Doppler ultrasound for quantification of fluid influx/ efflux from borehole fractures using LWD tools

The work presented in this thesis is aimed at studying the potential for applying ultrasonic Doppler measurements during a borehole drilling process using ultrasonic logging-while-drilling (LWD) tools. The primary intention for the application of Doppler measurements is the identification and quantification of influx/efflux of formation fluids/drilling mud through fractures and pore networks in boreholes. This thesis is composed of six chapters and all chapters may be read individually. The results presented in this thesis are generated using laboratory scale experiments and some simulations. This work was funded by the Centre for Innovative Ultrasound Solutions (CIUS), which is a Norwegian Research Council appointed centre for research-based innovation (SFI). CIUS was hosted by the Department of Medical Imaging and Circulation (ISB), at the Faculty ofMedicine at Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. Chapter 1 provides a brief background about the project and introduces the ultrasonic LWD tools and technologies currently used in the field. A detailed summary of literature on ultrasonic Doppler measurements for borehole applications is provided so as to set the basis for this project. Finally the objectives set for this project during its inception are outlined along with links to relevant chapters within the thesis where they are addressed. The following chapters are grouped into two independent parts. Part I comprises of chapters 2-4 and are based on the topic of Doppler ultrasound for influx/efflux velocity estimation from borehole fractures. Part II comprises of chapters 5 and 6, and are based on the topic of influx gas detection and quantification in boreholes using ultrasound backscatter. Chapter 2 discusses the optimal parameters for ultrasonic Doppler acquisition in typical LWD conditions along with the expected upper and lower limits on velocity estimation. The use of short pulse-lengths of 2-4 for flow velocity estimation is explored along with the corresponding measurement error. The possibility of achieving a high signal to noise ratio of about 30 dB, using instrumentation with technical specifications similar to ultrasonic LWD tools, is demonstrated while using water as the working fluid. The improved imaging capabilities of Doppler ultrasound compared to conventional pulseecho imaging is demonstrated for fractures with fluid influx. Estimation of the fracture shape using the power Doppler image with a resolution approaching the point-spread-function of the transducer is demonstrated. Chapter 3 extends the work done in Chapter 2 by evaluating the performance of Doppler ultrasound for velocity estimation and fracture shape imaging using real drilling fluids used in the field. Experimental studies using five different drilling fluids under two categories viz. oil based and water based, and density range of 1200 kg/m3 - 1800 kg/m3 are discussed. Experiments studying the influence of drilling fluids, flowing in orthogonal directions mimicking the annular flow and influx/efflux flow, on Doppler measurements is discussed. The effect of attenuation in the drilling fluids and subsequent influence on SNR of the power Doppler images are discussed. The estimation of fracture shapes using the power Doppler images was done using conventional thresholding approaches in chapters 2 and 3. Chapter 4, which is a collaboration with Sigurd Vangen Wifstad, a fellow CIUS PhD, demonstrates the use of convolutional neural networks (CNNs) to improve the estimation of fracture shapes beyond the point spread function limit. The CNN was trained using simulated power Doppler images from several procedurally generated fracture shapes. The trained model was tested on experimental power Doppler images generated during the work done in chapter 3. The CNN was able to estimate the fracture areas with a significantly lower mean absolute error of 4.9 ± 4.1 mm2, compared to 22.9 ± 1.7 mm2 using the conventional thresholding method. The CNN also enabled the estimation of fracture shape and area using a single frame of the power Doppler image, compared to about 30 frames required for the thresholding method, facilitating a possible increase in scanning speed of the logging tool. Chapter 5 discusses a method for detecting free gas in drilling fluids from the ultrasonic signal using conventional LWD tools. The use of higher order statistics to make the method noise-robust is also described and demonstrated using simulations and experiments. Gas influx in deep boreholes or under high pressure/high temperature conditions is usually in the supercritical state of matter and as such would be dissolved into the drilling fluid. A concept for applying ultrasonic methods for detecting dissolved gas in deep boreholes is also discussed. Several prior studies suggest a good correlation between ultrasound backscatter/ attenuation and the gas content in drilling muds, and thereby propose methods for its quantification in boreholes. However, the aforementioned studies neglect the influence of gas bubble size, which can vary significantly over time and has a significant influence on the ultrasound backscatter/attenuation. A model combining existing theories on ultrasound backscatter from bubbles depending on their size is presented. The proposed model is demonstrated using simulations and experiments, where the ultrasound backscatter is evaluated from bubble clouds of varying bubbles sizes. It is shown that the size and number of bubbles strongly influence ultrasound backscatter intensity, and it is correlated to gas content only when the bubble size distribution is independently known. Such information is difficult to obtain under downhole conditions during drilling. Consequently, it is difficult to reliably apply methods based on ultrasound backscatter, and by extension its attenuation, for the quantification of gas content during influx events in a borehole

