APPLICATION OF LWD ACOUSTIC DISPERSIVE DATA PROCESSING FOR HIGH-QUALITY SHEAR SLOWNESS LOGS IN SLOW FORMATIONS

2021 ◽  
Author(s):  
Ruijia Wang ◽  
◽  
Jiajun Zhao ◽  
Taher Kortam ◽  
◽  
...  

For conventional acoustic monopole sources in a logging-while-drilling (LWD) or wireline environment, shear slowness logs can be hard to obtain, particularly in slow formations where direct refracted shear-wave arrivals are often absent. For LWD dipole sources, formation flexural waves are often coupled with the lowest order of tool flexural waves, so the flexural mode does not approach shear wave slowness at low frequencies. A dispersion correction is required to extract shear slowness from LWD dipole data. Instead, a quadrupole firing, which generates screw waves, is considered the best LWD excitation mode for shear measurement. A fundamental feature of screw waves in an LWD environment is that their non-leaky cutoff frequency slowness is the formation shear slowness. However, slowness data near the cutoff frequency of LWD screw waves are often influenced by noise or the presence of other modes because of low excitation amplitude. To overcome these LWD data processing challenges, we propose a data-driven processing method that uses all useful dispersion responses of existing modes in the frequency domain. The process first generates a differential phase frequency-slowness coherence map and extracts the slowness dispersion vs. frequency. Then, it computes the slowness density log, referring to the intensity of the dispersion response along the slowness axis. Next, an edge-detection method is applied to capture the edge of the first peak associated with shear slowness on the slowness density map. To refine the shear slowness answer, this initial estimate of shear slowness serves as the input to another algorithm that minimizes the misfit between the screw slowness vector and a simplified screw dispersion model. The simplified screw dispersion model consists of a pre-computed base library of theoretical screw dispersion curves and two data-driven parameters. The two data-driven parameters are used by the measured data to stretch the base dispersion model in the frequency and slowness axes, respectively, to account for errors generated by alteration, anisotropy, or other parameters not included in the forward modeling. The method can also be applied to flexural waves, where the initial guess of shear slowness is picked from the slowness density map of flexural waves after dispersion-correction processing. This paper shows a case study of borehole flexural and screw waves processing in soft formations. A modified differential-phase frequency-semblance (MDPFS) approach is applied to extract the mode waves' full-frequency dispersion response from measured waveforms. The data-driven shear slowness processing is applied to the dispersion response. Both dipole flexural waves and quadrupole screw waves are processed. A combination of slowness density log from the flexural or screw wave slowness and the dispersion-corrected slowness is used as a QC metric of the final estimated shear. Results show that flexural and screw dispersions are well measured by the LWD sonic tool, even if the shear slowness is as large as 500 s/ft. Shear slowness extracted from flexural waves and screw waves match well with each other and with wireline shear slowness logs, demonstrating that the processing is reliable and robust.

Geophysics ◽  
2020 ◽  
pp. 1-91
Author(s):  
Ruijia Wang ◽  
Brian Hornby ◽  
Kristoffer Walker ◽  
Chung Chang ◽  
Gary Kainer ◽  
...  

Real-time open hole wireline sonic logging data processing becomes a nontrivial task to accurately, automatically and efficiently evaluate both the compressional and shear slowness of a borehole rock formation when human interaction is not possible and signal processing time is limited to elapsed time between different transmitter firings. To address real-time sonic data processing challenges, we present self-adaptive, data-driven methods to accurately measure formation compressional and shear wave slowness from both monopole and dipole waveforms in all types of formations. These new real-time processing techniques take advantage of the fact that advanced wireline sonic logging tools have wide frequency responses and little to no detectable tool body arrivals. These technology improvements provide an opportunity to implement a first-motion-detection technique that detects the onset of compressional waves in the monopole array waveforms. The knowledge of compressional arrival time and corresponding slowness are then used to project an appropriate slowness-time window in order to identify the monopole refracted shear wave and its slowness based on the range of possible Vp/Vs for earth rock formation. To process the borehole dipole flexural waves, we provide a new, data-driven frequency domain method that enables the evaluation of the full flexural-wave dispersion response and its corresponding low-frequency shear slowness asymptote. Field data processing results show that our methods provide high-quality compressional slowness (DTC) and shear slowness(DTS) measurements that are not affected by other borehole modes or dispersion complications in all formation types.


