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

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.

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

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
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.

scholarly journals A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Ali M. Al-Salihi ◽  
Zahraa A. AL-Ramahy
Keyword(s):  
Neural Network ◽  
Soil Temperature ◽  
Hydrological Cycle ◽  
Absolute Error ◽  
Training Data ◽  
Percentage Error ◽  
Ann Model ◽  
Temperature Estimation ◽  
Data Set ◽  
Daily Data

Soil temperature is an important meteorological variable which plays a significant role in hydrological cycle. In present study, artificial intelligence technique employed for estimating for 3 daysa head soil temperature estimation at 10 and 20 cm depth. Soil temperature daily data for the period 1 January 2012 to 31 December 2013 measured in three stations namely (Mosul, Baghdad and Muthanna) in Iraq. The training data set includes 616 days and the testing data includes 109 days. The Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian regularization algorithms. To evaluate the ANN models, Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Correlation Coefficient (r) were determined. According to the four statistical indices were calculated of the optimum ANN model, it was ANN model (3) in Muthanaa station for the depth 10 cm and ANN model (3) in Baghdad station for the depth 20 were (RMSE=0.959oC, MAE=0.725, MAPE=4.293, R=0.988) and (RMSE=0.887OC, MAE=0.704, MAPE=4.239, R=0.993) respectively, theses statistical criteria shown the efficiency of artificial neural network for soil temperature estimation.

scholarly journals Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)

2014 ◽  
Vol 7 (4) ◽  
pp. 132-143
Author(s):  
ABBAS M. ABD ◽  
SAAD SH. SAMMEN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Water Reservoir ◽  
Prediction Method ◽  
Training Data ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

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

2020 ◽  
Vol 12 (4) ◽  
pp. 1376 ◽  
Author(s):  
Ahmad Al-AbdulJabbar ◽  
Salaheldin Elkatatny ◽  
Ahmed Abdulhamid Mahmoud ◽  
Tamer Moussa ◽  
Dhafer Al-Shehri ◽  
...  
Keyword(s):  
Empirical Correlation ◽  
The Self ◽  
Carbonate Reservoirs ◽  
Ann Model ◽  
Horizontal Drilling ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Artificial Neural ◽  
Self Adaptive

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.

scholarly journals Prediction of the Bicarbonate Amount in Drinking Water in the Region of Médéa Using Artificial Neural Network Modelling

2020 ◽  
Vol 69 (11-12) ◽  
pp. 595-602
Author(s):  
Hichem Tahraoui ◽  
Abd Elmouneïm Belhadj ◽  
Adhya Eddine Hamitouche
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Drinking Water ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Mean Square ◽  
Ann Model ◽  
Network Modelling ◽  
Data Set ◽  
Artificial Neural

The region of Médéa (Algeria) located in an agricultural site requires a large amount of drinking water. For this purpose, the water analyses in question are imperative. To examine the evolution of the drinking water quality in this region, firstly, an experimental protocol was done in order to obtain a dataset by taking into account several physicochemical parameters. Secondly, the obtained data set was divided into two parts to form the artificial neural network, where 70 % of the data set was used for training, and the remaining 30 % was also divided into two equal parts: one for testing and the other for validation of the model. The intelligent model obtained was evaluated as a function of the correlation coefficient nearest to 1 and lowest mean square error (RMSE). A set of 84 data points were used in this study. Eighteen parameters in the input layer, five neurons in the hidden layer, and one parameter in the output layer were used for the ANN modelling. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid, and linear transfer function were used, respectively, for the hidden and the output layers. The results obtained during the present study showed a correlation coefficient of <i>R</i> = 0.99276 with root mean square error RMSE = 11.52613 mg dm<sup>–3</sup>. These results show that obtained ANN model gave far better and more significant results. It is obviously more accurate since its relative error is small with a correlation coefficient close to unity. Finally, it can be concluded that obtained model can effectively predict the rate of soluble bicarbonate in drinking water in the Médéa region.

scholarly journals Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

2019 ◽  
Vol 70 (3) ◽  
pp. 247-255
Author(s):  
Ayşenur Gürgen ◽  
Derya Ustaömer ◽  
Sibel Yildiz
Keyword(s):  
Water Absorption ◽  
Medium Density Fiberboard ◽  
Correlation Coefficients ◽  
Percentage Error ◽  
Ann Model ◽  
Thickness Swelling ◽  
Empirical Formulas ◽  
Data Set ◽  
Press Temperature ◽  
Artificial Neural