Analytical Methods, Correlative Microscopy and Software Tools for Quantitative Single Molecule Localization Microscopy

Single Molecule Localization Microscopy (SMLM) techniques, such as PhotoActivated Localization Microscopy (PALM) and STochastic Optical Reconstruction Microscopy(STORM), can get around the diffraction limited resolution of conventional fluorescent microscopy (FM). However, there are a number of possible sources of imaging errors in SMLM which can significantly impact such studies. They include labeling artifacts, a limited detection efficiency of label molecules(of about 40-60% in PALM, e.g.,) and an uncertainty in localization in the range of 20-50nm. This thesis describes a rigorous review of such sources of error. Also, the SMLM readout is different from FM, the tools used in FM may not be directly applicable to SMLM. Accurate and precise quantitative imaging with SMLM requires analytical, experimental and software tools that address such issues. We describe analytical methods that accounts for two major sources of errors in analysis of membrane protein organization with SMLM. We model limited detection efficiency as independent subsampling of the set of label molecules. We then use a theoretical property of commonly used second order properties in quantitative SMLM, such as Ripleyâs K-function, L(r) - r function and the Pair Correlation Function (PCF), to show that they are invariant to such subsampling. We derive expressions for their stochastic estimators due to subsampling, and characterize the errors. The results can be extended to co-localization analysis as well. We then describe a method that estimate the true locations of points given the observed ones in clusters. We characterize the relative Mean Squared Error of the combined approach, and find that it can significantly reduce the errors in quantification. We apply these methods on data on clustering due to photoblinking of individual fluorophores, and data with redundant labeling. We then study the theoretical properties of a function that has been proposed as an estimator of cluster size. We also describe a method to identify the cluster model from data. SMLM provides single molecule resolution images of specific molecular species. Atomic Force Microscopy ( AFM), on the other hand, provides nonspecific, high resolution spatial profile information. Correlative AFM-SMLM can provide not only validation of SMLM, but also the complementary information so obtained can be used to design innovative experiments. We describe in vitro imaging of actin filaments with an AFM-SMLM correlative tool, that could provide information about sources of imaging inhomogeneity in SMLM. The tool were also used to image live mammalian cells, and can be used to obtain nanoscale information about mechanical properties of cells and also as a tool for nanomanipulation. Co-localization is a common spatial interaction quantification method used in biological imaging. It is possible to use tools from spatial statistics to obtain better statistical power on tests of spatial interaction, compared to conventional co-localization measures. Such tools can also handle SMLM data better, since they work on point patterns rather than images. We describe an ImageJ/Fiji plugin that implements a spatial statistics framework that extends the concept of co-localization, by means of a model based on Gibbs function of interaction potentials. We describe the application of this software on both confocal microscopy data of virus-endosomes, and SMLM data on GPCR protein-clathrin

Perspectives on a 6G Architecture

Mobile communications have been undergoing a generational change every ten years. Whilst we are just beginning to roll out 5G networks, significant efforts are planned to standardize 6G that is expected to be commercially introduced by 2030. This paper looks at the use cases for 6G and their impact on the network architecture to meet the anticipated performance requirements. The new architecture is based on integrating various network functions in virtual cloud environments, leveraging the advancement of artificial intelligence in all domains, integrating different sub-networks constituting the 6G system, and on enhanced means of exposing data and services to third parties.Comment: 7 pages, 5 figures, one tabl

