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

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3506 ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Support Vector ◽  
Empirical Correlations ◽  
Validation Data ◽  
Inference System ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Svm Model ◽  
Data Points

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%.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

2021 ◽  
pp. 1-26
Author(s):  
Ahmed Mahmoud ◽  
Hany Gamal ◽  
Salaheldin Elkatatny ◽  
Ahmed Alsaihati
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Drilling Process ◽  
Validation Data ◽  
Inference System ◽  
Data Points

Abstract Total organic carbon (TOC) is an essential parameter that indicates the quality of unconventional reservoirs. In this study, four machine learning (ML) algorithms of the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), functional neural networks (FNN), and random forests (RF) were optimized to evaluate the TOC. The novelty of this work is that the optimized models predict the TOC from the bulk gamma-ray (GR) and spectral GR logs of uranium, thorium, and potassium only. The ML algorithms were trained on 749 datasets from Well-1, tested on 226 datasets from Well-2, and validated on 73 data points from Well-3. The predictability of the optimized algorithms was also compared with the available equations. The results of this study indicated that the optimized ANFIS, SVR, and RF models overperformed the available empirical equations in predicting the TOC. For validation data of Well-3, the optimized ANFIS, SVR, and RF algorithms predicted the TOC with AAPE's of 10.6%, 12.0%, and 8.9%, respectively, compared with the AAPE of 21.1% when the FNN model was used. While for the same data, the TOC was assessed with AAPE's of 48.6%, 24.6%, 20.2%, and 17.8% when Schmoker model, ΔlogR method, Zhao et al. correlation, and Mahmoud et al. correlation was used, respectively. The optimized models could be applied to estimate the TOC during the drilling process if the drillstring is provided with GR and spectral GR logging tools.

Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques

2021 ◽  
Author(s):  
Vagif Suleymanov ◽  
Hany Gamal ◽  
Guenther Glatz ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Shear Wave ◽  
Correlation Coefficients ◽  
Acoustic Properties ◽  
Support Vector ◽  
Inference System ◽  
Svm Model ◽  
Drilling Data ◽  
Sonic Logs

Abstract Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper delineates an alternative solution for the prediction of sonic log data by means of Machine Learning (ML). This study takes advantage of an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) ML techniques to predict compressional and shear wave slowness from drilling data only. In particular, the network is trained utilizing 2000 data points such as weight on bit (WOB), rate of penetration (ROP), standpipe pressure (SPP), torque (T), drill pipe rotation (RPM), and mud flow rate (GPM). Consequently, acoustic properties of the rock can be estimated solely from readily available parameters thereby saving both costs and time associated with sonic logs. The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs. The developed ANFIS model was able to predict compressional and shear wave slowness with correlation coefficients of 0.94 and 0.98 and average absolute percentage errors (AAPE) of 1.87% and 2.61%, respectively. Similarly, the SVM model predicted sonic logs with high accuracy yielding to correlation coefficients of more than 0.98 and AAPE of 0.74% and 0.84% for both compressional and shear logs, respectively. Once a network is trained, the approach naturally lends itself to be integrated as a real time service. This study outlines a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters. Importantly, the model has been verified for wells drilled in different formations with complex lithology substantiating the effectiveness of the approach.

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Optimal Model ◽  
Time Data ◽  
Fracture Pressure ◽  
Drilling Parameters ◽  
Drilling Operations

Abstract Several correlations are available to determine the fracture pressure, a vital property of a well, which is essential in the design of the drilling operations and preventing problems. Some of these correlations are based on the rock and formation characteristics, and others are based on log data. In this study, five artificial intelligence (AI) techniques predicting fracture pressure were developed and compared with the existing empirical correlations to select the optimal model. Real-time data of surface drilling parameters from one well were obtained using real-time drilling sensors. The five employed methods of AI are functional networks (FN), artificial neural networks (ANN), support vector machine (SVM), radial basis function (RBF), and fuzzy logic (FL). More than 3990 datasets were used to build the five AI models by dividing the data into training and testing sets. A comparison between the results of the five AI techniques and the empirical fracture correlations, such as the Eaton model, Matthews and Kelly model, and Pennebaker model, was also performed. The results reveal that AI techniques outperform the three fracture pressure correlations based on their high accuracy, represented by the low average absolute percentage error (AAPE) and a high coefficient of determination (R2). Compared with empirical models, the AI techniques have the advantage of requiring less data, only surface drilling parameters, which can be conveniently obtained from any well. Additionally, a new fracture pressure correlation was developed based on ANN, which predicts the fracture pressure with high precision (R2 = 0.99 and AAPE = 0.094%).

scholarly journals Real-time prediction of Poisson’s ratio from drilling parameters using machine learning tools

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Osama Siddig ◽  
Hany Gamal ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Poisson’S Ratio ◽  
Wellbore Stability ◽  
Additional Cost ◽  
Poisson's Ratio ◽  
Inference System ◽  
Time Prediction ◽  
Drilling Parameters ◽  
Data Points

