Intelligent Exploration and Appraisal Program for a Multiprospect Development

Abstract

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.