2021 ◽  
Author(s):  
Airat Kotliar-Shapirov ◽  
Fedor S. Fedorov ◽  
Henni Ouerdane ◽  
Stanislav Evlashin ◽  
Albert G. Nasibulin ◽  
...  

In our manuscript, we present our protocol for data processing to mitigate the effects of interfering analytes on the identification of the chemical species detected by sensors. Considering NO2 and CO2, we designed electrochemical sensors whose response yielded the cyclic voltammetry data that we analyzed to classify single-species components and their mixtures using a data-driven approach to generate a chemical space where their mixtures can be deconvoluted.<br>


2021 ◽  
Author(s):  
Hongbao Zhang ◽  
Baoping Lu ◽  
Lulu Liao ◽  
Hongzhi Bao ◽  
Zhifa Wang ◽  
...  

Abstract Theoretically, rate of penetration (ROP) model is the basic to drilling parameters design, ROP improvement tools selection and drill time & cost estimation. Currently, ROP modelling is mainly conducted by two approaches: equation-based approach and machine learning approach, and machine learning performs better because of the capacity in high-dimensional and non-linear process modelling. However, in deep or deviated wells, the ROP prediction accuracy of machine learning is always unsatisfied mainly because the energy loss along the wellbore and drill string is non-negligible and it's difficult to consider the effect of wellbore geometry in machine learning models by pure data-driven methods. Therefore, it's necessary to develop robust ROP modelling method for different scenarios. In the paper, the performance of several equation-based methods and machine learning methods are evaluated by data from 82 wells, the technical features and applicable scopes of different methods are analysed. A new machine learning based ROP modelling method suitable for different well path types was proposed. Integrated data processing pipeline was designed to dealing with data noises, data missing, and discrete variables. ROP effecting factors were analysed, including mechanical parameters, hydraulic parameters, bit characteristics, rock properties, wellbore geometry, etc. Several new features were created by classic drilling theories, such as downhole weight on bit (DWOB), hydraulic impact force, formation heterogeneity index, etc. to improve the efficiency of learning from data. A random forest model was trained by cross validation and hyperparameters optimization methods. Field test results shows that the model could predict the ROP in different hole sections (vertical, deviated and horizontal) and different drilling modes (sliding and rotating drilling) and the average accuracy meets the requirement of well planning. A novel data processing and feature engineering workflow was designed according the characteristics of ROP modelling in different well path types. An integrated data-driven ROP modelling and optimization software was developed, including functions of mechanical specific energy analysis, bit wear analysis and predict, 2D & 3D ROP sensitivity analysis, offset wells benchmark, ROP prediction, drilling parameters constraints analysis, cost per meter prediction, etc. and providing quantitative evidences for drilling parameters optimization, drilling tools selection and well time estimation.


2020 ◽  
Vol 22 (9) ◽  
pp. 1528-1544
Author(s):  
Mark Andrejevic ◽  
Lina Dencik ◽  
Emiliano Treré

Debates on the temporal shift associated with digitalization often stress notions of speed and acceleration. With the advent of big data and predictive analytics, the time-compressing features of digitalization are compounded within a distinct operative logic: that of pre-emption. The temporality of pre-emption attempts to project the past into a simulated future that can be acted upon in the present; a temporality of pure imminence. Yet, inherently paradoxical, pre-emption is marked by myriads of contrasts and frictions as it is caught between the supposedly all-encompassing knowledge of the data-processing ‘Machine’, and the daily reality of decision-making practices by relevant social actors. In this article, we explore the contrasting temporalities of automated data processing and predictive analytics, using policing as an illustrative example. Drawing on insights from two cases of predictive policing systems that have been implemented among UK police forces, we highlight the prevalence of counter-temporalities as predictive analytics is situated in institutional contexts and consider the conditions of possibility for agency and deliberation. Analysing these temporal tensions in relation to ‘slowness’ as a mode of resistance, the contextual examination of predictive policing advanced in the article provides a contribution to the formation of a deeper awareness of the politics of time in automated data processing; one that may serve to counter the imperative of pre-emption that, taken to the limit, seeks to foreclose the time for politics, action and life.


