A learning system, supported by binomial probability theory, has the characteristic that newly acquired information is incorporated into the geological model thereby revising the GPOS of subsequent discovery. The informed Explorationist is therefore presented with a decision-gated or staged approach to divest/relinquish or continue exploration. The application of a learning system offers improved discovery frequency for the least cost to explore. This result is compared to a repetitive-system baseline where GPOS remains static throughout the exploration campaign. The disadvantage of a learning system is the time or duration required to absorb new information following each stage of data acquisition. This disadvantage drives the need for Explorationists to select efficient processes and technology resulting in quicker divest/relinquish or continue decisions.
Presenter: Graeme Morrison
Graeme is a Principal Reservoir Engineer with Woodside based in Perth Western Australia. Graeme has been a member of the SPE for 35 years, has a background in Physics and Mathematics, and Graduated with a Masters in Petroleum Engineering from the UNSW in 1990. His work experience has taken him to many locations, with in-depth experiences in the North Sea and Offshore Western Australia. Graeme developed an interest in Exploration Statistics whilst working in Woodside’s Exploration department. His manuscript SPE 186972 published for the SPE-APOGCE conference (Bali, October 2017) is titled: Binomial Probability Theory Supporting a Learning System Applied to Exploration Decision Making.
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