In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

Quality Prediction of Injection-Molded Parts Based on PLS-ANN

2013 ◽  
Vol 330 ◽  
pp. 269-273 ◽  
Author(s):  
Peng Fei Zhu ◽  
Xiao Fang Sun ◽  
Ying Jun Lu ◽  
Hai Tian Pan
Keyword(s):  
Percentage Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Melt Temperature ◽  
Data Set ◽  
Independent Variables ◽  
Injection Process ◽  
Molded Parts ◽  
Injection Molded ◽  
Artificial Neural Network Ann

A feed-forward three-layer artificial neural network (ANN) combined with Partial Least-Squares (PLS) was presented to predict the part weight of injection-molded products. Firstly, melt temperature, holding pressure and holding time which are the most important influenced factors of injection-molded parts quality were chosen as independent variables and part weight were chosen as dependent variable. Secondly, PLS was used to analysis the relationship among these variables and calculate the aggregate elements of independent variables and dependent variable. Here, dependent variable was single, so parts weight is the aggregate element of dependent variable. Thirdly, the principal elements of independent variables and dependent variable were used to construct an ANN. At last, the performance of PLS-ANN model was evaluated and tested by its application to verification tests. Results showed that the PLS-ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.06% and the maximum relative error (MRE) in the range of 0.15% for the test data set, which can accurately reflect the influence of the injection process parameters on parts quality index under the circumstance of data deficiencies.

Efficient Drilling Technology of Horizontal Wells in the Duverney Block in Canada

2021 ◽  
Author(s):  
Haochen Han ◽  
Guobin Yang ◽  
Guobin Zhang ◽  
Jia Chen ◽  
Peter Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Economic Effect ◽  
Horizontal Wells ◽  
Field Application ◽  
Parameters Optimization ◽  
Horizontal Section ◽  
Horizontal Drilling ◽  
Technical Problems ◽  
Drilling Parameters ◽  
Drilling Engineering

Abstract Recent years, both exploration and development have made considerable progress in the Duverney block shale gas in Canada. However, technical problems exposed in horizontal drilling engineering need to be optimized: 1) Loss is common in shallow formations; 2) High downhole friction torque, low ROP and drilling cuttings accumulation in long horizontal well section; 3) Borehole instability leads to hanger or packer failure; 4) Drill bits and PDM have short servicing life and low efficiency. Optimization comes from three aspects: (1) Based on previous drilling experience and latest formation condition and development requirement, we design a new well profile for the block taking into drilling safety and further development account;(2) Optimize strong inhibitive, easy-maintenance and high cutting-carrying capacity OBM to ensure the safety requirements in the ultra-long open hole section; (3) BHA and parameters optimization. Optimize drill bit with high-abrasiveness and axial efficiency according to logging data, drillability and UCS. Upgrade conventional PDM into high-performance PDM with even-wall thickness. By means of simulation and calculation, drilling parameters suitable for Duverney block has been optimized. Based on the optimization above, a stable and efficient well profile has been improved solving hanger failure on-site efficiently and complete with composite casing design (4-1/2 inch plus 5 inch) in reservoir section; a 90/10 oil-water ratio OBM has been optimized and applied onsite; Combined with high performance PDC bit and even-wall PDM as well as optimized drilling parameters, higher ROP and longer horizontal section have been achieved. The optimization has made successful field application results:(1) Completion depth has been deepened gradually, from 18,080 ft in 2013 to 23,208 ft at present, with an increase of 28.3%;(2) Horizontal section length has been increasing dramatically, from 6,190 ft in 2013 to 10,301 ft at present, with an increase of 66.4%;(3) The average drilling cycle has been shortened, from 51 days in 2013 to 26 days at present, with a decrease of 48.8%;(4) The average ROP has increased steadily, from 71.2ft/h in 2013 to 82.12ft/h at present, with an increase of 15.3%; (5) Drilling costs per meter have been significantly reduced, from 442.4 CAD/ft in 2013 to 228.3 CAD/ft at present. Combining the optimization and matching design above, it has effectively solved the difficulties of the Duverney block drilling engineering and achieved good field application effects: well depth and horizontal section length in the block have been deepened year by year, the drilling cycle and cost have been decreased year by year, as well as the economic effect has been significant. In all, the research achievements provide a practical and effective reference for horizontal wells in other region especially for the unconventional gas.