Accounting for Limited Detection Efficiency and Localization Precision in Cluster Analysis in Single Molecule Localization Microscopy

Single Molecule Localization Microscopy techniques like PhotoActivated Localization Microscopy, with their sub-diffraction limit spatial resolution, have been popularly used to characterize the spatial organization of membrane proteins, by means of quantitative cluster analysis. However, such quantitative studies remain challenged by the techniques' inherent sources of errors such as a limited detection efficiency of less than 60%, due to incomplete photo-conversion, and a limited localization precision in the range of 10 - 30nm, varying across the detected molecules, mainly depending on the number of photons collected from each. We provide analytical methods to estimate the effect of these errors in cluster analysis and to correct for them. These methods, based on the Ripley's L(r) - r or Pair Correlation Function popularly used by the community, can facilitate potentially breakthrough results in quantitative biology by providing a more accurate and precise quantification of protein spatial organization

On characterizing protein spatial clusters with correlation approaches

Spatial aggregation of proteins might have functional importance, e.g., in signaling, and nano-imaging can be used to study them. Such studies require accurate characterization of clusters based on noisy data. A set of spatial correlation approaches free of underlying cluster processes and input parameters have been widely used for this purpose. They include the radius of maximal aggregation r(a) obtained from Ripley's L(r) - r function as an estimator of cluster size, and the estimation of various cluster parameters based on an exponential model of the Pair Correlation Function(PCF). While convenient, the accuracy of these methods is not clear: e.g., does it depend on how the molecules are distributed within the clusters, or on cluster parameters? We analyze these methods for a variety of cluster models. We find that ra relates to true cluster size by a factor that is nonlinearly dependent on parameters and that can be arbitrarily large. For the PCF method, for the models analyzed, we obtain linear relationships between the estimators and true parameters, and the estimators were found to be within +/- 100% of true parameters, depending on the model. Our results, based on an extendable general framework, point to the need for caution in applying these methods

Effect of bubble size on ultrasound backscatter from bubble clouds in the context of gas kick detection in boreholes

Early detection of gas influx in boreholes while drilling is of significant interest to drilling operators. Several studies suggest a good correlation between ultrasound backscatter/attenuation and gas volume fraction (GVF) in drilling muds, and thereby propose methods for quantification of GVF in boreholes. However, the aforementioned studies neglect the influence of bubble size, which can vary significantly over time. This paper proposes a model to combine existing theories for ultrasound backscatter from bubbles depending on their size, viz. Rayleigh scattering for smaller bubbles, and specular reflection for larger bubbles. The proposed model is demonstrated using simulations and experiments, where the ultrasound backscatter is evaluated from bubble clouds of varying bubbles sizes. It is shown that the size and number of bubbles strongly influence ultrasound backscatter intensity, and it is correlated to GVF only when the bubble size distribution is known. The information on bubble size is difficult to obtain in field conditions causing this correlation to break down. Consequently, it is difficult to reliably apply methods based on ultrasound backscatter, and by extension its attenuation, for the quantification of GVF during influx events in a borehole. These methods can however be applied as highly sensitive detectors of gas bubbles for GVF ≥1% vol.publishedVersio

MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far. Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy. Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software

Progress in quantitative single-molecule localization microscopy

With the advent of single-molecule localization microscopy (SMLM) techniques, intracellular proteins can be imaged at unprecedented resolution with high specificity and contrast. These techniques can lead to a better understanding of cell functioning, as they allow, among other applications, counting the number of molecules of a protein specie in a single cell, studying the heterogeneity in protein spatial organization, and probing the spatial interactions between different protein species. However, the use of these techniques for accurate quantitative measurements requires corrections for multiple inherent sources of error, including: overcounting due to multiple localizations of a single fluorophore (i.e., photoblinking), undercounting caused by incomplete photoconversion, uncertainty in the localization of single molecules, sample drift during the long imaging time, and inaccurate image registration in the case of dual-color imaging. In this paper, we review recent efforts that address some of these sources of error in quantitative SMLM and give examples in the context of photoactivated localization microscopy (PALM)