10.6092/issn.2038-3460/18595
application/pdf
2038-3460
18595
2
en
Barbara Lazarotto
https://creativecommons.org/licenses/by/4.0
Tecnoscienza – Italian Journal of Science & Technology Studies
Eric Monteiro, Digital Oil: Machineries of Knowing, The MIT Press, 2022
Text.Serial.Journal
https://tecnoscienza.unibo.it/
14
Book Reviews
2024-01-16
2023-12-06
2024-01-16
2024-01-16
133-137
TS
Barbara Lazarotto
2023
10.6092/issn.2038-3460/18595
133
Vrije Universiteit Brussel
2038-3460
2
en
137
Eric Monteiro, Digital Oil: Machineries of Knowing, The MIT Press, 2022
https://tecnoscienza.unibo.it/article/download/18595/17032
14
T/S                                                                                  BOOK REVIEWS



Digital Oil: Machineries of Knowing
by Eric Monteiro (2022) Cambridge (MA), The MIT Press, pp. 215.




Barbara Lazarotto
Vrije Universiteit Brussel


   In the last decade, “data is the new oil” was a metaphor commonly used to demonstrate the
economic significance of data. Labeling data as “the new oil”, was a way of expressing how
data is now considered to be the most valuable asset, just like petrol once was. Empires were
built over it, and tech companies are now more valuable than petrol companies once were,
as demonstrated by the European Parliament briefing “Is data the new oil?” (Marcin Szcze-
pański 2020). Eric Monteiro draws on this metaphor, exploring the digital transformations
in offshore oil and gas work practices, and delving into obstacles faced by digitalization. What
is distinctive about Monteiro’s approach is the use of the metaphor “data is the new oil”, to
analyze the Norwegian offshore oil industry – which has been through an intense process of
digitalization – as a backdrop to discuss datafication in a broader landscape.
   The author, who is located at the Norwegian University of Science and Technology in Trond-
heim, chose a relevant topic in Norway – the industry of commercial oil and gas – to explore
practices, roles, and organizational decisions in the field throughout the evolution of technol-
ogy, highlighting how societal and political decisions have shaped the process. The goal of the
author is not to feed the divide between early or predigital practices of offshore oil exploration
and recent forms of digitalization but to advocate that the adoption of technology is part of an
evolutionary, small-step movement and that digital representations are as real as the physical in
the material of knowledge and that oil and digital oil is a suitable example of this affirmation.
   Digital Oil: Machineries of Knowing is divided into three parts, in which the eight chapters of
the book explore all the phases of commercial oil activities, taking place as follows. In Chapter 1,
the Introduction, the author centers the narrative of the book on the context of the Norwegian
continental shelf with the North Sea and the Barents Sea, where offshore gas and oil reservoirs
reside over one to five kilometers below the seabed. The chapter offers the reader a historical
outline of the political and institutional process that has shaped Norway’s oil and gas industry.
   The first part of the book, Chapters 2 and 3, work as the backdrop of the subsequent parts,
outlining the historical conditions of Norway’s fifty years of oil exploration, and the further
digitalization of oil, a process that was initiated around the 1980s and early 1990s and that has
significantly transformed since then. Throughout Chapter 2, named Context, it is possible to
observe how the political choice of recognizing oil resources as a public good and the impor-

CNOSIEZA.T tI alian Journal of eScin & echnoT o
                                              l gy tS udies         © The Author(s) 2023
          14(2) p. 137- ISN 2038-46                                 Attribution 4.0 International (CC BY 4.0)
               DOI: /18590.62isn3-4                                 www.tecnoscienza.net
Lazarotto                                                                                       134

