Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Poisson’S Ratio ◽  
Carbonate Reservoirs ◽  
Poisson's Ratio ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Cement Slurry ◽  
Inference System ◽  
Log Data

Static Poisson's ratio (νstatic) is a key factor in determine the in-situ stresses in the reservoir section. νstatic is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. νstatic is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. νstatic can be measured in the lab using a real reservoir cores. The laboratory measurements of νstatic will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting νstatic for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the νstatic. The developed artificial neural network (ANN) model yielded more accurate results for estimating νstatic based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for νstatic gave a coefficient of determination (R2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of νstatic without the need for coring and laboratory work and hence will reduce the overall cost of the well.

scholarly journals Estimation of Oil Recovery Factor for Water Drive Sandy Reservoirs through Applications of Artificial Intelligence

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3671 ◽  
Author(s):  
Ahmed Mahmoud ◽  
Salaheldin Elkatatny ◽  
Weiqing Chen ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Oil Recovery ◽  
Water Saturation ◽  
Data Availability ◽  
Recovery Factor ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Subtractive Clustering ◽  
Oil Viscosity ◽  
Inference System

Hydrocarbon reserve evaluation is the major concern for all oil and gas operating companies. Nowadays, the estimation of oil recovery factor (RF) could be achieved through several techniques. The accuracy of these techniques depends on data availability, which is strongly dependent on the reservoir age. In this study, 10 parameters accessible in the early reservoir life are considered for RF estimation using four artificial intelligence (AI) techniques. These parameters are the net pay (effective reservoir thickness), stock-tank oil initially in place, original reservoir pressure, asset area (reservoir area), porosity, Lorenz coefficient, effective permeability, API gravity, oil viscosity, and initial water saturation. The AI techniques used are the artificial neural networks (ANNs), radial basis neuron networks, adaptive neuro-fuzzy inference system with subtractive clustering, and support vector machines. AI models were trained using data collected from 130 water drive sandstone reservoirs; then, an empirical correlation for RF estimation was developed based on the trained ANN model’s weights and biases. Data collected from another 38 reservoirs were used to test the predictability of the suggested AI models and the ANNs-based correlation; then, performance of the ANNs-based correlation was compared with three of the currently available empirical equations for RF estimation. The developed ANNs-based equation outperformed the available equations in terms of all the measures of error evaluation considered in this study, and also has the highest coefficient of determination of 0.94 compared to only 0.55 obtained from Gulstad correlation, which is one of the most accurate correlations currently available.

Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction

2021 ◽  
Author(s):  
Ololade Adetifa ◽  
Ibiye Iyalla ◽  
Kingsley Amadi
Keyword(s):  
Artificial Intelligence ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Artificial Intelligent ◽  
Rate Of Penetration ◽  
Drilling Data ◽  
Intelligent Models ◽  
Prediction Capability

Abstract Rate of penetration is an important parameter in drilling performance analysis. The accurate prediction of rate of penetration during well planning leads to a reduction in capital and operating costs which is vital given the recent downturn in oil prices. The industry is seen to embrace the use of novel technologies and artificial intelligence in its bid to be sustainable which is why this study focuses on the use of artificial intelligent models in predicting the rate of penetration. The predictive performance of three data-driven models [artificial neural network (ANN), extreme learning machine (ELM) and least-square support vector machine (LS-SVM)] were evaluated using actual drilling data based on three performance evaluation criteria [mean square error (MSE), coefficient of determination (R2) and average absolute percentage error (AAPE)]. The models were validated using the physics based Bourgoyne and Young's model. The results show that all three models performed to an acceptable level of accuracy based on the range of the actual drilling data because, although the ELM had the least MSE (1317.44) and the highest R2 (0.52 i.e. 52% prediction capability) the LS-SVM model had a smaller spread of predicted ROP when compared with the actual ROP and the ANN had the least AAPE (38.14). The results can be improved upon by optimizing the controllable predictors. Validation of the model's performance with the Bourgoyne and Young's model resulted in R2 of 0.29 or 29% prediction capability confirming that artificial intelligent models outperformed the physics-based model.

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 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 Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Gurmanik Kaur ◽  
Ajat Shatru Arora ◽  
Vijender Kumar Jain
Keyword(s):  
Prediction Models ◽  
Fuzzy Inference ◽  
Research Work ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Inference System ◽  
Forward Stepwise ◽  
Artificial Neural Network Ann

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

Hybrid artificial intelligence-based inference models for accurately predicting dam body displacements: A case study of the Fei Tsui dam

2021 ◽  
pp. 147592172110441
Author(s):  
Min-Yuan Cheng ◽  
Minh-Tu Cao ◽  
I-Feng Huang
Keyword(s):  
Artificial Intelligence ◽  
Real Data ◽  
Influential Factors ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Prediction Errors ◽  
Operation Condition ◽  
Inference Models

Surveillance is a critical activity in monitoring the operation condition and safety of dams. This study reviewed the historical monitoring data of the Fei Tsui dam to determine possible influential factors for the dam body displacement and then evaluated the influencing degree of these factors by using correlation analysis. Thus, the key influential factors were identified objectively and further chosen as the input variables for numerous artificial intelligence (AI)-based inference models, including single machine learning techniques (support vector machine (SVM), artificial neural networks) and hybrid AI models. The models were trained and tested with 4722 real data retrieved in 11 years from the monitoring devices installed on elements of the dam, and then generated their respective inferred dam body displacement values. The results revealed that the adaptive time-dependent evolutionary least squares SVM model had the greatest performance by providing the lowest values of prediction errors in terms of mean absolute percentage error (MAPE = 8.14%), root mean square error (RMSE = 1.08 cm), and coefficient of determination (R = 0.993). The analysis results endorsed that the hybrid AI model could be an efficient tool to early produce accurate warnings of the dam displacements.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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