Real-Time Prediction of Acoustic Velocities While Drilling Vertical Complex Lithology Using AI Technique

2021 ◽  
Vol 62 (3) ◽  
pp. 265-281
Author(s):  
Ahmed Alsaihati ◽  
◽  
Salaheldin Elkatatny ◽  
Keyword(s):  
Shear Velocity ◽  
Rock Properties ◽  
Percentage Error ◽  
Burial Depth ◽  
Data Set ◽  
Sonic Log ◽  
Unseen Data ◽  
Data Points ◽  
Acoustic Velocities ◽  
Artificial Neural Network Ann

Mechanical rock properties are often determined using sonic log data—compressional velocity (VP) and shear velocity (VS). However, a sonic well log is not always acquired due to deteriorated hole condition (i.e., hole washout), sonic tool failures, especially in high-pressure, high-temperature (HPHT) wells, and relatively high cost. This paper introduces two data-driven models, namely artificial neural network (ANN) and random forest (RF), to estimate VP and VS across different formations that are characterized by deep burial depth and strong heterogeneity. Two types of actual field data were used to develop the models: (i) drilling surface parameters, which include flow rate, standpipe pressure, rotary speed, and surface torque, and (ii) acoustic velocities VP and VS, which were acquired by a conventional sonic log. Well-1 and Well-2 with data points of 6,846 were used to develop the models, while Well-3 with 1,016 data points was used to evaluate the capability of the developed models to generalize on an unseen data set with different statistical behavior. Furthermore, Well-3 was used to compare the accuracy of the developed models with the earliest published correlations in estimating the VS. The results showed that the RF outperformed the optimized ANN in estimating VP and VS in Well-3. The RF predicted the VP with a low average absolute percentage error (AAPE) of 0.9% and correlation of coefficient (R) of 0.87, while the AAPE and R were 6.7 % and 0.45 in the case of ANN. Similarly, the RF estimated the VS with an AAPE of 1.1% and R of 0.85, whereas the ANN predicted the VS with an AAPE of 9.5% and R of 0.40. Furthermore, the RF was the most accurate in determining VS in Well-3 compared to the earliest published correlations.

Rock Strength Prediction in Real-Time while Drilling Employing Random Forest and Functional Network Techniques

2021 ◽  
pp. 1-21
Author(s):  
Hany Gamal ◽  
Ahmed Alsaihati ◽  
Salaheldin Elkatatny ◽  
Saleh Haidary ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Rock Strength ◽  
Prediction Models ◽  
Functional Network ◽  
Percentage Error ◽  
Data Set ◽  
Unseen Data ◽  
Drilling Data ◽  
Data Points

Abstract The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2,333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen data set (1,300 data points) of Well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3 %, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4 and 7.9 % for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost and enhancing the well stability by generating UCS log from the rig drilling data.

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.

scholarly journals Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2787
Author(s):  
Ahmed Gowida ◽  
Salaheldin Elkatatny ◽  
Khaled Abdelgawad ◽  
Rahul Gajbhiye
Keyword(s):  
Rheological Properties ◽  
Drilling Fluid ◽  
Rheological Parameters ◽  
Percentage Error ◽  
Cleaning Efficiency ◽  
Periodic Monitoring ◽  
Data Points ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Marsh Funnel

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

Rapid Estimation of Rock Cuttability Using Fracture Toughness and Rock Strength

2009 ◽  
Vol 76-78 ◽  
pp. 591-596 ◽  
Author(s):  
Bulent Tiryaki ◽  
Ian D. Gipps ◽  
Xing S. Li
Keyword(s):  
Fracture Toughness ◽  
Regression Tree ◽  
Direct Determination ◽  
Empirical Models ◽  
Alternative Methods ◽  
Rock Properties ◽  
Data Set ◽  
Mode I Fracture Toughness ◽  
Artificial Neural Network Ann ◽  
Rock Cuttability

Rock cuttability is expressed by specific energy (SE) that is defined as the energy required for cutting unit volume of rock. Direct determination of SE requires a rock cutting rig and is expensive and time-consuming. Therefore, empirical models have been alternative methods for predicting SE from rock properties. Two different predictive models of SE have been developed in this study using regression tree and artificial neural network (ANN) methods. Both empirical models employed the uniaxial compressive strength (UCS) and Mode I fracture toughness (KIC), being derived from tensile strength (t), as predictors. Data from four different studies have been used to develop the models. Statistical analyses on the data set have shown that both UCS and KIC are closely related to SE in a nonlinear form. Numerical and graphical measures of the goodness of the fit and ANOVA test have shown that regression tree and ANN models have performed similarly.

