Academic literature on the topic 'Time machine (Wells, H. G.)'
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Journal articles on the topic "Time machine (Wells, H. G.)"
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Harris, Mason. "The Time Machine by H. G. Wells." ESC: English Studies in Canada 27, no. 1-2 (2001): 230–33. http://dx.doi.org/10.1353/esc.2001.0019.
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Shackleton, David. "H. G. WELLS, GEOLOGY, AND THE RUINS OF TIME." Victorian Literature and Culture 45, no. 4 (November 8, 2017): 839–55. http://dx.doi.org/10.1017/s1060150317000249.
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H. G. Wells's The Time Machine (1895) has hitherto been read in two principal scientific contexts: those of evolutionary biology and thermodynamic physics. Numerous critics have situated the romance in the context of evolutionary biology and contemporary discourses of degeneration (McLean 11–40; Greenslade 32–41). Others have discussed it in the context of thermodynamic physics. For instance, Bruce Clarke has read The Time Machine as “a virtual allegory of classical thermodynamics,” and shows that its combination of physical and social entropy reflects a wider transfer within the period of concepts and metaphors from physical science to social discourses of degeneration (121–26). Neatly linking these scientific contexts with issues of form, Michael Sayeau has argued that the social and physical entropy that are themes of the romance are reflected in its narrative structure, which manifests a type of narrative entropy, and thereby raises the spectre of the end of fiction (109–46).
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Minaieva, E. V. "Conceptological analysis of artistic space in novel „The Time Machine" by H. G. Wells (translated into Russian)." Bulletin of Luhansk Taras Shevchenko National University, no. 4 (335) (2020): 151–60. http://dx.doi.org/10.12958/2227-2844-2020-4(335)-151-160.
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The article discusses the features of modeling artistic space in novel The Time Machine, by H. G. Wells through a complex system of concepts top, bottom, fear, darkness, light. The constant interaction of these concepts leads to increased permeability of their boundaries, to a continuous exchange of conceptual features. The artistic space in the novel by H. G. Wells has a pronounced vertical character. We have identified the universal axis of top-bottom concepts in the artistic space and analyzed it. Movement along the vertical axis of the up and down concepts is carried out throughout the novel. In the novel The Time Machine, the features of the top concept become blurred, as they are overlaid with the features of the bottom concept. In the novel by H. G. Wells, the emotive concept of fear forms a fusion with the concepts of darkness and light. Fear unites different levels of the novel's artistic space. The binary concepts of darkness and light actively model the artistic space in novel The Time Machine. These concepts are closely related to the binary opposition of top-bottom concepts. In addition, they perform ontological, epistemological, axiological and aesthetic functions. The study of the features of artistic space in the novel The Time Machine opens up opportunities for further research of the artistic model of the world by H. G. Wells and the problems of modeling artistic space in the literature.
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Arteaga Martínez, Alejandro. "Un viaje hacia el pasado mexicano: Diego Cañedo, seguidor de H. G. Wells." Catedral Tomada. Revista de crítica literaria latinoamericana 9, no. 17 (January 10, 2022): 226–51. http://dx.doi.org/10.5195/ct/2021.480.
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Palamás, Echevete y yo o el lago asfaltado (Palamás, Echevete and I or the asphalted lake), Mexican Diego Cañedo’s second novel (1945), elaborates the time travel to the Mexican past. The sci-fi theme of the novel sustains a social criticism, and imitates H. G. Wells’ The Time Machine plot. In this essay, the sociocritical part of Cañedo’s work is studied, on one hand, because it seems to respond to the social problems of the period 1934-1946; and, on the other hand, because the relations established with Wells’ novel.
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Mohmad Aslam Najar. "The Time Machine: A Brief Review." Creative Launcher 4, no. 1 (April 30, 2019): 38–40. http://dx.doi.org/10.53032/tcl.2019.4.1.06.
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The father of science fiction novel, H. G. Wells (1866-1946) wrote ‘The Time Machine’ in 1895. The Novel highlights the disillusionment and disappointment of modern science. The tale claims of science to improve life seem hollow as inequality and oppression aggrandized with the progress of science. The horror of progress is shown by the morlocks- Eloi division in future. Dreams of socialism and communism turned into nightmares. Time traveller although travelled ahead but he sees regression and capitalist dominated world. Morlocks (working class) feed Eloi (capitalist class). They live subterranean and nocturnal where as Elio live upper ground superior and authoritarian.
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Guinter, Robert H. "The Time Machine." Pediatrics In Review 9, no. 6 (December 1, 1987): 171–72. http://dx.doi.org/10.1542/pir.9.6.171.
