann Model ◽

Rate Of Penetration ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

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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Intelligent Model for Predicting Downhole Vibrations Using Surface Drilling Data During Horizontal Drilling

Journal of Energy Resources Technology ◽

10.1115/1.4052794 ◽

2021 ◽

pp. 1-19

Author(s):

Ramy Saadeldin ◽

Hany Gamal ◽

Salaheldin Elkatatny ◽

Abdulazeez Abdulraheem

Keyword(s):

Correlation Coefficient ◽

Mechanical Energy ◽

Time Estimation ◽

Percentage Error ◽

Ann Model ◽

Horizontal Section ◽

Data Set ◽

Horizontal Drilling ◽

Vibration Prediction ◽

Drilling Parameters

Abstract Drillstring vibration is a major concern during drilling wellbore and it can be split into three types axial, torsional, and lateral. Many problems associate with the high drillstring vibrations as tear and wear in downhole tools, inefficient drilling performance, loss of mechanical energy, and hole wash-out. The high cost for the downhole measurement of the drillstring vibrations encourages machine learning applications toward downhole vibration prediction during drilling. Consequently, the objective of this paper is to develop an artificial neural network (ANN) model for predicting the drillstring vibration while drilling a horizontal section. The ANN model uses the surface drilling parameters as model inputs to predict the three types of drillstring vibrations. These surface drilling parameters are flow rate, mud pumping pressure, surface rotating speed, top drive torque, weight on bit, and rate of penetration. The study utilized a dataset of 13,927 measurements from a horizontal well that was used to train the ANN model. In addition, a different data set (9,284 measurements) was employed to validate the developed ANN model. Correlation coefficient (R) and average absolute percentage error (AAPE) are statistical metrics that are used to evaluate the model accuracy based on the difference between the actual and predicted values for the axial, torsional, and lateral vibrations. The results of the optimized parameters for the developed model showed a high correlation coefficient between the predicted and the actual drillstring vibrations that showed R higher than 0.95 and AAPE below 3.5% for all phases of model training, testing, and validation. The developed model proposed a model-based equation for real-time estimation for the downhole vibrations.

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GENETIC ALGORITHM-ASSISTED ARTIFICIAL NEURAL NETWORK FOR THE ESTIMATION OF DRILLING PARAMETERS OF MAGNESIUM AZ91 IN VERTICAL MILLING MACHINE

Surface Review and Letters ◽

10.1142/s0218625x19502214 ◽

2020 ◽

Vol 27 (10) ◽

pp. 1950221

Author(s):

M. VARATHARAJULU ◽

G. JAYAPRAKASH ◽

N. BASKAR ◽

A. SARAVANAN

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

High Speed Steel ◽

Milling Machine ◽

Ann Model ◽

Drilling Parameters ◽

Vertical Milling ◽

Vertical Milling Machine ◽

Artificial Neural

The selection of appropriate drilling parameters is essential for improving productivity and part quality, therefore, this work mainly concentrates on the investigation of drilling time, burr height, burr thickness, roundness and surface roughness. The drilling experiments were carried out on Magnesium (Mg) AZ91 with High Speed Steel (HSS) tool using the Vertical Milling Machine (VMM). The parameters reckoned are spindle speed and feed rate. Artificial Neural Network (ANN) was concerned with the building of the model that will be used to forecast the responses following the consideration of Response Surface Methodology (RSM). Conventional method of modeling (RSM) yields poorer results which redirected the study with ANN. The Genetic Algorithm (GA)-based ANN has been reckoned for developing the model. With two nodes in the parameter layer and seven nodes in the response layer, six different networks were constructed using variety of nodes in the hidden layers which are 2–6–7, 2–7–7, 2–8–7, 2–6–6–7, 2–7–6–7 and 2–8–6–7. It is observed that the 2–8–7 network offers the best ANN model in predicting the various responses. The prediction results ensure the reliability of the ANN model to analyze the effect of drilling parameters over the various responses.

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