It is a contradiction that our industry has a reputation for being, and in fact is, very conservative when it comes to adopting new technologies and yet is made up of individuals (also known as consumers) who are anything but that: the latter will pick up a Blackberry like they are going out of fashion but drop it in favour of a smartphone almost overnight!
And when I talk to colleagues in the industry and ask them about their reaction to specific new technologies and to those marketing them, they will say things like “this is a solution looking for a problem; they have no domain knowledge; etc”.
In fact the phrase “solution(s) looking for a problem” seemed to me to have become a shorthand for “we ain't going to buy it!” so the other day I googled on this phrase and came upon this interesting short article. It is worth clicking on this to get it onto a separate Tab and coming back to my article when you have read it!
You will see that he is referring specifically to entrepreneurs when he says “I have found that when they have worked in the industry and have lived the problem they are trying to solve, they have a much better shot at success.” To me this defines what the comment about ‘domain knowledge' implies…..….the folk pitching the technology don't understand the problem that might be solved.
So I thought I would do a couple of things.
Firstly I would write down no more than 4 or 5 problems which I see in E&P: these are my ideas and others will have different ones – a point which will I will come to in 5 or 6 lines. How do we:
1. Explore for onshore oil?
2. Move seismic interpretation out of the Stone Age?
3. Solve the “cloud of points” problem?
4. Improve Projects – they are invariably late, over budget and oftentimes do not deliver what was promised production rates?
5. Spot equipment failures ahead of the “Aw, snap!!” message?
I will work my way through the first three of these shortly but please feel free to tell me/us if you have another problem that you think is important to crack.
1. Exploring for onshore oil
We need to remind ourselves that we are not just interpreters of 3D or 4D (towed streamer) seismic but actually need to integrate a wide variety of data.
Exploring onshore, we might have to integrate satellite and airborne data, a significant number of well results (logs, cuttings, core, flow rates), potential field, seismic, surface geology etc.
How might satellite and airborne data help?
Well, it is fairly well documented that seepage of petroleum, and here I am talking about micro-seepage, can impact both the health of vegetation and the colour of surface-exposed rocks. Such changes should be visible both from space and from altitude, and the data available to us has mushroomed both in amount and in the variety and resolution of sensor technologies. Can we get at the meaningful patterns using Analytics?
2. Moving seismic interpretation out of the Stone Age
You may have seen the news of the resounding win by the artificial intelligence program AlphaGo (built in the UK! Hooray!! Now owned by Google…..) over the South Korean world champion, the Go master, in the complex board game Go back in March.
AI experts had predicted – last year, I think - that computer programs needed at least 10 more years of development before they would be able to beat a Go master. However, as I understand it, any rules-based system such as Go – and see below! – is a prime target for Machine Learning.
Well-established ‘rules' have been proven for Stratigraphy, Structural Geology, Sedimentology and describing Petroleum Systems (especially by creating GDE, CRS and CCRS maps). Nowadays these ‘rules' are most commonly applied through seismic data, especially 3D seismic data.
The key ‘technologies' are a) large quantities of inexpensive multi-client 3D seismic and b) commoditised interpretation workstations.
In truth, this methodology has now become completely commoditised: little commercial advantage accrues from getting it right, simply disadvantage flows from incompetent execution.
Hmmm, an interesting message there for the folks who have been telling me that it will take “a decade or more” for AI to replace Subsurface Scientists in the oil & gas industry!
How long before an AI system can actually do all of this?
3. The ‘cloud of points' problem
Thirty years ago, a “previous employer” had an internal R&D project which rejoiced in the name of Lithology & Fluid Prediction (LFP).
Now LFP was founded on the idea that not only does seismic data show us geological geometries – folds, downlaps, onlaps, erosional truncation and the like – but that the very existence of reflections depends on rock physics, contrasts in impedances, and that we might get smart enough to predict actual lithologies and – wait for it – hydrocarbon content. And of course this notion has had some success, with AVO anomalies, flat spots etc etc.
However, I assert that we have not done as well as we might with such predictions and that this is primarily due to the relatively weak calibration that can be derived from well logs.
Who has worked in this arena and not found that well log-derived parameters such as sonic velocity, density or resistivity exhibit “cloud of points” behaviour when plotted against for example depth? A “cloud of points” through which it is a pretty brave person who fits a straight line or series of such lines, and then uses them to make lithology/fluid predictions?
Part of the problem has been selection and prejudgement. So many wells have penetrated, for example, the Kimmeridge Clay Formation in the UKCS that the problem only seems tractable if only a limited selection of them are used. And then a model is imposed – for example that the particular property will vary most strongly with depth or, perhaps, stress (if we have a way of calculating it).
Thus a sample of the available data is exposed to bi-variate analysis whereas the correct approach would be to subject all of it to a multi-variate analysis.
Maybe then we might even find some more oil in mature provinces such as the North Sea or South East Asia!
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