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Just before Christmas, my 12-year-old son was reading The Time Machine by H. G. Wells. According to the story, the driver of this machine could travel to any date in the past or future, and he could return to the present time whenever he wished. We enjoyed talking about the story and about how much fun it would be to own a time machine. Where would we go and what would we see? He decided, among other things, that we should see his grandparents as children, to see whether their way of life really was as different from his as they say it was. Then we should visit some famous persons from the past and witness some great event, such as a Civil War battle. Then we should look to the future. What will we be like in 10 or 20 years? What will our city or country be like in 100 or 1,000 years? What will our planet be like in 1 million years? We thoroughly enjoyed ourselves as we talked about owning and using such a machine.
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Valentine, Colton. "H. G. Wells and the Fin-de-Siècle Gustatory Paradox." Review of English Studies 71, no. 302 (February 14, 2020): 937–51. http://dx.doi.org/10.1093/res/hgaa002.
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Abstract Beginning with a little-studied scene linking H. G. Wells’s ‘A Misunderstood Artist’ to Joris-Karl Huysmans’s À Rebours, this essay argues that a shared gustatory paradox runs from Huysmanian decadence, through the theories of Edwin Lankester and Max Nordau and into Wells’s writings. In each case, both a pragmatic and an aesthetic relationship to food can signify degeneration. The argument has three major stakes. The first is to reconstruct a robust intertextual relation between the oeuvres of Huysmans and Wells. The second is to complicate readings that cast two of Wells’s scientific romances, The Time Machine and The War of the Worlds, as mouthpieces for imperialist or (pseudo)scientific discourses (Anger, Brantlinger, Budd, Gailor, Gregory, Hendershot, Pick). The third is to build on recent studies of food representation in nineteenth-century literature and propose a novel interpretive method (Cozzi, Gyman, Lee). Taking up William Greenslade’s proposal that fictions construct a ‘network of resistances’ to discursive myths, I argue that gustatory scenes show Wells’s ‘network’ operating in a curious way. They neither kowtow to degeneration nor assume Greenslade’s active role of a ‘critical, combative humanist’. Instead, they give contradictory depictions of moralized eating that play out the myth’s structural paradox.
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Fagan, Joshua. "Invitations to New Worlds: The Potential of Hospitality for William Morris and H. G. Wells." Victorians: A Journal of Culture and Literature 144, no. 1 (2023): 168–85. http://dx.doi.org/10.1353/vct.2023.a913514.
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ABSTRACT: The enhanced accessibility of rail and steamship travel for the rising middle-classes transformed Victorian tourism, replacing the privileged classes’ Grand Tour with a new conception of hospitality that defied conventional social mores. The writings of H. G. Wells and William Morris navigate the modern pursuit of intense, revitalizing sensations that create fresh conceptions of society, in the process critiquing modern Britain’s one-sided celebration of industrialization and consumerism. Both authors explore relations between guests and hosts, particularly a host’s duty to reveal the unfamiliar and unexpected to the guest while also encouraging reflection and sober contemplation. In News from Nowhere , Morris portrays ideal hosts who help guests understand the possibilities of a utopian world. Alternatively, Wells’ The Island of Doctor Moreau and The Time Machine demonstrate the instability and malaise of guests left to explore an unknown, dystopian world with no reliable host for guidance.
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Berchez, Amanda Naves. "A forma do porvir: literatura vitoriana, a máquina, as classes ou sobre a ficção científica de H. G. Wells en fin de siècle e um estudo sobre The time machine." Literartes 1, no. 17 (December 30, 2022): 51–77. http://dx.doi.org/10.11606/issn.2316-9826.literartes.2022.184251.
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O principal objetivo deste artigo é demostrar como o romance vitoriano The time machine de H. G. Wells reverbera, pelas óticas da ficção científica e distópica, questões (de ordem, sobretudo, social) intensamente exploradas no gênero romanesco industrial, também nomeado condition-of-England novels, ao exemplo das lutas de classes e da desumanização para com o proletariado no nicho industrial. Para tanto, recuperamos tanto histórica quanto teoricamente todos os referidos gêneros e formas literários.
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Roy, Malay. "The Future of Nature Prophesied in the Select Futuristic Science-fictions of H. G. Wells." International Journal of English Literature and Social Sciences 8, no. 3 (2023): 478–81. http://dx.doi.org/10.22161/ijels.83.73.
