Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

scholarly journals Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

2021 ◽  
Vol 13 (24) ◽  
pp. 4990
Author(s):  
Tianjun Qi ◽  
Yan Zhao ◽  
Xingmin Meng ◽  
Wei Shi ◽  
Feng Qing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Distribution ◽  
Fault Zone ◽  
Roc Curve ◽  
Human Activities ◽  
Geological Structure ◽  
Machine Learning Algorithms ◽  
Qinling Mountains ◽  
Western Qinling ◽  
Landslide Distribution

The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

Quantitative Evaluation of The Chemical Composition of γ-γ’Interfaces in Ni Superalloys

2001 ◽  
Vol 7 (S2) ◽  
pp. 264-265
Author(s):  
H. A. Calderon ◽  
M. Benyoucef ◽  
N. Clement
Keyword(s):  
Mechanical Properties ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Large Volume ◽  
Alloy Composition ◽  
Volume Fraction ◽  
Alloying Elements ◽  
Local Distribution ◽  
Strain Fields ◽  
Ni Alloys

The excellent mechanical properties of Ni based superalloys depend upon the presence of γ’ particles (LI2 structure). Their volume fraction, spatial distribution and size determine the mechanical strength of these alloys. Ni alloys for technological applications make use of large volume fractions of precipitates where processes of coarsening and coalescence take place during service leading in some cases to deterioration of properties. Addition of different alloying elements prevents accelerated coalescence by retarding diffusion and thus improving the mechanical properties of such alloys. Coalescence can also take place under the influence of an applied stress leading to the formation of rafts of the y' phase. For example the microstructure changes during creep deformation, depending on the alloy composition, with the corresponding formation of dislocation networks and rafts of different morphologies [1]. The γ-γ’ interfaces are also different depending on the alloy composition and most likely to the local distribution of alloying elements and their strain fields.

scholarly journals Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 395
Author(s):  
Milan Koreň ◽  
Rastislav Jakuš ◽  
Martin Zápotocký ◽  
Ivan Barka ◽  
Jaroslav Holuša ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Distribution ◽  
Discriminant Analysis ◽  
Bark Beetle ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Forest Damage ◽  
Spruce Forest ◽  
Explanatory Variables ◽  
The Mean

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.

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