During appraisal of an undeveloped segment of a producing offshore oilfield, three well penetrations revealed unexpected complexity and compartmentalization. Business decisions on whether and how to develop this segment depended on understanding the possible interpretations of the subsurface. This was achieved using the following steps that incorporated a novel practical application of Bayesian logic.

1. Scenarios were identified to span the full range of possible subsurface interpretations. This was achieved through a facilitated cross-disciplinary exercise including external participants. The exercise generated 12 widely differing subsurface scenarios, which could be grouped into 4 types of mechanisms: slumping, structural, depositional, and diagenetic.

2. Prior probabilities were assigned to each scenario. These probabilities were elicited from the same subsurface team and external experts who performed step 1, using their diverse knowledge and experience.

3. The probabilities of each scenario were updated by evaluating them sequentially with 21 individual pieces of evidence, progressively down-weighting belief in scenarios that were inconsistent with the evidence. For each piece of evidence, the likelihood (chance that the scenario could produce the evidence) was estimated qualitatively by the same team using a “traffic-light” high-medium-low assessment. Offline, these were converted to numerical likelihood values. Posterior probabilities were derived by multiplying the priors by the likelihoods and renormalizing to sum to unity across all of the scenarios.

4. The most probable scenarios were selected for quantitative reservoir modeling, to evaluate the potential outcomes of business decisions, given each scenario.

Of the 12 scenarios identified in step 1, most were strongly down-weighted by the sequential revisions against evidence in step 3; after this, only scenarios in the “slumping” group retained significant posterior probabilities. The data showed minimal sensitivity to the initial assumption of prior probability in step 2.

This process had several benefits. First, it encouraged the subsurface team to imagine a full range of scenarios that were likely to bracket the actual subsurface “truth,” something that is critical for subsequent decision-making. Second, it allowed belief in the probability of each scenario to be updated systematically in a way that was strongly conditioned to the evidence, so that the choice of scenarios to take through to reservoir modeling was more objective and evidence-based. Third, it allowed an assessment of the usefulness of individual pieces of evidence, which could be used to guide value-of-information assessments for subsequent data acquisition. Finally, the process enabled rigorous Bayesian revision methods to be applied in a simple practical way that engaged the subsurface team without exposing them to the underlying mathematics. During field appraisal and development, when the subsurface is revealed gradually as more data are acquired and studied, the process outlined here provides a practical way of generating and modifying belief in a range of subsurface scenarios while minimizing exposure to potential biases and logical fallacies that could affect subsequent decision quality. It also helps to decide which scenarios are sufficiently probable that they need to be represented by detailed reservoir models.