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Eco-criticism is one of the most recent interdisciplinary fields of study to have risen in the domain of literature which includes ecology, ecosystem and various other environmental issues relating to nature and its myriad aspects. Also, the discussions in the domain have increased significantly. For the last three decades, the environment has been facing a whole range of threats by the human-centric present day society. Also, the environmental consciousness in literature is a matter of vital concern for the scholars and the academic alike, and the subsequent emergence of the green theory, i.e. Ecocriticism is a remarkable addition. It is a field of study wherein nature and literature are mingled. As natural resources are being used injudiciously, the ecological environment is fast losing its poise and equilibrium. The cycle of seasons is fast turning irregular leaving the environment with a whole range of limitations and existential hazards for the entire human race. This crisis is not a recent phenomenon. Rather, it has been continuing since the Victorian age–the age of rapid industrialization, which consequently led to various disastrous phenomena, such as deforestation, water pollution, air pollution, soil pollution etc. Various scientific experimentations and developments have also brought harm to nature. The present paper attempts to show how H.G.Wells in his two novels, namely The Time Machine (1895) and The Island of Dr. Moreau (1896) prophesied the future of the environment in the light of the ideas laid down by the enterprise of science. The paper showcases how the characters of Wells forecast in their course of intersecting and interacting the two different natural worlds and play the role of a mediator between the two civilizations–one natural and the other reigned by science and the future. Through the literary analysis, the paper attempts to analyze not only the interrelationship shared between the human and the non-human worlds, but also, it lays bare the otherwise unwelcome outcomes of the man-nature interactions. In continuation of the analysis, the paper exhibits the demolition of the civilization as well as the environment through the film versions adapted by George Pal and David Duncan in 1960 and 2002 respectively under the same titles. In yet another novel titled The Island of Doctor Moreau(1896), Wells has shown how animals have been exploited for the sake of science and experimentation. The paper concludes how nature is confronting a great threat by the deliberate negligence of the human race, where there is a necessity to improve the correlation between human and nonhumans.
Dissertations / Theses on the topic "Time machine (Wells, H. G.)"
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Hanström, Sissel. "The Fear of the Fall: Degeneration and Social Inequality in the Frame Narrative of H. G. Wells’s The Time Machine." Thesis, Stockholms universitet, Engelska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-100264.
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H. G Wells’s novel The Time Machine is a significant work of science fiction that dramatizes the themes of degeneration and social inequality, themes that were very relevant during the Victorian era in relation to the discovery of evolution. Degeneration was seen as the degradation of society into primitiveness far from the Victorian standards, and the problem of social difference, where the gap between poor and rich was very wide, became the visible proof of the difference between the evolved and civilized and the degenerated and primitive. The purpose of this essay is to analyse how the frame narrative, the story surrounding the main adventure, affects the theme of degeneration in the novel. The framework reveals the reactions of the people present at the dinner parties, where the Time Traveller recounts his journey into a degenerated future. The guests are all representing different factions of Victorian society, such as the Provincial Mayor, the Very Young Man and the Editor who all have their own motives and agendas in relation to degeneration, social differences and time travel. By examining the guests’ individual motives, the essay argues that they do not want to believe in time travel since it would include believing in a degenerated future where all the glory of their present-day Victorian era would crumble into chaos and pandemonium. This essay shows that by denying the relevance of the Time Traveller’s story, despite the evidence presented, the dinner guests are condemning themselves to the degenerated future they are afraid of, hence making the novel a warning example of not accepting new ideas.
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Dutra, Daniel Iturvides. "Literatura de ficção-científica no cinema : a transposição para a mídia fílmica de A Máquina do Tempo de H. G. Wells." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/21566.
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Esta dissertação realiza uma análise do livro A Máquina do Tempo, escrito por H.G. Wells em 1895, e sua transposição fílmica homônima realizada por George Pal em 1960 dentro do escopo do gênero ficção-científica. São analisadas as mudanças que foram realizadas no processo de transposição da literatura para o cinema. Este trabalho discute inicialmente as características do gênero ficção-científica na literatura, como o gênero se diferencia de outros gêneros como a literatura fantástica, por exemplo, e quais são os elementos que compõem a identidade do gênero ficção-científica. Em um segundo momento investigamos os problemas que o gênero ficção-científica apresenta quando transposto a mídia cinematográfica. Entre os fatores investigados estão como a verossimilhança funciona na literatura de ficção-científica e como o leitor se relaciona com ela. Também se analisou como o gênero utiliza o conhecimento científico para criar seus universos ficcionais, e as mudanças que os realizadores fílmicos às vezes precisam fazer para tornar o universo literário ficcional aceitável ao espectador em termos de verossimilhança, considerando que o que é verossímil na literatura não é necessariamente verossímil no cinema, especialmente na literatura de ficção-científica, um gênero que possui uma ligação íntima com a ciência e o progresso. Para finalizar essa pesquisa analisa a relação entre o gênero ficção-científica e o cinema no que diz respeito aos problemas que o gênero apresenta ao cinema em termos de tecnologia. Em outras palavras, o gênero literário ficção-científica traz aos realizadores fílmicos problemas e desafios que exigem a pesquisa e o desenvolvimento de novas tecnologias e técnicas visando resolvê-los.The present work analyzes the book The Time Machine, written by H.G.Wells in 1895, as well as its transposition into a homonymous movie made by George Pal in 1960 as part of the science fiction genre. This work initially discusses the characteristics of the science fiction genre in literature, the way the genre differs from others such as the fantastic literature, for instance, and the fundamental elements of the science fiction genre. It then investigates the problems the science fiction genre presents when transposed to film media. Among the investigated factors are the problem of verisimilitude in science fiction literature and how the reader responds to it. This includes discussing the way the genre uses scientific knowledge to create its fictional universe, and the changes, in terms of verisimilitude, moviemakers need to consider when the fictional literary text is transferred into film. What sometimes is plausible in literature not always seems plausible in movies, especially in regard to science fiction literature, a genre that has a intimacy with science and progress. To conclude, this research analyzes the relation between the science fiction genre and film concerning the challenges the genre presents when transposed into another medium in terms of technology. These challenges require research and the development of new procedures for the cinematographic medium.