tance of learning by doing was essential to place Norway as one of the leaders of commercial
oil and gas activities. Initially, the political choice was to maintain control over oil handling to
national companies and to gradually open the market to international competition, a process
that allowed Norwegian companies to be competitive with major international companies
but also control not only its oil but also the data later extracted from it. Demonstrating how
the process of digitalization and generation of data is highly influenced by technical, econom-
ic, ethical, and political factors, as highlighted by Rob Kitchin (2014b; 2021).
   Chapter 3, Apparatus, focuses on the different technological developments and adapta-
tions across all phases of Norwegian oil activities. Throughout the chapter, the author en-
lightens that each phase of oil activity – such as exploration, drilling, production, and logging
– is measured by different instruments, with different purposes, generating different data
with different quality. The volume of data is considerable, which also results in several issues
such as the veracity of data that might be under question due to noise and the calibration
of sensors. This process of constant adaptation to collect data despite the adversities posed
by the specific phase of oil drilling is the perfect example of the point made by Rob Kitchin
(2014b). Data is not a single homogeneous concept, instead, data is partial, selective, and
aimed at drawing conclusions from a specific environment, which in this case corresponds
to each phase of the oil drilling activity. At the same time, the example brought by Monteiro
fits to explain common issues faced by many other areas that depend on data collection, since
its concerns with data quality, the volume of data, and the need for constant adaptations are
present. Thus, although data is not a single homogeneous concept, the obstacles faced by the
Norwegian oil industry when collecting oil data are also faced in data collection in other areas,
bridging the gap between the metaphor “data is the new oil” and the “oil data”.
   In the second part of the book, Monteiro focuses on empirical studies which mirror the
phases of commercial oil activities. Initiated with Chapter 4 titled Data, the author explores
the process of “cooking data” – as coined by Rob Kitchin (2014a, 5) – or “crafting data” (as
Monteiro refers on p. 75), through algorithmic cleaning and repairing. To do so, Monteiro
delves into the work of “data managers” who are multidisciplinary professionals who work at
different organizational units of Norwegian Oil production and are responsible for grasping
different datasets of historical geodata with different formats, patching them together. Their
work holds value due to their knowledge of going through data noises – such as seismic data
– and knowing what data to trust and to disregard, an essential task for the corporate quality
system. The role of data managers sheds light on the process of datafication described by Jose
van Dijck (2014), which is characterized by the process of quantification and digitalization
of all human activity. While data managers are essential to the corporate quality system due
to their knowledge of historical seismic data, their jobs are vulnerable due to the potential for
automation, a process which is again a metaphor for the current process of datafication that
occurs outside the world of oil mining and embraces many professions.
   Chapter 5, named Uncertainty, focuses on the work of geoscientists and geophysics along
oil exploration, which is an essential and strategic phase for global oil operators that is full of
uncertainties with a hit-rate of 5%, (p. 89) but representing 10 to 20% of total investments for
an upstream oil operator. The task of digitalizing a highly uncertain activity seems contradic-
tory in a world that often takes data as the ultimate source of truth and makes decisions based
135                                                                         Tecnoscienza. 2023. 14(2)

on them. However, as Rob Kitchin has pointed out, data analysis must take into considera-
tion the issues of representativeness, error, bias, and uncertainty that come (2014b). Thus, in
reality, data collection in all sectors requires attention due to technical and ethical consider-
ations often ignored, and therefore causing bias, injustices, violation of rights, and unethical
behaviors. And just like the work of “explorationists” – a concept created by Monteiro to
refer to geologists and geophysicists that feed the data that data managers will eventually ana-
lyze – must balance these considerations with corporate necessities, other professionals in
other areas also face the same dilemma, with often deeper consequences such as dataveillance
as pointed out by Jose van Dijck (2014).
   Chapter 6, titled Knowing, inaugurates the section of the book that covers the develop-
ment of oil production and addresses the issues coming with the process, especially sand.
Sand monitoring is an essential process in offshore gas and oil explorations since it reduces the
plant’s processing capacities and oil quality. At the same time, sand is used by Monteiro to dis-
cuss other types of “data dirt” – lack of quality data – present in other areas of data collection
and discuss how operators know and act upon these challenges and what mitigating actions
are taken. To make this analysis, the author conducted interviews in which he observed how
engineers navigate and interpret “real sand” – the dirt that comes out of the ocean’s soil – and
“digital sand” – errors that real sand causes to the IoT sensors and to the data their collect. He
highlights that although real sand can be digitally represented and sand-monitoring routines
are put in place to address related issues, this digital representation carries little value if there
is no accompanying company infrastructure, practices, and technologies. This analysis brings
back the discussion raised by Rob Kitchin (2014b) related to the ethical and technical issues
with data. Issues such as bias, injustices, and violations of rights can be digitally represented
in datasets, yet if the ones that are using the collected data do not adapt their infrastructure,
practices, and technologies to address these issues, this digital representation holds little value.
   The following Chapter 7, Politics, explores the political discussion behind oil exploration,
which was responsible for financing the Norwegian welfare state but also its repercussions
on data control and collection in the country. This section ties back with Chapters 2 and 3,
which outlined the political process of oil exploration in Norway to explore how digitaliza-
tion – of oil but also of any other world aspect – is complex and nuanced with political choic-
es which might affect the quality of data and who has access to it. In Norway, the political
process of oil exploration and later digitalization was essential to make the oil data a public
good and finance the Norwegian welfare. In other countries, such as the United States, the
process of privatization of oil drilling was essential to put data in the control of the private sec-
tor and eventually further the divide between the private and the public sector. Through this
analysis, the author connects directly with Rob Kitchin (2014b) and Lisa Gitelman (2013)
who argue that data is not simply data, but instead, a process that is conceived and nuanced
depending on those who capture, analyze, and draw conclusions from them, and that data is
not “raw”, but a cultural resource that is socially interpreted and shaped.
   In this context of social interpretation and shaping of data, the author explores datafication of
the marine environment through the Venus Project, a project that started in 2005 aiming to test
the viability of using IoT in the ocean. While the project did not get much attention initially, po-
litical processes and the need for further exploration of oil reserves located in the ocean have raised
Lazarotto                                                                                        136

