The method the industry has used for three decades — now in a commercial-grade desktop application. Score reservoir, source, seal, trap, and charge evidence into calibrated [0, 1] surfaces. Propagate with explicit spatial models. Combine into a composite Common Risk Segment map with paired confidence and full provenance audit trail.
The petroleum system requires all five elements to work simultaneously: a reservoir to hold the hydrocarbons, a source to generate them, a seal to prevent escape, a trap to focus accumulation, and a charge pathway to deliver fluids at the right time. Failure of any single element means a dry hole. The PFA application scores each element independently and combines them with non-compensatory methods that respect this logic.
Reservoir is not a single number — it decomposes into Presence (is there sand at all?) and Effectiveness (does it have adequate porosity and permeability?). Source decomposes into Presence, Richness, and Maturity. The PFA application supports this hierarchy natively: Play → Element → Component → Proxy, with combination at each level.
The same GDE polygon map can serve as a proxy for Reservoir Presence, Source Presence, and Seal Presence simultaneously — the system reads the appropriate column from the 74-entry GDE Library for each context. Multi-context reuse of data layers is not a workaround; it is the designed workflow.
Does porous, permeable rock exist at the target horizon? The GDE Library provides reservoir_presence_prob directly as a calibrated [0, 1] value. Effectiveness scored from porosity, N:G, and permeability data.
Has organic-rich rock been buried deeply enough to generate hydrocarbons? Source potential from GDE Library, richness from TOC measurements, maturity from vitrinite reflectance and Tmax.
Is there a laterally continuous, ductile, low-permeability cap above the reservoir? GDE and Lithology Libraries provide seal scoring. Thickness and capillary pressure data quantify effectiveness.
Is there a structural or stratigraphic geometry that focuses hydrocarbon accumulation? Structural closure maps, four-way dip closure, and fault-seal risk from the Structure Library.
Did hydrocarbons migrate to the trap, and did the trap exist when charge arrived? Kitchen proximity, migration fairway modeling, and burial-history timing constraints.
Petroleum exploration produces the widest variety of evidence types: well data (logs, cores, tests), seismic interpretations (horizons, attributes), geological maps (GDE polygons, lithology polygons, structural domains), and derived surfaces (gravity, magnetics, basin models). The PFA importer handles all formats and the scoring system normalizes everything to [0, 1] favorability.
The SWSS v2.0 scoring system operates on a four-level hierarchy. Each proxy produces a scored raster. Proxies combine into component scores. Components combine into element CRS maps. Elements combine into the play-level CCRS. Combination method and weights are independently configurable at each level, with non-compensatory geometric mean as the default.
weighted geometric mean — it is non-compensatory
(a zero element kills the composite) but less punishing than pure multiplication for sparse frontier data. In mature basins
with discovery/failure calibration, switch to weights-of-evidence.
Always run weight-perturbation sensitivity before committing to a prospect ranking.
The PFA system generates a paired confidence surface for every CCRS. Confidence is computed via kernel density estimation (Sheather–Jones bandwidth), independent of the interpolation decay. Well-explored fairways show high confidence; frontier blocks show low. The composite can be confidence-weighted so that poorly constrained anomalies cannot outrank well-constrained leads.
A regional explorer opens a new project, imports five evidence layers (TOC, N:G, seal thickness, structural closure, Bouguer gravity), tags each with a play element, and runs the scoring pipeline. In under 15 minutes the application produces a composite CCRS map with confidence transparency, identifying two leads and one prospect — all with full audit trail and sensitivity analysis ready for peer review.
.pfa project · 317 attribute library entries across 5 tables (GDE 74, Lithology 81, Structure 64, Alteration 52, Hydrogeology 46) · 49 scoring contexts.We are working with exploration teams, new ventures groups, and research partnerships. If you are evaluating a frontier basin, ranking a portfolio of leads, or building a regional prospectivity atlas — the PFA application is the tool that was missing.