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Tombs, George 1956. "Man the machine : a history of a metaphor from Leonardo da Vinci to H. G. Wells." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84203.
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During the Italian Renaissance, artists and anatomists compared man to various mechanical devices, in an attempt to uncover knowledge about the structure and processes of the human body. In so doing, they drew on ancient Greek notions of instrumentality and proportion. During the early Scientific Revolution, the metaphor of Man the machine played a key role in the development of mechanistic philosophy. During the Enlightenment, it served views on materialism and atheism. By the nineteenth century, when the Industrial Revolution was in full swing, a fundamental change in the relationship of man to machine had come about. Whereas, for Protagoras, man had been the measure of all things, now suddenly the machine was the standard by which the capacities and limits of man were judged. Man the machine was a key feature in the development of the totalitarian ideology of Communism. Moreover, for over a century now, the technocratic viewpoint has guided many technological innovations. Tracing a history of this metaphor, through Leonardo, Vesalius, Harvey, Descartes, Hobbes, Leibniz, La Mettrie, d'Holbach, Marx and Wells, places man's relationship with technology and his gradual loss of identity since the Renaissance in a new context.
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Chen, Yung-Hao, and 陳永豪. "Mapping Time in Digital Humanities: The Multi-Layered Message Embedded in H. G. Wells’s The Time Machine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/cx37j4.
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碩士靜宜大學
英國語文學系
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The thesis examines the messages under a multi-layered one that can be extracted from Wells’s The Time Machine by establishing a link between literature and science. The so-called link can be created by looking into the role that the time machine—a piece of technology—plays in the novel. That piece of technology thus opens up a conversation between literature and science. The discussion is then made possible by applying Herbert Marcuse’s theory, “one-dimensionality,” in his One-Dimensional Man: Studies in the Ideology of Advanced Industrial Society. Coming along with Marcuse’s theory are discussions on digital humanities and various perspectives of time. Moreover, other literary works by T. S. Eliot, Edward Bellamy, and Mark Twain are also called upon and discussed. Finally, when revisiting Marcuse’s one-dimension, one can see that by reading and imagining the code in literature, one is bound to see the hidden messages presented and discussed. The messages not only show that advanced technology does not necessarily guarantee a better future for the mankind but also that why literature has its foundation to preserve an individual’s imagination and the probability of the future even in a world leaning toward a one-dimensional one under the influence of digital products, a form of technology and science.
Books on the topic "Time machine (Wells, H. G.)"
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R, Hammond J. H. G. Wells' The time machine: A reference guide. Westport, Connecticut: Praeger, 2004.
Find full textBook chapters on the topic "Time machine (Wells, H. G.)"
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Gymnich, Marion. "Wells, H. G.: The Time Machine." In Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_17338-1.
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Hammond, J. R. "The Time Machine: the Riddle of the Sphinx." In H. G. Wells and the Modern Novel, 73–84. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-08655-9_5.
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McLean, Steven. "Heart of Darkness: The Time Machine and Retrogression." In The Early Fiction of H. G. Wells, 11–40. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230236639_2.
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Pintér, Károly. "An Epistemological Journey: The Uncertainty of Construed Realities in The Time Machine." In Utopias and Dystopias in the Fiction of H. G. Wells and William Morris, 157–72. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-52340-2_10.
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Hammond, J. R. "Experiments with Time." In H. G. Wells and the Modern Novel, 58–70. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-08655-9_4.
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Dryden, Linda. "Martians, Sleepers, Time Travellers and Hearts of Darkness." In Joseph Conrad and H. G. Wells, 10–38. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137500120_2.
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Viegnes, Michel. "Writing the History of the Future. Epochs and Time Frames in Golden Age Science Fiction (H. G. Wells, W. Miller, C. Simak and I. Asimov)." In Ausdehnung der Zeit, 143–56. Köln: Böhlau Verlag, 2019. http://dx.doi.org/10.7788/9783412513184.143.
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"The romances of the 1890s: The Time Machine, The Island of Dr Moreau, The War of the World." In H. G. Wells, 1–31. Cambridge University Press, 1985. http://dx.doi.org/10.1017/cbo9780511553646.002.
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"H. G. Wells: The Time Machine." In The Collector's Voice, 238–44. Routledge, 2016. http://dx.doi.org/10.4324/9781315264448-49.
