Summary
While many companies are hunting for elephants, exploration results in deep
offshore plays over the past years show the increasing trend away from
giant-field discoveries toward smaller fields in the 50 - 100 million bbl
range, which tend to be geographically dispersed. These resources need to
accumulate to a critical mass, a global threshold, to justify an economically
viable development. This is not only a question of volume but also of
geographic location of the discoveries, and the threshold, of course, also
depends on economic factors.
A methodology is proposed to evaluate the potential of a block to lead to a
multiprospect development and to optimize exploration and appraisal. It is
illustrated by a real deepwater case study including five discoveries and four
prospects, 10 - 30 km apart. The practical approach taken is to define circles
or ellipses on a map representing potential hubs and their catchment areas. For
each area, a global resource threshold is defined by analogy with other
regional developments or by a detailed economic study of representative cases
(not discussed in the paper).
A probabilistic model of the resource base is derived from the geological
assessments of discoveries and prospects. It is entered into Monte Carlo
simulation to generate a large number of scenarios representing exploration
outcomes and discovery volumes, which are stored in a scenario database. This
allows a probabilistic evaluation of the performance of an exploration and
appraisal program, using specially developed indicators such as the cumulative
discovered P50. Intelligence is introduced in the process by evaluating after
each well the probability of meeting the threshold with the remaining wells. If
it is low (10% or less) the program is stopped, which has a great impact on
risked economics.
The main results of the analysis are the economic decision tree with the
probability of a development decision and the P90/P50/P10 of the developed
resources; the number of wells in the dry branch of the tree, which actually is
not a fixed number but a probability distribution; the definition of a firm and
contingent well program; and an optimum drilling order, which may also reduce
the well count.
© 2012. Society of Petroleum Engineers
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History
- Original manuscript received:
8 July 2011
- Meeting paper published:
31 October 2011
- Revised manuscript received:
7 March 2012
- Manuscript approved:
13 March 2012
- Published online:
24 April 2012
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