Visual Techniques for Geological Fieldwork Using Mobile Devices

Visual techniques in general and 3D visualisation in particular have seen considerable adoption within the last 30 years in the geosciences and geology. Techniques such as volume visualisation, for analysing subsurface processes, and photo-coloured LiDAR point-based rendering, to digitally explore rock exposures at the earth’s surface, were applied within geology as one of the first adopting branches of science. A large amount of digital, geological surface- and volume data is nowadays available to desktop-based workflows for geological applications such as hydrocarbon reservoir exploration, groundwater modelling, CO2 sequestration and, in the future, geothermal energy planning. On the other hand, the analysis and data collection during fieldwork has yet to embrace this ”digital revolution”: sedimentary logs, geological maps and stratigraphic sketches are still captured in each geologist’s individual fieldbook, and physical rocks samples are still transported to the lab for subsequent analysis. Is this still necessary, or are there extended digital means of data collection and exploration in the field ? Are modern digital interpretation techniques accurate and intuitive enough to relevantly support fieldwork in geology and other geoscience disciplines ? This dissertation aims to address these questions and, by doing so, close the technological gap between geological fieldwork and office workflows in geology. The emergence of mobile devices and their vast array of physical sensors, combined with touch-based user interfaces, high-resolution screens and digital cameras provide a possible digital platform that can be used by field geologists. Their ubiquitous availability increases the chances to adopt digital workflows in the field without additional, expensive equipment. The use of 3D data on mobile devices in the field is furthered by the availability of 3D digital outcrop models and the increasing ease of their acquisition. This dissertation assesses the prospects of adopting 3D visual techniques and mobile devices within field geology. The research of this dissertation uses previously acquired and processed digital outcrop models in the form of textured surfaces from optical remote sensing and photogrammetry. The scientific papers in this thesis present visual techniques and algorithms to map outcrop photographs in the field directly onto the surface models. Automatic mapping allows the projection of photo interpretations of stratigraphy and sedimentary facies on the 3D textured surface while providing the domain expert with simple-touse, intuitive tools for the photo interpretation itself. The developed visual approach, combining insight from all across the computer sciences dealing with visual information, merits into the mobile device Geological Registration and Interpretation Toolset (GRIT) app, which is assessed on an outcrop analogue study of the Saltwick Formation exposed at Whitby, North Yorkshire, UK. Although being applicable to a diversity of study scenarios within petroleum geology and the geosciences, the particular target application of the visual techniques is to easily provide field-based outcrop interpretations for subsequent construction of training images for multiple point statistics reservoir modelling, as envisaged within the VOM2MPS project. Despite the success and applicability of the visual approach, numerous drawbacks and probable future extensions are discussed in the thesis based on the conducted studies. Apart from elaborating on more obvious limitations originating from the use of mobile devices and their limited computing capabilities and sensor accuracies, a major contribution of this thesis is the careful analysis of conceptual drawbacks of established procedures in modelling, representing, constructing and disseminating the available surface geometry. A more mathematically-accurate geometric description of the underlying algebraic surfaces yields improvements and future applications unaddressed within the literature of geology and the computational geosciences to this date. Also, future extensions to the visual techniques proposed in this thesis allow for expanded analysis, 3D exploration and improved geological subsurface modelling in general.publishedVersio

Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devices

Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data. And InversionNet is one of the most successful data-driven machine learning models that is applied to seismic FWI. However, the high computing costs to run InversionNet have made it challenging to be efficiently deployed on edge devices that are usually resource-constrained. Therefore, we propose to employ the structured pruning algorithm to get a lightweight version of InversionNet, which can make an efficient inference on edge devices. And we also made a prototype with Raspberry Pi to run the lightweight InversionNet. Experimental results show that the pruned InversionNet can achieve up to 98.2 % reduction in computing resources with moderate model performance degradation

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud supported FinTech Applications

Financial Technology have revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. However, the massive amount of data (often called as big data) generated seamlessly across different geographic locations can end end up as a bottleneck for the underlying network infrastructure. To mitigate this challenge, software-defined network (SDN) has been leveraged in the proposed approach to provide scalability and resilience in multi-controller environment. However, in case if one of these controllers fail or cannot work as per desired requirements, then either the network load of that controller has to be migrated to another suitable controller or it has to be divided or balanced among other available controllers. For this purpose, the proposed approach provides an adaptive recovery mechanism in a multi-controller SDN setup using support vector machine-based classification approach. The proposed work defines a recovery pool based on the three vital parameters, reliability, energy, and latency. A utility matrix is then computed based on these parameters, on the basis of which the recovery controllers are selected. The results obtained prove that it is able to perform well in terms of considered evaluation parameters