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"31. H. G. Wells, The Time Machine (1895)." In Handbook of the English Novel, 1830–1900, 547–64. De Gruyter, 2020. http://dx.doi.org/10.1515/9783110376715-032.
Full textConference papers on the topic "Time machine (Wells, H. G.)"
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Abraham, J. J., C. Devers, C. Teodoriu, and M. Amani. "Scaling Field and Experimental Data Using Machine Learning Approaches to Evaluate Oilwell Cement Degradation, Stability and Integrity for CCUS Applications." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219115-ms.
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Abstract Carbon Capture, Utilization and Storage (CCUS) processes are increasingly being utilized as a viable solution for carbon removal and meet the goal of net-zero carbon emissions by 2050. Captured carbon dioxide (CO2) is stored deep underground – typically in depleted oil or gas (O&G) wells - utilizing technologies and methods currently employed by the energy industry. However, there are certain ongoing well integrity challenges that would need to be addressed – especially those relating to the cement layer. Cement present in wells used for CCUS applications – including old or abandoned wells - need to ensure zonal isolation, be resistant to deterioration, corrosion, or gas migration, as well as be suited for adverse downhole conditions. Oilwell cement present in existing or abandoned O&G assets have been exposed to a wide range of downhole conditions throughout their lifecycle. It is generally very difficult to determine the mechanical properties and physical condition of the cement downhole and a decline in these properties is expected over time. Experimental evaluations have shown that temperature plays a role in the setting and maturity of the cement, and in CCUS wells, corrosive factors are a major concern due to the acidic environment produced at the CO2 injection zone. These can significantly affect cement mechanical properties such as the Uniaxial Compressive Strength (UCS). Evaluations have shown Temperature or Acoustic Logs can be used to determine downhole properties which can then be correlated to the behavior of cements and the change in their mechanical properties over time using machine learning algorithms. Laboratory evaluations showed varying mechanical properties for oilwell cement at different temperatures and degradation over time. Overall, Class G cements developed the highest stress failure resistance, followed by Class H cements. Higher temperatures accelerated the setting time of all cement samples due to rapid dehydration. However, this in turn reduced the peak UCS developed, indicating a lower stress failure criterion. UCS also showed a direct relationship to acoustic data which can be utilized to evaluate mature and abandoned wells for their integrity. When modeled using supervised machine learning algorithms, field temperature data and acoustic data can reliably predict the mechanical properties of cements over time. An artificial neural network model, and two tree based models were developed, which showed good correlation in predicting compressive strength of downhole cements. Properly understanding the behavior of oilwell cement and the evolution of their mechanical properties is critical to ensure safe storage. Data driven algorithms which can correlate the dynamic mechanical properties of cement to the temperature gradient and acoustic logs can help reliability predict the integrity of the cement layer over time especially for CCUS applications.
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Al-Riyami, N., O. Revheim, T. S. Robinson, P. Batruny, M. H. Meor Hakeem, and G. Tze Ping. "Drilling in the Digital Age: Case Studies of Field Testing a Real-Time ROP Optimization System Using Machine Learning." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214521-ms.
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Abstract O&G operators seek to reduce CAPEX by reducing unit development costs. In drilling operations this is achieved by reducing flat time and bit-on-bottom time. For the last five years, we have leveraged data generated by drilling operations and machine learning advancements in drilling operations. This work is focused on field test results using a real-time global Rate of Penetration (ROP) optimization solution, reducing lost time from sub-optimal ROPs. These tests were conducted on offshore drilling operations in West Africa and Malaysia, where live recommendations provided by the optimization software were implemented by the rig crews in order to test real-world efficacy for improving ROP. The test wells included near-vertical and highly deviated sections, as well as various formations, including claystones, sandstones, limestones and siltstones. The optimization system consisted of a model for estimating ROP, and an optimizer algorithm for generating drilling parameter values that maximize expected ROP, subject to constraints. The ROP estimation model was a deep neural network, using only surface parameters as inputs, and designed to maximize generalizability to new wells. The model was used out-of-the-box, with no specific retraining for the field testing. During field-tests, increased average ROP was observed after following recommendations provided by the optimizer. Compared to offset wells, higher average ROP values were recorded. Furthermore, drilling was completed ahead of plan in both cases. In the Malaysian test well, following the software's advice yielded an increase in ROP from 10.4 to 31 m/h over a 136 m drilling interval. In the West Africa well, total depth was reached ∼24 days ahead of plan, and ∼2.4 days ahead of the expected technical limit. Importantly, the optimization system provided value in operations where auto-driller technologies were used. This work showcases field-test results and lessons learnt from using machine learning to optimize ROP in drilling operations. The final plug-and-play model improves cycle efficiency by eliminating model training before each well and allows instantaneous, real-time intervention. This deployable model is suitable to be utilized anytime, anywhere, with retraining being optional. As a result, minimizing the invisible lost time from sub-optimal ROP and reducing costs associated with on-bottom drilling for any well complexity and in any location is now part of the standard real-time operation solutions. This deployment of technology shows how further optimization of drilling time and reduction in well cost is achievable through utilization of real time data and machine learning.