Development of Machine Learning Based Propped Fracture Conductivity Correlations in Shale Formations

2021 ◽  
Author(s):  
Mahmoud Desouky ◽  
Zeeshan Tariq ◽  
Murtada Al jawad ◽  
Hamed Alhoori ◽  
Mohamed Mahmoud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Data Science ◽  
Mean Squared Error ◽  
Empirical Models ◽  
Ann Model ◽  
Data Set ◽  
Fracture Conductivity ◽  
Data Points ◽  
Conductivity Models

Abstract Propped hydraulic fracturing is a stimulation technique used in tight formations to create conductive fractures. To predict the fractured well productivity, the conductivity of those propped fractures should be estimated. It is common to measure the conductivity of propped fractures in the laboratory under controlled conditions. Nonetheless, it is costly and time-consuming which encouraged developing many empirical and analytical propped fracture conductivity models. Previous empirical models, however, were based on limited datasets producing questionable correlations. We propose herein new empirical models based on an extensive data set utilizing machine learning (ML) methods. In this study, an artificial neural network (ANN) was utilized. A dataset comprised of 351 data points of propped hydraulic fracture experiments on different shale types with different mineralogy under various confining stresses was collected and studied. Several statistical and data science approaches such as box and whisker plots, correlation crossplots, and Z-score techniques were used to remove the outliers and extreme data points. The performance of the developed model was evaluated using powerful metrics such as correlation coefficient and root mean squared error. After several executions and function evaluations, an ANN was found to be the best technique to predict propped fracture conductivity for different mineralogy. The proposed ANN models resulted in less than 7% error between actual and predicted values. In this study, in addition to the development of an optimized ANN model, explicit empirical correlations are also extracted from the weights and biases of the fine-tuned model. The proposed model of propped fracture conductivity was then compared with the commonly available correlations. The results revealed that the proposed mineralogy based propped fracture conductivity models made the predictions with a high correlation coefficient of 94%. This work clearly shows the potential of computer-based ML techniques in the determination of mineralogy based propped fracture conductivity. The proposed empirical correlation can be implemented without requiring any ML-based software.

scholarly journals Intelligent Prediction of The Equivalent Circulating Density From Surface Data Sensors During Drilling By Employing Machine Learning Techniques

2021 ◽  
Author(s):  
Hany Gamal ◽  
Ahmed Abdelaal ◽  
Salaheldin Elkatatny
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Sensitivity Analyses ◽  
Percentage Error ◽  
Model Parameters ◽  
Data Set ◽  
Inference System ◽  
Mathematical Correlation ◽  
Drilling Parameters

Abstract The precise control for the equivalent circulating density (ECD) will lead to evade well control issues like loss of circulation, formation fracturing, underground blowout, and surface blowout. Predicting the ECD from the drilling parameters is a new horizon in drilling engineering practices and this is because of the drawbacks of the cost of downhole ECD tools and the low accuracy of the mathematical models. Machine learning methods can offer a superior prediction accuracy over the traditional and statistical models due to the advanced computing capacity. Hence, the objective of this paper is to use the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques to develop ECD prediction models. The novel contribution for this study is predicting the downhole ECD without any need for downhole measurements but only the available surface drilling parameters. The data in this study covered the drilling data for a horizontal section with 3,570 readings for each input after data preprocessing. The data covered the mud rate, rate of penetration, drill string speed, standpipe pressure, weight on bit, and the drilling torque. The data used to build the model with a 77:23 training to testing ratio. Another data set (1,150 data points) from the same field was used for models` validation. Many sensitivity analyses were done to optimize the ANN and ANFIS model parameters. The prediction of the developed machine learning models provided a high performance and accuracy level with a correlation coefficient (R) of 0.99 for the models' training and testing data sets, and an average absolute percentage error (AAPE) less than 0.24%. The validation results showed R of 0.98 and 0.96 and AAPE of 0.30% and 0.69% for ANN and ANFIS models respectively. Besides, a mathematical correlation was developed for estimating ECD based on the inputs as a white-box model.

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