78 comments on Predicting Future Supply from Undiscovered Oil
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78 comments on Predicting Future Supply from Undiscovered Oil
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Your graph shows production prior to 2015. If it takes 8 years to find, plan, develop and bring onstream production then the graph should be essentially zero to that point. Sure you might assume some can be bought to maturity faster, but there is a limit (3-4 years I'd suggest). That goes double because this has to be a totally new field to be compared to the IEA data (red wedge part vs cyan wedge).
Thus the question - are you comparing like with like?
I think to arrive at a comparable set of data you would need to shift your production line to the right - undershooting the IEA graph at all points (I think) and implying the exponential rise shape of the IEA data is very optimistic from your PoV.
Read carefully what I wrote:
... it takes an average of 8 years between first discovery well and the time oil production is mature and reaches a production plateau
Again, this is the average delay between the initial oil discovery and the time you get a mature oil production from it.
Shouldn't there also be a minimum delay?
It could be, you could refined this model and add many more parameters. This model is simply modeling the effect of the oil industry on oil discoveries via a simple linear convolution process by a transfer function. This transfer function can be modified in order to impose a minimal number of years (or months) but there is always the possibility that a shallow onshore field can be brought online fairly quickly.
Yes, it removes one parameter by assuming always that the standard deviation equals the mean, giving a damped exponential PDF. This means that there is less than a 10% chance of having a latency less than 10% of the mean latency.
How about rise discovery rate? Is it impossible? Why?
There is new exploration technology which can triple oil discoveries for same money and time-frame as compared with conventional technologies www.binaryseismoem.weebly.com.
The Dispersive Discovery model assumes an accelerating search space; I started out with a high-order power-law but a rising exponential works as well. So the fact that new and better technology exists for increasing discovery rate, that is well suited to the model. Yet, even though we have these huge increases in search rate per year, we still keep finding fewer and fewer reservoirs on average per year. I keep saying that it is a very conservative predictor in the fact that it assumes large size discoveries are equally likely at any time and that no relenting on the search acceleration is allowed. If you relaxed these considerations, the outlook would be even more grim.
"Yet, even though we have these huge increases in search rate per year, we still keep finding fewer and fewer reservoirs on average per year."
You are right indeed. Situation, when exploration well spudding is based on sisemic data only exists for a long time. Today, as it was decades before, oil companies drill mostly dry exploration wells. Drilling success rate doesn’t overcome 25% on average. It means that three dry wells go to waste from each four drilled. It means also that discovery occurs too slowly today, but there is a highly productive exploration technology (Seismo-electromagnetic - SEM) for detection of hydrocarbon deposits. It provides a success rate close to 75%. In other words, 3 productive wells for each dry well. Obviously with the technology like this world oil industry could make three times more oil discoveries then using conventional technology, and this technology won't need more investment,time-frame and so on compare to a conventional one. It would significantly mitigate world energy problems.
Seismics---Prospects---Drilling---Discoveries: one in four
Funding --------project time-----------------------------------------
SEM--------Prospects---Drilling---Discoveries: three in four
www.binaryseismoem.weebly.com
You have changed the straw's diameter, not the size of the glass.
Further, to the extent that discovery means 'new,' the wells are still going to be concentrated where the oil is - the problem being, the area of the globe where the oil isn't is pretty well known at this point.
Right, and the point is that we have more and more efficient ways of analyzing the smaller and smaller places left to discover oil. This is an example of rapidly exploring diminishing returns. A double-whammy, so to speak.
Take a look at what I wrote.
There is zero chance of finding a new field today and getting production from it next year. I'd suggest there is zero chance of production up to about 3-4 years minimum. If you are predicting future supply from undiscovered fields that delay has to be in there, smoothing functions or not.
It might be mathematical, but its not physical.
The model is reflecting that already, your original comment included the word find where as the build time as nothing to do with discovery timing.
I'd suggest there is zero chance of production up to about 3-4 years minimum
I could add a lower-cutoff value but it won't change the result by much as the value of the transfer function used here (could be interpreted also in terms of probability density function) is very samll for this time range.
The model creates a "dead zone" from the convolution of the fallow, construction, and maturation phases. Since each phase has less than a 10% chance of achieving a latency less than 10% of the mean, the succession of these three reduces that probability even further. It is a neat mathematical trick that happens to match reality. It can also account for the fast decrease in the tails of the normal distribution or Poisson distribution, where you can imagine many of these phases might apply.
It seems to me the distribution of the development and production of resources discovered in a given year would be fairly skewed. There might be a minimum of 4 years, but a maximum of 20 years or more.
Do you have actual data that you are basing your model on? It seems like quite often, fuel has to get scarce from other sources, before it makes sense to put in the infrastructure to develop the oil. We knew about oil in Alaska, but didn't develop it until US-48 started declining. Oil in Iraq has been known for quite a while, but political issues have interfered with development.
The exponential distribution is definitely skewed. There is a finite probability of having delays of 20 years or more. As a matter of fact, the combination of the 4 latencies could conceivably push the peak location to beyond 30 and approaching 40 years. This occurs when you add in the 0.04 proportional extraction rate, which essentially provides a 25 year "shift".
As far as actual data, there is some data that we can infer parameters from:
http://mobjectivist.blogspot.com/2005/10/oil-production-ramp-up-times.html
The reasons for delays in developing the Alaska fields and the pipeline are given here, in a TIME article of 1971 (extracts):
http://www.time.com/time/magazine/article/0,9171,877050,00.html
This is an example of the fallow period.
The way I look at it, the point spread function associated with startup time is decidedly asymmetric, 3-4 years at one end, 20+ at the other. Convolve that with the expected discovery curve and you should see something that has a sharp 'kick' at the 3-4 year mark, with a reducing power law shape thereafter. It probably possible to graph with a hour and a suitable package.
However there is another question. Where are these new discoveries likely to be made? Its certainly not going to be a random event - many of the likely spots have been well surveyed - particularly if it were cheap to do so. Even if you take the plot of previous discovery rates, you need to take into account that discoveries in the tail are harder and more expensive, reducing the rate that might be expected.
Thus we should expect oil to be preferentially found in the difficult to reach and expensive spots - pushing out discovery -> exploitation times even further still. Rates will also be less as smaller fields are addressed. All that would tend to flatten the curve, certainly not the exponential increase of the IEA graph.
The Dispersive Discovery model takes future discoveries into account. By the time that the backside tail is reached, the search space traversal is 100's of times faster than it is on the frontside. I promised a few plots earlier. Here are a few examples of noisy discovery profiles and the smoothened Shock production curve (in red) that follows:




Compare against Khebab's Figure 3.
These are each Monte Carlo runs of 120,000 samples each (corresponding to number of reservoirs tapped) with a maximum reservoir size of 80 billion barrels and a minimum nucleated size of 2 million barrels. The key thing to note is that the amount of fluctuation does not affect the final shape after production convolution with the 3 stages plus the 0.04 proportional extraction rate. Yet these are all still random events in the sense that the DD profile provides a weighting for the sampling space.
I just caution on the interpretation of the IEA concave-up curve. Some of this is justified as it it making up for the concave down shape of previous discoveries. But of course they truncate the data before it reaches an expected maximum.