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Zhang, Junbo, and Liang Zhao. "Study on the Drilling Performance Improvement and Risk Reduction with 3D Geomechanical Modelling in Horizontal Wells." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214614-ms.
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Abstract The deep shale gas reservoir are high formation temperature and pore pressure in Sichuan Basin. Due to the unclear geomechanical characteristics of the reservoir, a large number of accidents occurred during the drilling operation. At the same time, the wellbore instability and frequent adjustment trajectory cause long drilling cycle, low drilling efficiency, and high drilling operation cost. To solve the above problems, the drilling mud weight is optimized based on the three-dimensional geomechanical research and by establishing the pore pressure, collapse pressure and fracture pressure (leakage pressure) models. The key technology of reducing drilling mud weight are used to significantly reduce the drilling mud loss. Field application shows that the mud weight is reduced from 2.15 g/cm3 to 1.87 g/cm3, the average ROP increased by 44.1% from 8.4 m/h to 12.1 m/h, the average drilling operation cycle decreased by 40.7% from 54.2 days to 32.1 days, and the drilling performance and efficiency are significantly improved. The fine 3D geomechanical modeling technology has great promotion and reference significance for the performance and efficiency improvement of the deep shale gas horizontal well drilling operation in China.
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Herbet, R. "Machine Learning Technology to Improve Precision and Accelerate Screening Shallow Gas Potentials in Tunu Shallow Gas Zone, PT Pertamina Hulu Mahakam." In Indonesian Petroleum Association 44th Annual Convention and Exhibition. Indonesian Petroleum Association, 2021. http://dx.doi.org/10.29118/ipa21-g-220.
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Tunu is a giant gas field located in the present-day Mahakam Delta, East Kalimantan, Indonesia. Tunu gas produced from Tunu Main Zone (TMZ), between 2500-4500 m TVDSS and Tunu Shallow Zone (TSZ) located on depth 600 - 1500 m TVDSS. Gas reservoirs are scattered along the Tunu Field and corresponds with fluio-deltaic series. Main lithologies are shale, sand, and coal layers. Shallow gas trapping system is a combination of stratigraphic features, and geological structures. The TSZ development relies heavily on the use seismic to assess and identify gas sand reservoirs as drilling targets. The main challenge for conventional use of seismic is differentiating the gas sands from the coal layers. Gas sands are identified by an established seismic workflow that comprises of four different analysis on pre-stack and angle stacks, CDP gathers, amplitude versus angle(AVA), and inversion/litho-seismic cube. This workflow has a high success rate in identifying gas, but requires a lot of time to assess the prospect. The challenge is to assess more than 20,000 shallow objects in TSZ, it is important to have a faster and more efficient workflow to speed up the development phase. The aim of this study is to evaluate the robustness of machine learning to quantify seismic objects/geobodies to be gas reservoirs. We tested various machine learning methods to fit learn geological Tunu characteristic to the seismic data. The training result shows that a gas sand geobody can be predicted using combination of AVA gather, sub-stacks and seismic attributes with model precision of 80%. Two blind wells tests showed precision more than 95% while other final set tests are under evaluated. Detectability here is the ability of machine learning to predicted the actual gas reservoir as compared to the number of gas reservoirs found in that particular wells test. Outcome from this study is expected to accelerate gas assessment workflow in the near future using the machine learning probability cube, with more optimized and quantitative workflow by showing its predictive value in each anomaly.
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Figueirêdo, Ilan Sousa, Tássio Farias Carvalho, Wenisten José Dantas Silva, Lílian Lefol Nani Guarieiro, and Erick Giovani Sperandio Nascimento. "Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31297-ms.
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Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.
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Sahidu, M. R. H. "Enhancing Karstified Carbonate Characterization Through Focused Seismic Reprocessing and Machine Learning Utilization in Ubadari Field." In Indonesian Petroleum Association - 46th Annual Convention & Exhibition 2022. Indonesian Petroleum Association, 2022. http://dx.doi.org/10.29118/ipa22-g-154.
