One or more machine-learning algorithms are trained using the compositional database, and the trained one or more machine-learning algorithms are used to predict phase stability and perform flash calculations for compositional reservoir simulation.RAMAN Vinay...
Applications of Machine Learning in Geothermal Reservoir Engineering From the series: MathWorks Energy Symposium 2023 Prof. Mayank Tyagi, Louisiana State University Learn why Machine Learning is important for digital oil field, the MATLAB Reservoir Simulation Toolkit (MRST) and some application...
machine learning MLR multiple linear regression ANN artificial neural network k-FCV k-fold cross validation GS grid search ANOVA analysis of variance RT reservoir thickness Kh horizontal permeability Kv/Kh vertical-horizontal permeability ratio So oil saturation Po porosity VL vertical location of shale ...
Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model incr...
Machine learning algorithm based on the uniformly constructed dataset is introduced to realize fast and accurate prediction of methane adsorption behavior, which is well validated by typical inorganic and organic models. Moreover, the application of the proposed ML model to predict the adsorption ...
Well logs are processed and Interpreted to estimate in-situ reservoir properties, which are essential for reservoir modeling, reserve estimation, and production forecasting. While the traditional methods are mostly based on multimineral physics or empirical formulae, machine learning provides an alternative...
Machine Learning MLP: Multi-Layer Perceptron MSE: Mean Squared Error MTR: Multi-Target Regression PIM: Perfectly Ineslastic Merging PCE: Polynomial Chaos Expansion SLR: Second-Largest Remnant SMBO: Sequential Model-Based Optimization SPH: Smoothed-Particle Hydrodynamics STR: Single-Target ...
In this work we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the ...
This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. Demonstrated thro
In this study, we developed a method for label-free motion tracking to elucidate the mechanism by which leptospirosis spirochetes move on host cells. Our label-free bacterial tracking method uses machine learning-based image processing that highlights moving objects by subtracting the background from...