attention to it, demonstrating once again how political and financial motivations often influence
the collection and interpretation of data. The hard task of quantification of the marine environ-
ment, as well as other phenomena such as climate change, has the effect of reducing nuances
into metrics, “transforming quality into quantity” (p. 164), eventually influencing the methods
of environmental classification to ones that favor a specific political or philosophical thought.
Therefore, the environmental complexity of the ocean is transformed into numbers through da-
tafication, and once parameters are put in place, numbers become more acceptable than reality,
allowing the exploration of ocean zones that before were excluded from oil exploration.
   The third and last section of the book is named Implications and consists of the Conclusion
in Chapter 8. Here, the author elaborates and synthetizes the previous sections, exploring
the implications of digitalization moving from oil to the process of datafication of society.
Overall, Monteiro takes the readers on a journey that initiates from a very specific context of
the Norwegian oil exploration, and its process of datafication but expands to explore how the
same obstacles are faced in multiple scenarios that are going through datafication. Taking a
provocative route, the author accomplishes the task of demonstrating the interconnections
between the tangible and the intangible worlds by emphasizing how organizational and insti-
tutional aspects shape how data is collected and processed. Through this process, Monteiro
demonstrates how data is a dynamic concept, and how this changeable characteristic impacts
how it is perceived and how its results will impact the world.
   Hence, the merit of this book, in addition to allowing to learn more about the process of
data oil, lies in enabling the comprehension of how the social organization and the institu-
tional fabric shape technological advancements and the consequent expansion of datafica-
tion. Through the use of the oil industry as a backdrop, readers can appreciate the intercon-
nections between the “data world” and the “real world” and comprehend how they interact
and influence each other. Thus, the interdependent relationship between society, science,
and technology is a source of inspiration for STS researchers, who can draw from the innova-
tive angle of this book to enrich their own research.


Acknowledgments

  The author has received funding from the European Union’s Horizon 2020 research and
innovation program under the GA n. 956562.


References

Gitelman, Lisa (ed.) (2013) “Raw Data” Is an Oxymoron, Cambridge (MA), The MIT Press.
Kitchin, Rob (2014a) Big Data, new epistemologies and paradigm shifts, in “Big Data & Society”, 1(1),
   2053951714528481.
Kitchin, Rob (2014b) The Data Revolution: Big Data, Open Data, Data Infrastructures & Their
   Consequences, Thousand Oaks, Sage Publications.
Kitchin, Rob (2021) Data Lives: How Data Are Made and Shape Our World, Briston, Policy Press.
137                                                                       Tecnoscienza. 2023. 14(2)

Szczepański, Marcin (2020) Is data the new oil? Competition issues in the digital economy [Briefing],
   European Parliamentary Research Service (EPRS).
van Dijck, José (2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm
   and ideology, in “Surveillance & Society”, 12(2), pp. 197-208.
