Petrophysical Analysis Based on Well Logging Data for Tight Carbonate Reservoir: The SADI Formation Case in Halfaya Oil Field

Carbonate reservoirs are an essential source of hydrocarbons worldwide, and their petrophysical properties play a crucial role in hydrocarbon production. Carbonate reservoirs' most critical petrophysical properties are porosity, permeability, and water saturation. A tight reservoir refers to a reservoir with low porosity and permeability, which means it is difficult for fluids to move from one side to another. This study's primary goal is to evaluate reservoir properties and lithological identification of the SADI Formation in the Halfaya oil field. It is considered one of Iraq's most significant oilfields, 35 km south of Amarah. The Sadi formation consists of four units: A, B1, B2, and B3. Sadi A was excluded as it was not filled with hydrocarbons. The structural and petrophysical models were built based on data gathered from five oil wells. The data from the available well logs, including RHOB, NPHI, SONIC, Gamma-ray, Caliper, and resistivity logs, was used to calculate the petrophysical properties. These logs were analyzed and corrected for environmental factors using IP V3.5 software. where the average formation water resistivity (Rw = 0.04), average mud filtrate resistivity (Rmf = 0.06), and Archie's parameters (m = 2, n = 1.9, and a = 1) were determined. The well-log data values calculated the porosity, permeability, water saturation, and net-to-gross thickness ratio (N/G)

Hypothesis-based machine learning for deep-water channel systems

2020 Spring.Includes bibliographical references.Machine learning algorithms are readily being incorporated into petroleum industry workflows for use in well-log correlation, prediction of rock properties, and seismic data interpretation. However, there is a clear disconnect between sedimentology and data analytics in these workflows because sedimentologic data is largely qualitative and descriptive. Sedimentology defines stratigraphic architecture and heterogeneity, which can greatly impact reservoir quality and connectivity and thus hydrocarbon recovery. Deep-water channel systems are an example where predicting reservoir architecture is critical to mitigating risk in hydrocarbon exploration. Deep-water reservoirs are characterized by spatial and temporal variations in channel body stacking patterns, which are difficult to predict with the paucity of borehole data and low quality seismic available in these remote locations. These stacking patterns have been shown to be a key variable that controls reservoir connectivity. In this study, the gap between sedimentology and data analytics is bridged using machine learning algorithms to predict stratigraphic architecture and heterogeneity in a deep-water slope channel system. The algorithms classify variables that capture channel stacking patterns (i.e., channel positions: axis, off-axis, and margin) from a database of outcrop statistics sourced from 68 stratigraphic measured sections from outcrops of the Upper Cretaceous Tres Pasos Formation at Laguna Figueroa in the Magallanes Basin, Chile. An initial hypothesis that channel position could be predicted from 1D descriptive sedimentologic data was tested with a series of machine learning algorithms and classification schemes. The results confirmed this hypothesis as complex algorithms (i.e., random forest, XGBoost, and neural networks) achieved accuracies above 80% while less complex algorithms (i.e., decision trees) achieved lower accuracies between 60%-70%. However, certain classes were difficult for the machine learning algorithms to classify, such as the transitional off-axis class. Additionally, an interpretive classification scheme performed better (by around 10%-20% in some cases) than a geometric scheme that was devised to remove interpretation bias. However, outcrop observations reveal that the interpretive classification scheme may be an over-simplified approach and that more heterogeneity likely exists in each class as revealed by the geometric scheme. A refined hypothesis was developed that a hierarchical machine learning approach could lend deeper insight into the heterogeneity within sedimentologic classes that are difficult for an interpreter to discern by observation alone. This hierarchical analysis revealed distinct sub-classes in the margin channel position that highlight variations in margin depositional style. The conceptual impact of these varying margin styles on fluid flow and connectivity is shown

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Spatially detailed analysis of drill core samples with Laser-Induced Breakdown Spectroscopy: Detection, classification, and quantification of rare earth elements and lithium

In the transformation towards climate neutral consumption, electric alternatives rise in favour of fossil energy sources in a variety of different fields. Lithium and several elements from the group of Rare Earth Elements (REEs) are of particular importance for modern battery production and the supply of green energy, and therefore play a crucial role for this transformation. Their demand has increased constantly over the last years and an ongoing trend is expected for the future. New instruments and analytical methods for the geochemical investigation of drill cores can support mineral exploration and active mining and thereby help to cope with the growing demand. Laser-Induced Breakdown Spectroscopy (LIBS) is an analytical technique with many advantages for the analysis of drill core material. It has a high measurement speed, no sample preparation is needed, and major, minor as well as trace elements can be detected in a single spectrum under atmospheric conditions. Nevertheless, physical and chemical matrix effects prevent a straightforward analysis of heterogeneous material, which is especially relevant for spatially resolved investigations of drill core samples. This work displays novel methods that enable the analysis of LIBS mappings of large REE- and Li-bearing drill core samples by overcoming the problematic matrix effects with different un- semi- and supervised machine learning algorithms. In the first application, drill core samples of brecciated carbonatites were spatially investigated with LIBS to establish an intensity limit for La using the k-means clustering algorithm. Based on this intensity limit, REE enrichments were detected in the investigated sample. Afterwards, the REE content of the sample was estimated with mass balance calculations. For the second application, different Li-bearing drill core samples were mapped in high resolution with LIBS and a new classification model was developed. It combines Linear Discriminant Analysis (LDA) and One-Class Support Vector Machines (OC-SVM) to enable the classification of minerals that were covered by a train set, while also identifying LIBS matrices that are unknown to the model. The third application combined Laser Ablation – Inductively Coupled Plasma – Time of Flight Mass Spectrometry (LA-ICP-TOFMS) with LIBS measurements of the same sample. After image registration, this reference sample was used to create a Least-Square Support Vector Machine (LS-SVM) quantification model, which can be employed to convert LIBS intensities of similar material into element concentrations. The model allows a pixel-specific, spatially resolved quantification of multiple minerals with a single model. Each application displays possible solutions to minimize the influence of physical and chemical matrix effects on the spatial analysis of LIBS mappings of large drill core samples, which enables different kinds of analysis. Thereby, the great potential but also the challenges of LIBS as an analytical tool in geology and mining are highlighted

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen

Autonomous and Real Time Rock Image Classification using Convolutional Neural Networks

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify 9 different types of rock images using a with the image features extracted autonomously. Through this method, they achieved a test accuracy of 96.71%. Within the last few years, Convolutional Neural Networks (CNNs) have been shown to be perform better than other algorithms in classifying images of everyday objects. In light of this development, this thesis demonstrates the use of CNNs to classify the same set of rock images. With the addition of dataset augmentation, a 3-layer CNN is shown to have a significant improvement over Shu et. al.\u27s results, achieving an average accuracy of 99.60% across 10 trials on the test set. Multiple CNN operations with similar output shapes have been designed and appended to an existing architecture to expand hyperparameter considerations. These Combinational Fully Connected Neural Networks achieves an accuracy of 99.36% on the test set. The resulting models are also shown to be lightweight enough that they can be deployed on a mobile device. To tackle a more interesting and practical problem, CNNs have also been designed to classify natural scene images of rocks, an inherently more complex dataset. The task has been simplified into a binary classification problem where the images are classified into breccia and non-breccia. This thesis shows that a Combinational Fully Connected Neural Network achieves an accuracy of 93.50%, better than a 5-layer CNN, which achieves 89.43%