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Ubadari is an undeveloped gas field in Bintuni Bay (West Papua, Indonesia) about 70km west of the Tangguh LNG plant. Seismic reprocessing (2015 and 2018) and appraisal well results (2019) elevated the field from option to the main project of Tangguh Expansion phase 2. The development plan will require several development wells from one or two platforms and a dedicated pipeline to supply gas to the 3rd Tangguh LNG train. The previous seismic reprocessing projects in Ubadari were focused on improving the reservoir image and the imaging in the overlying karstified carbonate remained suboptimal. Given the magnitude of drilling problems associated with this karstified Faumai formation and the role of seismic data in identifying the karst features (Birt, 2015; Riangguna, 2016; Birt, 2019), a seismic reprocessing with the focus on the overburden was required. bp completed the latest Ubadari seismic reprocessing in 2021 to identify the overburden drilling hazards that may impact the platform numbers and location. The seismic reprocessing resulted in step improvements of the overburden imaging although it was still highly masked by the karsts. Therefore, seismic attributes using the rapid interpretation Machine Learning-based tools were utilized to remove interpretation biases and improve the confidence level. The major faults in the carbonate can now be extracted and identified as having interaction with the older fault system on the reservoir level. The faults also formed a long span of collapse system on the very shallow carbonate section. The typical karst system in Tangguh (known as discrete circular bright troughs amplitude features) was also identified along or near the faults. This robust overburden characterization and drilling hazards assessment were delivered on time to be part of key consideration in the screening and optimization of the Ubadari future platform location for the TEP phase 2 project.
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Abraham, J. J., A. Carvero, C. Devers, C. Teodoriu, and M. Amani. "Stress Failure Assessment and Potential Well Integrity Issues with Different Oilwell Cement Classes and Formulations in Complex Wells." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219117-ms.
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Abstract Complex operations such as fracturing, and stimulations have become a mainstay in most drilling and completion operations around the world. Safe technologies have been adopted by the industry to mitigate issues in complex wells, HPHT conditions and difficult formations. However, well integrity problems - especially in the cement layer - are still a major concern in a lot of cases when performing workover, fracturing or re-completion operations in existing or older abandoned wells. Oilwell cement used in drilling and completion comes in several different classes and grades. Geopolymer based cements are also increasingly being considered for cementing operations, owing to their green credentials. Commonly used API Class C, Class H and Class G cements as wells as Geopolymers all have mechanical properties which vary widely, and a decline in these properties are expected after exposure to different downhole conditions over time. Experimental evaluations were performed to measure mechanical properties such as the Uniaxial Compressive Strength (UCS) and acoustic velocities and determine how they vary over time and under different physical environments. Finite element stress modeling was then performed to determine failure mechanisms in downhole conditions. Degradation of the cement layer due to ageing, as well as exposure to different downhole temperatures especially in the cement-casing interface are of particular interest. Each of the classes of oilwell cements perform differently and thereby have a different impact on the overall integrity of the well. Results from laboratory testing of samples showed significantly different mechanical properties during the mixing, setting and ageing periods for different oilwell cement classes and at different temperatures. Among the different formulations tested, Class G cement showed the highest failure stress with almost all samples showing a consistent peak UCS growth, before stabilizing. Class C cements and Geopolymers had the lowest stress failure resistance, indicating their unsuitability for HPHT operations. Higher temperatures accelerated the setting time, though reduced the UCS for all classes of cement. When stresses experienced during typical fracturing operations were modeled in a downhole scenario with these cements, propagating failure points were observed. Stresses can migrate and concentrate at different points - which in some cases can exceed the failure criteria of these cements leading to the formation of cracks. These can in turn cause integrity issues in the cement sheath and possibly a critical well integrity situation. Robust testing of oilwell cements and geopolymers is needed to properly understand their properties, as well as the development of stress failure points around the wellbore. Identifying potential well integrity issues for various cement formulations can in turn help in improving the quality and reliability of cementing operations, reduce the risks associated and ensure safe operations over the lifespan of a well.
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Al Dandan, Esam, and Md Mofazzal Hossain. "Understanding of Geochemical Reactions in Hydrogen-Injected Wells: Cement Integrity for Safe Underground Hydrogen Storage." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23620-ms.
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Abstract As the world transitions to clean energy sources, Underground Hydrogen Storage (UHS) has emerged as a leading solution for large-scale hydrogen storage. While the depleted oil or gas reservoirs are ideal for UHS, the effect of geochemical reactions among injected hydrogen, wellbore, and cement is not documented. This study aims to assess cement and well integrity by examining the geochemical interaction between API cement and hydrogen near the wellbore under varying temperature and pressure conditions. The numerical simulation was carried out to study the geochemical reaction between hydrogen and API class G/H cement minerals using the PHREEQC version 3 simulator. The dissolution reactions of hydrogen with the initial cement components, namely calcium tetra calcium alumino-ferrite (C4AF), tricalcium aluminate (C3A), tricalcium silicate (C3S), and dicalcium silicate (C2S) were modelled at various pressure and temperature conditions. The simulation assumed continuous cement hydration over an infinite time to assess the long-term effects of hydrogen-cement interactions and its impact on cement integrity near the wellbore. Based on this numerical simulation, we found that at 56.2oC, the formation of calcium silicate hydrate(CSH), portlandite, C3AH6, Mackinawite, magnetite, and hydrotalcite. At 95°C, similar minerals were formed with slightly higher amounts of CSH and slightly less portlandite, while others did not exhibit a noticeable difference. At 119°C, it was observed that a noticeable increase in CSH and a noticeable reduction in portlandite amount. Additionally, the formation of ettringite was observed at elevated temperatures. These findings highlight the temperature- dependent changes in mineral composition near the wellbore, which may have implications for the long-term integrity of the cement matrix in hydrogen-affected environments. Based on comprehensive numerical simulation studies, this paper highlights critical insights for a better understanding of hydrogen-cement interactions in the context of underground hydrogen storage, and its impact on the long-term-integrity of wellbores in hydrogen storage application, essential for enhancing the knowledge base for safe and effective implementation of underground hydrogen storage technologies.