AbstractRock elastic properties such as Poisson’s ratio influence wellbore stability, in-situ stresses estimation, drilling performance, and hydraulic fracturing design. Conventionally, Poisson’s ratio estimation requires either laboratory experiments or derived from sonic logs, the main concerns of these methods are the data and samples availability, costs, and time-consumption. In this paper, an alternative real-time technique utilizing drilling parameters and machine learning was presented. The main added value of this approach is that the drilling parameters are more likely to be available and could be collected in real-time during drilling operation without additional cost. These parameters include weight on bit, penetration rate, pump rate, standpipe pressure, and torque. Two machine learning algorithms were used, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To train and test the models, 2905 data points from one well were used, while 2912 data points from a different well were used for model validation. The lithology of both wells contains carbonate, sandstone, and shale. Optimization on different tuning parameters in the algorithm was conducted to ensure the best prediction was achieved. A good match between the actual and predicted Poisson’s ratio was achieved in both methods with correlation coefficients between 0.98 and 0.99 using ANN and between 0.97 and 0.98 using ANFIS. The average absolute percentage error values were between 1 and 2% in ANN predictions and around 2% when ANFIS was used. Based on these results, the employment of drilling data and machine learning is a strong tool for real-time prediction of geomechanical properties without additional cost.

scholarly journals Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water

2019 ◽  
Vol 9 (4) ◽  
pp. 372-384
Author(s):  
Maryam Sadi ◽  
Hajar Fakharian ◽  
Hamid Ganji ◽  
Majid Kakavand
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Treatment Process ◽  
Produced Water ◽  
Model Development ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Inference System ◽  
Average Absolute Relative Error ◽  
Svm Model

Abstract In this study, two artificial intelligence models based on an adaptive neuro-fuzzy inference system (ANFIS) and a support vector machine (SVM) technique have been successfully developed to predict the desalination efficiency of produced water through a hydrate-based desalination treatment process. A genetic algorithm as an evolutionary optimization method has been used to determine the optimal values of SVM model coefficients. To this end, compressed natural gas and CO2 hydrate formation experiments were carried out, and the desalination efficiency of produced water was measured and utilized for model training and validation. After model development, graphical and statistical analysis approaches have been applied to evaluate the performance of suggested models by a comparison of model predictions with measured experimental data. For the ANFIS model, the coefficient of determination (R2) and average absolute relative error (AARE) are 0.9927 and 0.58%, respectively. The values of AARE and R2 for the SVM model are obtained 0.35% and 0.9985, respectively. These statistical criteria confirm excellent accuracy and robustness of intelligent models in predicting the desalination efficiency of produced water through the hydrate-based desalination treatment process. Furthermore, the Leverage statistical technique has been carried out to define the outliers. The obtained results demonstrate that all experimental data are reliable and both ANFIS and SVM models are statistically valid.

scholarly journals Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3773
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Tool Wear ◽  
Alloy Steel ◽  
Bayesian Optimization ◽  
Research Trend ◽  
Support Vector ◽  
Machining Parameters ◽  
Svm Model ◽  
Data Points

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

Applying Different Artificial Intelligence Techniques in Dynamic Poisson's Ratio Prediction Using Drilling Parameters

2021 ◽  
pp. 1-15
Author(s):  
Osama Sidddig ◽  
Hany Gamal ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Poisson’S Ratio ◽  
Correlation Coefficients ◽  
Wellbore Stability ◽  
Performance Estimation ◽  
Poisson's Ratio ◽  
Support Vector ◽  
Drilling Parameters ◽  
Data Points

Abstract Rock geomechanical properties impact wellbore stability, drilling performance, estimation of in-situ stresses, and design of hydraulic fracturing. One of these properties is Poisson's ratio which is measured from lab testing or derived from well logs, the former is costly, time-consuming and doesn't provide continuous information, and the latter may not be always available. An alternative prediction technique from drilling parameters in real-time is proposed in this paper. The novel contribution of this approach is that the drilling data is always available and obtained from the first encounter with the well. These parameters are easily obtainable from drilling rig sensors such as rate of penetration, weight on bit and torque. Three machine-learning methods were utilized, support vector machine (SVM), functional network (FN) and random forest (RF). Dataset (2905 data points) from one well were used to build the models, while a dataset from another well with 2912 data points was used to validate the constructed models. Both wells have diverse lithology consists of carbonate, shale and sandstone. To ensure optimal accuracy, sensitivity and optimization tests on various parameters in each algorithm were performed.The three machine learning tools provided good estimations, however, SVM and RF yielded close results, with correlation coefficients of 0.99 and the average absolute percentage error (AAPE) values were mostly less than 1%. While in FN the outcomes were less efficient with correlation coefficients of 0.92 and AAPE around 3.8%. Accordingly, the presented approach provides an effective tool for Poisson's ratio prediction on a real-time basis at no additional expense. In addition, the same approach could be used in other rock mechanical properties.

Real-Time Solution for Down Hole Torque Estimation and Drilling Optimization in High Deviated Wells Using Artificial Intelligence

2021 ◽  
Author(s):  
Mahmoud Nader Elzenary
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Major Influence ◽  
Support Vector ◽  
Rate Of Penetration ◽  
Artificial Intelligence Technology ◽  
Weight On Bit ◽  
Artificial Neural Network Ann ◽  
Torque Values ◽  
Deviated Wells

ABSTRACT This project provides a new realistic solution for the accuracy of down hole torque measurements using the integration of the Artificial intelligence (AI) technology with the downhole challenges being faced while drilling deep and high deviated wells. The new estimates are based on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate. This downhole torque model will be validated and verified through a real drilling scenario from a field in north of Africa. The field data includes weight on bit, surface torque, stand-pipe pressure, and rate of penetration were collected from the mentioned well which had experienced a costly stuck pipe situation. However, with the provided model the same encountered scenario will be avoided, due to the optimization of the real time drilling variables and hence, saving the well and evade a costly non-productive time.

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