Development of a Python Library for Processing Seismic Time Series

Earthquakes occur around the world every day. This natural phenomena can result in enormous destruction and loss of life. However, at the same time, it is the primary source for studying Earth, the active planet. The seismic waves generated by earthquakes propagate deep into the Earth, carrying considerable information about the Earth’s structure, from the shallow depths in the crust to the core. The information transferred by seismic waves needs advanced signal processing and inversion tools to be converted into useful information about the Earths inner structures, from local to global scales. The ever­evolving interest for investigating more accurately the terrestrial system led to the development of advanced signal processing algorithms to extract optimal information from the recorded seismic waveforms. These algorithms use advanced numerical modeling to extract optimal information from the different seismic phases generated by earthquakes. The development of algorithms from a mathematical­physical point of view is of great interest; on the other hand, developing a platform for their implementation is also significant. This research aims to build a bridge between the development of purely theoretical ideas in seismology and their functional implementation. In this dissertation SeisPolPy, a high quality Python­based library for processing seismic waveforms is developed. It consists of the latest polarization analysis and filter algorithms to extract different seismic phases in the recorded seismograms. The algorithms range from the most common algorithms in the literature to a newly developed method, sparsity­promoting time­frequency filtering. In addition, the focus of the work is on the generation of high­quality synthetic seismic data for testing and evaluating the algorithms. SeisPolPy library, aims to provide seismology community a tool for separation of seismic phases by using high­resolution polarization analysis and filtering techniques. The research work is carried out within the framework of the Seismicity and HAzards of the sub­saharian Atlantic Margin (SHAZAM) project that requires high quality algorithms able to process the limited seismic data available in the Gulf of Guinea, the study area of the SHAZAM project.Terramotos ocorrem todos os dias em todo o mundo. Esta fenomeno natural pode vir a resultar numa enorme destruição e perda de vidas. No entanto, ao mesmo tempo, é a principal fonte para o estudo da Terra, o planeta activo. As ondas sísmicas geradas pelos terramotos propagam­se profundamente na Terra, levando informação considerável sobre a estrutura da Terra, desde as zonas de menor profundidade da crosta até ao núcleo. A informação transferida por ondas sísmicas necessita de processamento avançado de sinais e ferramentas de inversão para ser convertida em informação util sobre a estrutura interna da Terra, desde escalas locais a globais. O interesse sempre crescente em investigar com maior precisão o sistema terrestre levou ao desenvolvimento de algoritmos avançados de processamento de sinais para extrair informação óptima das formas de ondas sísmicas registadas. Estes algoritmos fazem uso de modelos numéricos avançados para extrair informação óptima das diferentes fases sísmicas geradas pelos terramotos. O desenvolvimento de algoritmos de um ponto de vista matemático­físico é de grande interesse; por outro lado, o desenvolvimento de uma plataforma para a sua implementação é também significativo. Esta investigação visa construir uma ponte entre o desenvolvimento de ideias puramente teóricas em sismologia e a sua implementação funcional. Com o decorrer desta dissertação foi desenvolvido o SeisPolPy, uma biblioteca de alta qualidade baseada em Python para o processamento de formas de ondas sísmicas. Consiste na mais recente análise de polarização e algoritmos de filtragem para extrair diferentes fases sísmicas nos sismogramas registados. Os algoritmos variam desde os algoritmos mais comuns na literatura até um método recentemente desenvolvido, que promove a frequência de filtragem por tempo e frequência. Além disso, o foco do trabalho é a geração de dados sísmicos sintéticos de alta qualidade para testar e avaliar os algoritmos. A biblioteca SeisPolPy, visa fornecer à comunidade sismológica uma ferramenta para a separação das fases sísmicas, utilizando técnicas de análise de polarização e filtragem de alta resolução. O trabalho de investigação é realizado no âmbito do projecto SHAZAM que requer algoritmos de alta qualidade que possuam a capacidade de processar os dados sísmicos, limitados, disponíveis no Golfo da Guiné, a área de estudo do projecto

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Coupled Lattice Boltzmann Modeling Framework for Pore-Scale Fluid Flow and Reactive Transport

In this paper, we propose a modeling framework for pore-scale fluid flow and reactive transport based on a coupled lattice Boltzmann model (LBM). We develop a modeling interface to integrate the LBM modeling code parallel lattice Boltzmann solver and the PHREEQC reaction solver using multiple flow and reaction cell mapping schemes. The major advantage of the proposed workflow is the high modeling flexibility obtained by coupling the geochemical model with the LBM fluid flow model. Consequently, the model is capable of executing one or more complex reactions within desired cells while preserving the high data communication efficiency between the two codes. Meanwhile, the developed mapping mechanism enables the flow, diffusion, and reactions in complex pore-scale geometries. We validate the coupled code in a series of benchmark numerical experiments, including 2D single-phase Poiseuille flow and diffusion, 2D reactive transport with calcite dissolution, as well as surface complexation reactions. The simulation results show good agreement with analytical solutions, experimental data, and multiple other simulation codes. In addition, we design an AI-based optimization workflow and implement it on the surface complexation model to enable increased capacity of the coupled modeling framework. Compared to the manual tuning results proposed in the literature, our workflow demonstrates fast and reliable model optimization results without incorporating pre-existing domain knowledge

Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art

Traditional formation evaluation via laboratory testing and wireline logging of horizontal wells and deep formations face challenges due to several reasons and lead to uncertain results. Real-time cuttings and drilling data analysis of horizontal wells is an actively developing alternative approach to formation evaluation that can overcome several challenges faced by laboratory testing and wireline logging in providing improved estimates of formation parameters relevant to reservoir and completion quality. This study presents a state-of-the-art review of the latest methods and technologies in drill cuttings analysis to enable real-time characterization of the entire suite of formation properties, including chemical composition, densities and porosity, permeability, lithology, geomechanical properties, and characterization of fracture patterns. Specifically, the methods/techniques that enable characterizing drill cuttings in real-time and critically reviewed in this study include Raman spectroscopy for chemical composition, nuclear magnetic resonance for densities and porosity, liquid pressure pulse for permeability, deep learning for rock classification, 7 different methods for geomechanical properties, and mud loss signatures for characterization of fracture patterns. Benchmark comparison of drill cuttings analysis with the measurements from the core samples at similar depths is also reviewed. Key learnings are provided in 4 areas: to address the uncertainties in estimates of specific parameters affected by physical deformations due to drill bits, minimum cutting size for reliable nuclear magnetic resonance data, sweet spot identification, and power and network considerations for real-time analysis, respectively.Cited as: Singh, H., Li, C., Cheng, P., Wang, X., Hao, G., Liu, Q. Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art. Advances in Geo-Energy Research, 2023, 8(1): 19-36. https://doi.org/10.46690/ager.2023.04.0