2019 ◽  
Vol 21 (11-12) ◽  
pp. 2366-2385 ◽  
Author(s):  
Lee McGuigan

Programmatic advertising describes techniques for automating and optimizing transactions in the audience marketplace. Facilitating real-time bidding for audience impressions and personalized targeting, programmatic technologies are at the leading edge of digital, data-driven advertising. But almost no research considers programmatic advertising within a general history of information technology in commercial media industries. The computerization of advertising and media buying remains curiously unexamined. Using archival sources, this study situates programmatic advertising within a longer trajectory, focusing on the incorporation of electronic data processing into the spot television business, starting in the 1950s. The article makes three contributions: it illustrates that (1) demands for information, data processing, and rapid communications have long been central to advertising and media buying; (2) automation “ad tech” developed gradually through efforts to coordinate and accelerate transactions; and (3) the use of computers to increase efficiency and approach mathematical optimization reformatted calculative resources for media and marketing decisions.


2020 ◽  
Author(s):  
Matthias Vanmaercke ◽  
Yixian Chen ◽  
Sofie De Geeter ◽  
Jean Poesen ◽  
Benjamin Campforts

&lt;p&gt;Gully erosion has been recognized as a main driver of soil erosion and land degradation. While numerous studies have focussed on understanding gully erosion at local scales, we have very little insights into the patterns and controlling factors of gully erosion at a global scale. Overall, this process remains notoriously difficult to simulate and predict. A main reason for this is that the complex and threshold-dependent nature of gully formation leads to very high data requirements when aiming to simulate this process over larger areas.&lt;/p&gt;&lt;p&gt;Here we help bridging this gap by presenting the first data-driven analysis of gully head densities at a global scale.&amp;#160; We developed a grid-based scoring method that allows to quickly assess the range of gully head densities in a given area based on Google Earth imagery. Using this approach, we constructed a global database of mapped gully head densities for currently &gt;7400 sites worldwide. Based on this dataset and globally available data layers on relevant environmental factors (topography, soil characteristics, land use) we explored which factors are dominant in explaining global patterns of gully head densities and propose a first global gully head density map.&lt;/p&gt;&lt;p&gt;Our results indicate that there are ca. 1.7 to 2 billion gully heads worldwide. This estimate might underestimate the actual numbers of gully heads since ephemeral gullies (in cropland) and gullies under forest remain difficult to map. Our database and analyses further reveal clear regional patterns in the presence of gullies. Around 27% of the terrestrial surface (excluding Antarctica and Greenland) has a density of &gt; 1 gully head/km&amp;#178;, while an estimated 14% has a density of &gt; 10 gully heads/km&amp;#178; and 4% has even a density of &gt; 100 gully heads/km&amp;#178;. Major hotspots (with &gt; 50 gully heads/km&amp;#178;) include the Chinese loess plateau, but also Iran, large parts of the Sahara Desert, the Andes and Madagascar. In addition, gully erosion also frequently occurs (with typical densities of 1-50 gully heads/km&amp;#178;) in the Mid-West USA, the African Rift, SE-Brazil, India, New-Zealand and Australia.&lt;/p&gt;&lt;p&gt;These regional patterns are mainly explained by topography and climate in interaction with vegetation cover. Overall, the highest gully densities occur in regions with some topography and a (semi-)arid climate. Nonetheless, it is important to point out that not all gully heads are still actively retreating. Building on earlier insights into the magnitude and controlling factors of gully head retreat rates, we explore what our current results imply for assessing actual gully erosion rates at a global scale.&lt;/p&gt;


2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.


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