Artificial Intelligence in Petroleum Engineering
1. Whether AI alone - would be able to mimic a real oil/gas field production scenario - in the absence of reservoir simulation?
2. What would be the various sources of 'uncertainty' that would be associated with an AI technique?
3. How would AI consider ‘measurement uncertainties’ (errors in measurement of state variables associated with reservoir rock properties and rock-fluid interaction properties) as well as ‘structural uncertainties’ (errors associated with the mathematical representation of actual draining principles of hydrocarbons) "explicitly"; along with ‘parametric uncertainty’ (resulting from the coupled effect of both measurement as well as structural uncertainties)?
4. How would AI do justice – particularly with the very limited data set associated with ‘reservoir permeability’?
5. Whether AI would reasonably support a hydrocarbon reservoir with a sparse, heterogeneous, anisotropic, multi-phase, multi-dimensional data?
6. Whether AI would be supplied with the best ever algorithms for coping with all kinds of uncertainties – by explicitly segregating the various forms of uncertainties by an efficient data training?
7. Whether AI-powered robots will be able to detect the oil seeps (in deep sea) efficiently by mitigating the exploration risk while lessening the harms to marine life?
8. Whether AI has the ability to forecast ‘well collapses’ – well before its occurrence?
To what extent, ‘downtime’ would be expected to be reduced - upon introducing ‘traffic light system’?
9. To what extent, the concept of ‘digital twins’ remains efficient in addressing the challenges associated with the hydrocarbon industry?
10. To what extent, AI would remain helpful – by efficiently recognizing patterns through deep learning – towards averting the catastrophes associated with HSE?
International Journal of Artificial Intelligence in Education (2000), 11, 122-143
122
The roles of models in Artificial Intelligence and Education
research: a prospective view
Michael Baker GRIC-COAST, CNRS & Université Lumière Lyon 2, 5 avenue Pierre
Mendès-France, 69676 Bron Cedex, France, email: [email protected]
Abstract. In this paper I speculate on the near future of research in Artificial Intelligence and
Education (AIED), on the basis of three uses of models of educational processes: models as
scientific tools, models as components of educational artefacts, and models as bases for design
of educational artefacts. In terms of the first role, I claim that the recent shift towards studying
collaborative learning situations needs to be accompanied by an evolution of the types of
theories and models that are used, beyond computational models of individual cognition. In
terms of the second role, I propose that in order to integrate computer-based learning systems
into schools, we need to ’open up’ the curriculum to educational technology, ’open up’
educational technologies to actors in educational systems and ’open up’ those actors to the
technology (i.e. by training them). In terms of the third role, I propose that models can be bases
for design of educational technologies by providing design methodologies and system
components, or by constraining the range of tools that are available for learners. In conclusion I
propose that a defining characteristic of AIED research is that it is, or should be, concerned
with all three roles of models, to a greater or lesser extent in each case.
"Wenn wir an die Zukunft der Welt denken,
so meinen wir immer den Ort, wo sie sein
wird, wenn sie so weiter läuft, wie wir sie
jetzt laufen sehen, und denken nicht, daß
sie nicht gerade läuft, sondern in einer
Kurve, und ihre Richtung sich konstant
ändert."
"When we think of the world’s future, we
always mean the destination it will reach
if it keeps going in the direction we can
see it going in now; it does not occur to
us that its path is not a straight line but a
curve, constantly changing direction."
Wittgenstein (1980), pp. 3 / 3e
.
1. INTRODUCTION
If, as some anthropologically-minded archæologists would claim, the present is the key to the
past, then perhaps the future is the key to the present? In this paper I assume the converse —
that the present and the past are keys to the future — for the case of research in the field of
Artificial Intelligence and/in Education (henceforth abbreviated to "AIED").
Any view of what objectives a research field may achieve in the future must be based on a
view of the nature of the field in question, up to the present day. I characterise the past, the
present and the near future of AIED research in terms of a combination of different roles played
by models of educational processes, namely: models as scientific tools, models as components
of educational artefacts, and models as bases for design of educational artefacts.
It should be noted that the views expressed here are not those of an objective historian of
science, but rather of a researcher engaged in the field that is being discussed. In that case,
description, prediction and prescription coincide to a certain extent.
One could say that there are basically three sorts of argumentative texts: those that argue
(mostly)
No, AI alone cannot mimic a real oil/gas field production scenario without reservoir simulation.
Sources of uncertainty in AI techniques include data, model, input, and predictive uncertainties.
AI can address measurement uncertainties, structural uncertainties, and parametric uncertainties through Bayesian methods, ensemble methods, and sensitivity analysis.
AI can improve predictions for reservoir permeability with techniques like transfer learning and data augmentation.
AI can handle sparse, heterogeneous, anisotropic, multi-phase, and multi-dimensional data in reservoir engineering using deep learning techniques.
AI algorithms can cope with uncertainties by employing probabilistic modeling, ensemble methods, and efficient data training.
AI-powered robots can efficiently detect oil seeps in deep sea, mitigating exploration risks and minimizing harm to marine life.
AI can forecast well collapses by analyzing well integrity data and historical failure patterns, reducing downtime with a "traffic light system."
Digital twins, integrated with AI techniques, remain efficient in addressing challenges in the hydrocarbon industry.
AI can help avert HSE catastrophes by recognizing patterns through deep learning and analyzing large volumes of data.
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