The Dusk of MEFS in the Digital Era of Exploration Value Creation

Abstract

The concept of minimum economic field size (MEFS) has been used by explorationists for almost four decades. MEFS is often the only filter to distinguish between a commercial and a non-commercial discovery—far before a wildcat well is drilled—to test a prospect for a working petroleum systems hypothesis. As simple as it gets, the concept started to lose traction in the 21st century as subsurface targets became more and more challenging. In the case of tight hydrocarbons, it is fairly common to observe a P90 case net present value (NPV) to be negative, a P50 case to be positive, and a P10 case to be negative again. The reason for this outcome is that a whole set of full-cycle factors, in addition to the field size, affects prospect commerciality. Their uncertainty ranges can match or exceed resource estimate uncertainty. These factors include, but are not limited to, initial productivity of development wells, estimated eltimate recovery (EUR) per well, decline curve parameters, capital investments, operating costs, and the project phases’ durations. A new way of handling the full universe of risks and uncertainties faced by modern explorers is already available in the new generation of industry-leading integrated prospect risk, resource and value assessment software. Innovators and thought leaders can already substitute MEFS with a commerciality threshold (CT) that neatly mimics board considerations at the final investment decision (FID) stage gate. Others can consider the economic chance of success (ECOS) estimated with a probabilistic full-cycle mindset, as an additional metric valuable for risk management purposes. Using fictional case studies inspired by real-life assessment situations, we discuss the additional value creation by a CT-powered workflow as compared to an MEFS-based one and explain the reasons for the key differences. The discussed workflow does not eliminate nature-specific uncertainties; neither does it reduce the geological risk. However, it helps to better understand human-controlled risks and prepare management exploration decisions with a greater degree of confidence.