Now that we’ve all been
trained to consider the Pete Rose method for characterizing the economics
of our prospects, we are aware that Pg is the probability of getting on the
reserve distribution curve. But does it matter which curve we are talking
about? Reacting to the question, as geologists and geophysicists might,
our intuition says, “Yes, the reserve distribution and the Pg together define
the prospect.” We know the risk on a prospect with big upside is usually
higher than one with small potential, but the effort to suggest an appropriate
Pg becomes distorted by the process of defining the low end of the curve.
If the low end is allowed to be very small, then the Pg should be high, right?
Wrong. The Pg is the probability of getting on *this *curve, not
just any curve. Both the high and low ends, not just the low end, define
the curve. This paper first examines a couple of examples of how this
affects us – one from the Pete Rose school and one from our own peer review.
Then we’ll examine how to best deal with this problem and it’s consequences.

An independent oil company at a Pete Rose school present
the class the following example.

They presented a prospect based on an amplitude map
made from 2D and 3D seismic data, which showed a stratigraphic trap based
on a weak amplitude anomaly on monoclinal dip covering an area equivalent
to the size of a Texas county! There were only a few wells in the
area that penetrated to the depth of the map. These wells encountered tight
sands; however, the amplitude anomaly was not present where the drilling
took place. The reserve estimates for this prospect ranged from 1 BCF
to 32 TCF for the P99 and P1, which resulted in a mean reserves estimate
of 2.2TCF. That’s a big prospect!

Next, they guided the class to an understanding of the
Pg. They showed that their drill location was in a bright portion of
the amplitude on a well-defined, four-way structure.

They estimated their Pg for this structure to be to
be about 20% - making use of the fact that it was a four-way and that it
had the brightest amplitude on the map. It seemed reasonable that 20%
was a good estimate for this small, four-way structure and that 1BCF was
a good estimate of its volume.

The problem is that they also used this Pg for the whole
county! They reasoned that 20% was the probability of finding their
minimum case reserves, of 1BCF, and hence their probability of getting on
the curve.

To put this in rough dollar teams let’s assume that
all their costs can be approximated by a net profit of $1.00 per MCF.
This implies that the value of their prospect would be approximately

$1.00 X (2.2TCF mean reserves) X
(20%chance of success) =$440 million.

Pete Rose and most of the participants at the school accepted this as
correct. We feel there is a logic problem here, as did some of the
class members.

The dissenters argued that if the proposed location where moved 2,000
feet in any direction, it would not test the small four-way prospect, but
test the large stratigraphic prospect. In that case, everyone in the
class agreed the probability of logging pay would have to be much smaller,
since it wasn’t in the brightest amplitude and wasn’t on the four-way.

The dissenters argued that there were really two prospects, not one.
The smaller one had a Pg of 20% as had been assumed, but it’s mean reserves
were roughly 0.5 BCF yielding a value of

$1 X 0.5BCF X 20% or $100 thousand.

The second prospect was the big strat trap, with a much smaller chance of success. The probability of having up to 32 TCF stratigraphically trapped in a sand that had no permeable tests is very small. The prospect presenters admitted it was less than 1%, since there are very few pools of this size in world. An approximation, of the countywide prospect’s value is

$1 X 2.2TCF X 1% = $22 million.

The NPV of the two prospects drilled separately would be $100K + $22Million
or $22.1 million instead of the $440 million in the first estimate.

If we persist in the single Pg approach, we feel the prospect is misrepresented.
Its Pg, mean reserves, and NPV are all too high! This is not an attempt
to suppress the advancement of large prospects. Look at Anadarko’s
Bossier Shale example. That’s a recent stratigraphic play that may
truly cover a county in Texas based on known, bypassed pay. The issue
is one of accurately representing the geology in our economics.

