scholarly journals Regularized ridge regression models to estimate static elastic moduli from wireline measurements: case study from Southern Iraq

Author(s):  
Husam H. Alkinani ◽  
Abo Taleb T. Al-Hameedi ◽  
Shari Dunn-Norman ◽  
Munir Aldin ◽  
Deepak Gokaraju ◽  
...  

AbstractElastic moduli such as Young’s modulus (E), Poisson’s ratio (v), and bulk modulus (K) are vital to creating geomechanical models for wellbore stability, hydraulic fracturing, sand production, etc. Due to the difficulty of obtaining core samples and performing rock testing, alternatively, wireline measurements can be used to estimate dynamic moduli. However, dynamic moduli are significantly different from elastic moduli due to many factors. In this paper, correlations for three zones (Nahr Umr shale, Zubair shale, and Zubair sandstone) located in southern Iraq were created to estimate static E, K, and ν from dynamic data. Core plugs from the aforementioned three zones alongside wireline measurements for the same sections were acquired. Single-stage triaxial (SST) tests with CT scans were executed for the core plugs. The data were separated into two parts; training (70%), and testing (30%) to ensure the models can be generalized to new data. Regularized ridge regression models were created to estimate static E, K, and ν from dynamic data (wireline measurements). The shrinkage parameter (α) was selected for each model based on an iterative process, where the goal is to ensure having the smallest error. The results showed that all models had testing R2 ranging between 0.92 and 0.997 and consistent with the training results. All models of E, K, and ν were linear besides ν for the Zubair sandstone and shale which were second-degree polynomial. Furthermore, root means squared error (RMSE) and mean absolute error (MAE) were utilized to assess the error of the models. Both RMSE and MAE were consistently low in training and testing without a large discrepancy. Thus, with the regularization of ridge regression and consistent low error during the training and testing, it can be concluded that the proposed models can be generalized to new data and no overfitting can be observed. The proposed models for Nahr Umr shale, Zubair shale, and Zubair sandstone can be utilized to estimate E, K, and ν based on readily available dynamic data which can contribute to creating robust geomechanical models for hydraulic fracturing, sand production, wellbore stability, etc.

Author(s):  
Osama Siddig ◽  
Salaheldin Elkatatny

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.


2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.


2020 ◽  
Vol 10 (5) ◽  
pp. 984-993
Author(s):  
Yongming Li ◽  
Yuanlin Zheng ◽  
Pin Wang ◽  
Xinzheng Zhang ◽  
Xiaoping Zeng ◽  
...  

Age estimation is very useful in the fields of pattern recognition and data mining, especially for medical problems. The current methods of age estimation do not consider the relationships among instances, especially the internal hierarchical structure, which limits the potential improvement of the age estimation error. A deep age estimation mechanism based on deep instance weighting fusion is proposed to solve this problem. First, an iterative means clustering (IMC) algorithm is designed to construct the hierarchical instance space (multiplelayer instance space) and obtain multiple trained regression models. Second, a deep instance weighting fusion (DIWF) mechanism is designed to fuse the results from the trained regression models to produce the final results. The experimental results show that the mean absolute error (MAE) of the estimated ages can be decreased significantly on two publicly available data sets, with relative gains of 4.97% and 0.8% on the Heart Disease Data Set and Diabetes Mellitus Data Set, respectively. Additionally, some factors that may influence the performance of the proposed mechanism are studied. In general, the proposed age estimation mechanism is effective. In addition, the mechanism is not a concrete algorithm but framework algorithm (or mechanism), and can be used to generate various concrete age estimation algorithms, so the mechanism is helpful for related studies.


2006 ◽  
Author(s):  
Bailin Wu ◽  
Nulwhoffal Mohamed ◽  
Chee Phuat Tan ◽  
Md. Wakif Sukahar ◽  
Teh Yat Hong ◽  
...  

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