Stories tagged with Shock model

Saudi Arabia: An Attempt to Link Oil Discoveries, Proven Reserves and Production Data

This article is an attempt to apply the Hybrid Shock Model (HSM) on Saudi Arabia's oil production. In a nutshell, the HSM is trying to model the observed production profile from the discovery curve by simulating the different phases involved in the development of oilfields (initial discovery, planning, build, maturity). The HSM is a variant of the Shock Model initially proposed by WebHubbleTelescope. One of the byproduct of the HSM is the instantaneous reserve addition (noted R). I based the following work on the assumption that the value for R should be somewhat close to available proven reserve figures. Of course, for Saudi Arabia available proven reserves are highly suspicious because of huge overnight reserve increase among OPEC members in the 80s without any new discoveries. Therefore, I will consider three increasingly conservative proven reserve hypothesis: 1) PR1:  the official numbers as published by BP; 2) PR2: spurious increase are removed; 3) PR3: as proposed by Euan here, cumulative production is also removed. The volume of recoverable oil from Ghawar is then inferred from the HSM based on the discovery curve from IHS as the value that is minimizing the error between proven reserves and simulated reserve additions. My conclusions are the following:
  1. No reasonable value for Ghawar size (i.e. < 150 Gb) is supporting the official proven reserve figure.
  2. The most likely size for Ghawar is 109 ± 10 Gb and the simulated reserves are matching closely the corrected proven reserves (PR2).
  3. Production capacity could reach a maximum around 10.5 ± 0.5 mbpd (crude oil + Natural Gas Liquids) between 2010 and 2013.

Application of the Dispersive Discovery Model

This is a guest post by WebHubbleTelescope.

Sometimes I get a bit freaked out by the ferocity and doggedness of the so-called global warming deniers. The latest outpost for these contrarians, climateaudit.org, shows lots of activity, with much gnashing over numbers and statistics, with the end result that they get a science blog of the year award (a 1st place tie actually). Fortunately, the blog remains open to commenting from both sides of the GW argument, which if nothing else makes it a worthy candidate for some type of award. Even though I don't agree with the nitpicking approach of the auditors, they do provide peer review for strengthening one's arguments and theories. I can only hope that this post on oil depletion modeling attracts the same ferocity from the peak oil deniers out there. Unfortunately, we don't have a complementary "oil depletion audit" site organized yet (though Stuart and Khebab, et al, seem to be working on it--see yesterday's post), so we have to rely on the devil's advocates on TOD to attempt to rip my Application of the Dispersive Discovery Model to shreds.  Not required, but see my previous post Finding Needles in a Haystack to prime your pump.

Finding Needles in a Haystack

This is a guest post by WebHubbleTelescope.

In school, we used to do horrendously difficult mathematical "word" problems routinely. I remember occasionally getting one right, but more often ended up punting on the problem, and then waiting for the teacher to explain the solution in all its elegant simplicity. Of course, just about every real-world problem contains inherent ambiguities and incomplete information. So we rarely get to see the elegant solution in our day-to-day work life. Sometimes we get lucky and nail a problem, but in the majority of cases, we eventually resort to creating a limited model of the problem domain and deal with that.

The problem that I have recently wrestled with has to do with predicting future oil discoveries based on historical dynamics. Ideally, I want to reduce it to a solution that has the elegance of a word problem, and not have to deal with messy economic and geologic factors that would quickly turn it into a rat's nest of complexity. Call me an optimist in this regard, but my intuition tells me that the solution remains as simple as ... finding needles in a haystack.

The Shock Model (Part II)

This post is the second part of a review of the Shock Model that was introduced in part I. The shock model was developed by WebHubbleTelecsope and aims at modeling oil production based on the backdated oil discovery data. In the first part, we proposed to apply a bootstrap filter in order to estimate the shock function that was previously manually set by the user. We also observed that the predictive ability was limited because of a too conservative projection of future extraction rate values.

In this second part, I propose a modification of the extraction rate function in order to improve the predictive ability of the model. This modification is based on the observation that the extraction rate function is linearly dependent to the ratio of the cumulative production to the cumulative shifted discovery. The new formulation is similar to the logistic differential equation at the difference that the Ultimate Recoverable Resource (URR) is replaced by the cumulative shifted discovery.

I look also at the modelisation of reserve growth which is an important aspect of modern oil production that is often overlooked in the peak oil community. 

The code in R language is provided at the end of this post.

Printer friendly version in pdf.

The Shock Model: A Review (Part I)

WebHubbleTelescope, a long time TOD poster, has been one of the most active in the blogosphere in the area of oil production modeling. He has advocated a more physically based approach instead of a heuristic curve fitting approach such as the Hubbert Linearization. He proposed an original method, the so called Shock Model, that has a clear physical interpretation and that is making use of both the production profile and the discovery data. I think that a review of the Shock Model is long overdue.

I also propose three modifications or extensions:

  1. Originally, the instantaneous extraction rate function E(t) has to be provided by the user. I propose a method to estimate E(t) directly from the observed production profile.
  2. Reserve growth is modeled as a fourth convolution function based on an empirical parabolic cumulative growth functions (this will be detailed in part II).
  3. A new way to project future extraction rate (in part II).

In summary, the shock model is a simple and intuitive model that is making use of both the production profile and the discovery curve. In this essay, the method is applied on the world conventional crude oil production (crude oil + condensate) and the ASPO backdated discovery data. Interestingly, the derived Reserve to Production ratio (R/P) seems to match the values obtained when using the proven reserve numbers (BP) once corrected for Middle-East spurious reserve revisions (in 1985, 1988 and 1990). In addition, R/P values are presently at a record low levels and below what have been observed during the previous oil shocks.

The code in R language is provided at the end of this post.