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unnecessary strain on the resources of an organisation. There are other techniques
described in the literature (for example, the analytic hierarchy process (Saaty, 1980)
and Markov chain analysis (Das et al., 1999) but these have either not been applied to
the upstream or the input they demand and, in many cases, the output they produce, is
not complementary to the other investment techniques used by organisations. Hence
their use would represent a significant amount of additional work for the
organisations. For these reasons, the tools described in the sections above, the researcher believes comprise the toolkit currently available to the upstream decisionmaker.
The following section provides an indication of how these tools can be integrated into
one approach for investment appraisal in the upstream. There are numerous other
ways that the tools can be combined. The main aim of the next section is to demonstrate that the tools are complementary.
5.7 CURRENT CAPABILITY
The techniques presented above represent current theory in investment appraisal
decision-making in the upstream oil and gas industry. This section presents an illustration of how these tools can be used together when an upstream company is
considering whether to drill an exploration well in a virgin basin at an estimated cost
of £10 million. It has been informed, modified and validated using knowledge gained
from the decision theory and oil industry literatures and insights ascertained from
118 attendance at conferences and seminars during the course of the research. The approach is summarised in figure 5.12.
The first step involves the geologist making a prediction based on historic statistics
and analogues of other basins and plays with similar geological characteristics, of the
chance of there being any hydrocarbons in the prospect. Some practitioners define
this chance of success estimate to be “geological risk” (Simpson et al., 1999).
Sensitivity analysis can be used here to identify the key reservoir parameters in this
1. Assess the chance of success based on historic statistics and analogues of other basins and plays
with similar geological characteristics.
2. Use sensitivity analysis to determine the critical reservoir parameters.
3. Conduct a probabilistic analysis of reserves using Monte Carlo techniques. If necessary, perform
a further sensitivity analysis here by altering the shapes of the probability distributions assigned
to the reservoir parameters and changing the nature of the dependencies between the variables.
4. Extraction from the probabilistic output of the reserves calculation of some deterministic samples
–for example, p10, p50 and p90 (high, mid, low cases).
5. Use sensitivity analysis to determine the critical economic parameters.
6. Perform probabilistic economic analysis for each deterministic reserve case using Monte Carlo
techniques. If necessary, perform a further sensitivity analysis here by altering the shapes of the
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- Summer '14
- The Land