Application of Various Machine Learning Techniques in Predicting Water Saturation in Tight Gas Sandstone Formation

2021 ◽  
pp. 1-14
Author(s):  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny ◽  
Yasmin Abdelraouf ◽  
Mustafa Al Ramadan
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Well Logs ◽  
Percentage Error ◽  
Support Vector ◽  
Tight Gas ◽  
Data Set ◽  
Unseen Data ◽  
Sandstone Formation ◽  
Tight Gas Sandstone

Abstract Water saturation (Sw) is a vital factor for the hydrocarbon in-place calculations. Sw is usually calculated using different equations; however, its values have been inconsistent with the experimental results due to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict Sw from the conventional well logs. Function networks (FN), support vector machine (SVM), and random forests (RF) were implemented to calculate the Sw using gamma-ray (GR) log, Neutron porosity (NPHI) log, and resistivity (Rt) log. A dataset of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to build and then validate the different ML models. The data set from Well-1 was applied for the ML models training and testing, then the unseen data from well-2 was used to validate the developed models. The results from FN, SVM and RF models showed their capability of accurately predicting the Sw from the conventional well logging data. The correlation coefficient (R) values between actual and estimated Sw from the FN model were found to be 0.85 and 0.83 compared to 0.98, and 0.95 from the RF model in the case of training and testing sets, respectively. SVM model shows an R-value of 0.95 and 0.85 in the different datasets. The average absolute percentage error (AAPE) was less than 8% in the three ML models. The ML models outperform the empirical correlations that have AAPE greater than 19%. This study provides ML applications to accurately forecast the water saturation using the readily available conventional well logs without additional core analysis or well site interventions.

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.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Measuring Congestion and Reliability Impacts of Safety Projects

2021 ◽  
pp. 036119812110067
Author(s):  
Simona Babiceanu ◽  
Sanhita Lahiri ◽  
Mena Lockwood
Keyword(s):  
Performance Measures ◽  
Positive Impact ◽  
Operating Conditions ◽  
Vehicle Miles Traveled ◽  
Data Set ◽  
Data Points ◽  
Practical Recommendations

This study uses a suite of performance measures that was developed by taking into consideration various aspects of congestion and reliability, to assess impacts of safety projects on congestion. Safety projects are necessary to help move Virginia’s roadways toward safer operation, but can contribute to congestion and unreliability during execution, and can affect operations after execution. However, safety projects are assessed primarily for safety improvements, not for congestion. This study identifies an appropriate suite of measures, and quantifies and compares the congestion and reliability impacts of safety projects on roadways for the periods before, during, and after project execution. The paper presents the performance measures, examines their sensitivity based on operating conditions, defines thresholds for congestion and reliability, and demonstrates the measures using a set of Virginia safety projects. The data set consists of 10 projects totalling 92 mi and more than 1M data points. The study found that, overall, safety projects tended to have a positive impact on congestion and reliability after completion, and the congestion variability measures were sensitive to the threshold of reliability. The study concludes with practical recommendations for primary measures that may be used to measure overall impacts of safety projects: percent vehicle miles traveled (VMT) reliable with a customized threshold for Virginia; percent VMT delayed; and time to travel 10 mi. However, caution should be used when applying the results directly to other situations, because of the limited number of projects used in the study.

scholarly journals The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 37
Author(s):  
Shixun Wang ◽  
Qiang Chen
Keyword(s):  
Image Retrieval ◽  
Loss Function ◽  
Single Mode ◽  
Local Similarity ◽  
Text And Image ◽  
Data Set ◽  
Standard Data ◽  
Weak Learner ◽  
Great Progress ◽  
Data Points

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

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