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Gowida, A., and S. Elkatatny. "Novel Physics-Informed Machine Learning Approach to Estimate Safe Mud Window Using Petrophysical Logging Data." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23895-ea.
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Abstract The allowable limits of mud weights for drilling O&G wells, known as the safe mud window (SMW), play a crucial role in preventing wellbore instability issues and loss of circulation. The SMW consists of the minimum mud weight for shear failure (MWBO) and the maximum mud weight for tensile failure (MWBD), which are determined by the principal stresses of the formation, including the maximum (Shmax) and minimum (Shmin) horizontal stresses. Measuring these stresses accurately can be achieved through field tests or approximated using physics-based equations. However, obtaining the necessary in-situ geomechanical parameters for these equations, such as static Poisson's ratio and static elastic modulus, is not always feasible for all wells. Furthermore, the existing machine learning models rely on expensive and destructive tests. To address these challenges, this study investigated the feasibility of utilizing machine learning (ML) algorithms to predict these parameters in a time- and cost-effective manner. New ML-based models employing artificial neural networks (ANN) were developed to predict the SMW limits (MWBO and MWBD) using petrophysical well-log data as inputs. A comprehensive dataset consisting of field test data and petrophysical logging data was collected and extensively analyzed to train the models. The predictions generated by the developed ANN-based models exhibited a high degree of accuracy, with a mean absolute average error (MAPE) of less than 0.30% when compared to the actual output values. Thes developed models were validated using an unseen dataset, demonstrating remarkable agreement with the actual stress gradient and SMW limit values. The prediction accuracy exceeded 95%, and the MAPE was as low as 0.59%. The statistical analysis of the results confirmed the robustness of the developed equations in accurately predicting the SMW limits, provided that the logging data are available. The originality of this research lies in its ability to efficiently and affordably predict the safe mud window (SMW), thereby mitigating drilling problems such as borehole instability. The developed models provide a reliable tool for accurately determining the SMW, surpassing the conventional methods that are more time-consuming and costly.
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Karnik, Saniya, Supriya Gupta, and Jason Baihly. "Machine Intelligence for Integrated Workover Operations." In SPE/ICoTA Well Intervention Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204423-ms.
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Abstract Because of recent advancements in the field of natural language processing (NLP) and machine learning, there is potential to ingest decades of field history and heterogeneous production records. This paper proposes an analytics workflow that leverages artificial intelligence to process thousands of historical workover reports (handwritten and electronic), extract important information, learn patterns in production activity, and train machines to quantify workover impact and derive best practices for field operations. Natural language processing libraries were developed to ingest and catalog gigabytes of field data, identify rich sources of workover information, and extract workover and cost information from unstructured reports. A machine learning (ML) model was developed and trained to predict well intervention categories based on free text describing workovers found in reports. This ML model learnt pattern and context of repeating words pertaining to a workover type (e.g. Artificial Lift, Well Integrity, etc.) and to classify reports accordingly. Statistical models were built to determine return on investment from workovers and rank them based on production improvement and payout time. Today, 80% of an oilfield expert's time can be spent manually organizing data. When processing decades of historical oilfield production data spread across both structured (production timeseries) and unstructured records (e.g., workover reports), experts often face two major challenges: 1) How to rapidly analyze field data with thousands of historical records. 2) How to use the rich historical information to generate effective insights to optimize production. In this paper, we analyzed multiple field datasets in a heterogeneous file environment with 20 different file formats (PDF, Excel, and other formats), 2,000+ files and production history spanning 50+ years across and 2000+ producing wells. Libraries were developed to extract workover files from complex folder hierarchies through an intelligent automated search. Information from reports was extracted through Python libraries and optical character recognition technology to build master data source with production history, workover, and cost information. A neural network model was trained to predict workover class for each report with >85% accuracy. The rich dataset was then used to analyze episodic workover activity by well and compute key performance indicators (KPIs) to identify well candidates for production enhancement. The building blocks included quantifying production upside and calculating return of investment for various workover classes. O&G companies have vast volumes of unstructured data and use less than 1% of it to uncover meaningful insights about field operations. Our workflow describes methodology to ingest both structured and unstructured documents, capture knowledge, quantify production upside, understand capital spending, and learn best practices in workover operations through an automated process. This process helps optimize forward operating expense (OPEX) plan with focus on cost reduction and shortens turnaround time for decision making.