Reservoir Management Process by a Reservoir Engineer/Team Work/AI
1. Upon joining a petroleum industry,
immediately following graduation in PE,
how long, in general, would take
for a ‘fresh petroleum engineer’ –
in order for him/her
to apply the ‘classroom knowledge’
on ‘drainage principles of reservoir engineering’
along with the application of
the latest available ‘industrial technology’ -
towards ‘controlling reservoir operations’
that would positively
‘maximize the economic value
of a petroleum reservoir’?
2. In a span of say, 10 years,
upon joining a petroleum industry,
whether a reservoir engineer
will be able to acquire the ability
to predict the consequences of
implementing
various reservoir decisions
that he/she acquired
based on the expected behaviors of a reservoir system
through modeling studies
along with the evaluation of its associated uncertainties?
3. How exactly a fresh petroleum engineer gets translated to become an expert in
(a) reservoir characterization;
(b) reservoir performance;
(c) well performance; and
(d) field development –
over a period of time in an oil/gas industry?
4. How quickly a reservoir engineer
would be able to identify
an appropriate model
for simulating
a dynamic reservoir system
(either by deterministic or by stochastic methods)
by successfully integrating
both ‘static data’ (reservoir structure description involving geology, geophysics,
geochemistry & petro-chemistry)
as well as
‘dynamic data’ (reservoir fluid flow behavior involving pressure/temperature, water/oil/gas
rates, saturations, production logs, well tests, geo-mechanics) –
towards precisely predicting both ‘data’ as well as ‘results’?
5. Whether a reservoir engineer by himself/herself would be able to precisely check,
whether the diagnosed reservoir model
remains ‘nearly consistent’
with reference to the ‘model output’ and ‘field data’ (through history matching)?
Or,
will we require AI in the event of a lack of match -
resulting from an inconsistent/incomplete/incorrect reservoir model – in order to reassure
the ‘optimum and cost effective field development
considering economic, environmental and safety constraints’
as a coupled effect of reservoir performance, well performance and surface facilities?
6. Feasible to become a Master of All the related disciplines:
Basics (Geology, Geophysics, Reservoir, Production, Drilling);
Reservoir Characterization (Geological/Geophysical Modeling, Geochemistry,
Geomechanics, Petrophysics, Production logging, Well Testing, and Integration into Reservoir Model);
Well Performance (Completions and Production Problems, Nodal Analysis);
Reservoir Performance (Analytical/Numerical Predictors, Upscaling, History Matching & EOR), and
Field Development (Pipelines and Surface Facilities, HSE, Economics) by a field reservoir engineer;
or,
a 'team work' would suffice;
or,
need AI
for characterizing complicated unconventional-, fractured-, carbonate-reservoirs?
We NEED solid science from ALL disciplines involved. In American Oil Shale Revolution, we have one major discipline that is corrupt and heavily involved in not only delivering "science" that facilitates drastic overbooking of reserves, but actually restricting quality science and vastly improved predictive capabilities from the other disciplines. It is truly disgusting. I don't know what your role is, but if you are NOT a Reservoir Engineer, be aware any real scientific understanding you develop or discover WILL be trashed if it doesn't tow the CEO/reservoir engineering propaganda.
I'm afraid science is dead. Greed killed it. Good luck.
Greetings Suresh,
With regards to your opening question of "Do we need an efficient Reservoir Engineer, or, a great team work, or, AI for characterizing complex reservoirs?", I agree with one of Scott's points and say "yes". We need all. You need a team of skilled reservoir engineers working collaboratively with differing (challenging) ideas and perceptions, who can appreciate and accept the range of possibilities. You need Monte Carlo type probability analysis, because with complexity comes unknowns and these still need to be accounted for. And AI will help, but only to the degree that its been trained.
And we need a work environment that is honest and based on integrity. Thankfully I don't work where Scott did, though I have seen what he's seen, in different ways. Greed and bias are out there but (and maybe I'm naïve) not all-pervasive.
Your other questions regarding how long it takes to acquire these skills are difficult to answer. It depends on the individual and their work environment & work scope. Will they have 10 years of diverse, challenging experience with good mentorship, or will they repeat one year of mediocre entry-level employment ten times? The former could be a talented, team-leading RE in 6-7 years. The latter may never acquire the skills you describe. Both of these work circumstances exist in our industry and the latter is painfully common.
Hope that helps,
R
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