Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Comprehensive analyses of transport phenomena and thermodynamics of complex multiphysics systems are laborious and computationally intensive. Yet, such analyses are often required during the design of thermal and process equipment. As a remedy, this paper puts forward a novel approach to the prediction of transport behaviours of multiphysics systems, offering significant reductions in the computational time and cost. This is based on machine learning techniques that utilize the data generated by computational fluid dynamics for training purposes. The physical system under investigation includes a stagnation-point flow of a hybrid nanofluid (Cu−Al2O3/Water) over a blunt object embedded in porous media. The problem further involves mixed convection, entropy generation, local thermal non-equilibrium and non-linear thermal radiation within the porous medium. The SVR (Support Machine Vector) model is employed to approximate velocity, temperature, Nusselt number and shear-stress as well as entropy generation and Bejan number functions. Further, PSO meta-heuristic algorithm is applied to propose correlations for Nusselt number and shear stress. The effects of Nusselt number, temperature fields and shear stress on the surface of the blunt-body as well as thermal and frictional entropy generation are analysed over a wide range of parameters. Further, it is shown that the generated correlations allow a quantitative evaluation of the contribution of a large number of variables to Nusselt number and shear stress. This makes the combined computational and artificial intelligence (AI) approach most suitable for design purposes.

A machine learning approach to the prediction of transport and thermodynamic processes in multiphysics systems - heat transfer in a hybrid nanofluid flow in porous media

Modeling the SOFC by BP neural network algorithm

ABSTRACT Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the needs of current practice, to exploit new opportunities from emerging technologies or to meet new needs from digital transformation, and to take advantage of current knowledge and accumulated experience. This agenda is called data-centric geotechnics and it contains three core elements: data centricity, fit for (and transform) practice, and geotechnical context. The future of machine learning in geotechnics should be envisioned with this “data first practice central” agenda in mind. Data-driven site characterization (DDSC) is an active research topic in this agenda because an understanding of the ground is crucial in all projects. Examples of DDSC challenges are ugly data and explainable site recognition. Additional challenges include making ML indispensable (ML supremacy), learning how to learn (meta-learning), and becoming smart (digital twin).

Future of machine learning in geotechnics

Key factors of marine shale conductivity in southern China—Part II: The influence of pore system and the development direction of shale gas saturation models

Application of ML & AI to model petrophysical and geo-mechanical properties of shale reservoirs – A systematic literature review

Smart shale gas production performance analysis using machine learning applications

Numerical Trend Analysis for Factors Affecting EOR Performance and CO2 Storage in Tight Oil Reservoirs

Artificial lift system optimization using machine learning applications

Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

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Amirmasoud K. Dahaghi

Papers

Application of ML & AI to model petrophysical and geo-mechanical properties of shale reservoirs – A systematic literature review

Smart shale gas production performance analysis using machine learning applications

Numerical Trend Analysis for Factors Affecting EOR Performance and CO2 Storage in Tight Oil Reservoirs

Artificial lift system optimization using machine learning applications

AI/ML assisted shale gas production performance evaluation