Consider the figure at right. This is very much like a real example
seen in a peer review. The orange represents a possible salt stock,
the dashed orange line is the extent of overhanging salt, and the red is
an amplitude anomaly. The two circles labeled 1 and 2 are proposed
locations. In this play, amplitude anomalies of similar quality find
pay sands 80% of the time. We understand the environment to be channel
and channel-levee dominated turbidities, which are highly stratigraphic.
Because of the stratigraphic nature of the sand, and without amplitude support,
the probability of logging pay at the up-dip location (well 2) is very much
less than at location 1, say 20%. Let’s assume that the reserve estimate
for the area in red is 10 BCF, and the up-dip area under salt in the same
fault block is 40 BCF. The approach suggested by the team was to assume
P90, P10 reserves of 10BCF and 50 BCF (10+40), yielding a mean of 27 BCF.
And since amplitudes work 80% of the time, they said the Pg should be 80%.
In order to assign an economic value to this, we
will use $1 per MCF, yielding: |

But this approach, like
the class example above grates on our sense of the geology.

Now, let’s look at the two prospects as separate ones.
The probability of finding up-dip reserves at location 2 might be 20%.
The mean of the sub-salt reserves is probably something like 17BCF.

The economic value of the up-dip reserves would be $1
X 17BCF X 20% = $3.4 million. The mean reserves in the amplitude are
10 BCF with a Pg of 80%. The economic value of the amplitude-supported
reserves would be:

$1 X 10BCF X 80% = $8 million.

The two combined yield a total value of only $11.4 million compared to
the previous $21.6 million.

This valuing process sits much better on our conscience.
We clearly have two prospects here. This is best illustrated by the
realization that the up-dip location is much more risky than the down-dip
amplitude location. It leads to the two prospect – two Pg model.
It is clear here that the 80% probability at location 1 is tied with the
amplitude supported reserve case. Were the first well to work, then
drilling up-dip to reserves is usually a good thing, but here it cannot be
assumed that it guarantees having sand up-dip. An 80% assumption for the
up-dip reserves is not justified even with success down-dip.

One should be careful
of “a prospect within a prospect” duality, where a low risk - low reserve
prospect is coupled with high reserve prospect. It is very tempting
to use the low risk Pg for all of the reserves. Doing so only overstates
the value and reserves, setting us up for big disappointments down the road
after the first, low-risk well is drilled.

How do we recognize these? If the geologic model
used to explain P90 reserve size is different than geologic model needed to
explain the P10 reserves, one should expect prospect duality is present.
Another way to recognize these is to consider different locations for the
initial test within the area used for the reserve distribution. When
the probabilities suddenly jump for locations that lie roughly near one another,
then one should suspect two models are being used.

For example, if a location is proposed inside a bright
spot while the area up-dip of the bright spot is necessary for the P10 case
reserves, then try a thought experiment starting at the first well consider
tests farther and farther up-dip. If the probabilities of finding pay
at two nearby locations dramatically differ, then two models are present.

Characterizing a prospect with one Pg and one reserves
curve is appropriate when there is one geologic model. If we consider
a four-way structure, then for any proposed location inside the closure,
the trapping model is always the same. The only consideration is how
much it fills up. The probability of logging pay at any given location
as we move down structure decreases uniformly. We assume a log normal
distribution for the fill up, and estimate a Pg for being on that curve.
Similarly, when we consider a bright-spot’s edges, there may be some difficulty
in pinning down the actual extent, but we consider it decreasingly likely
that it continues, the farther we try to extend it. So, the lognormal reserves
distribution and one Pg can describe this.

Believing in a high likelihood of finding a small amount
of pay somewhere in a proposed well is not the same as believing it’s an economic
venture. As for mixing the prospects by using one Pg, it shouldn’t happen.
We will end up with inflated reserves that we must pay for later in the game.
Focus on the geology, be fair, and above all, stress accuracy.

The flip side of this is that we need to accept accurate
portrayal of the geology even if it means high Pg’s. We need to guard
against the pressure to obviate geology that comes in the form of suppressing
the P99 reserve size. Pg’s should reflect the geology- as should the
reserve curves we use. In the case of a DHI amplitude, we might expect
the P90 and P10 areas should be adjusted until the mean and mode are not far
from the best guess case of the amplitude size. The Pg should be allowed
to range fairly high in such a case, provided the calculated Pc can be justified
by industry experience.

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