Schlumberger employs a proprietary vertical-effective, stress-based pore pressure prediction method with core concepts. The method accounts for disequilibrium compaction, clay diagenesis, and hydrocarbon buoyancy—the dominant geopressure mechanisms in passive continental margins. It is fully calibrated at intermediate steps when well logs are available and is fully operable in regions where no downhole information exists.
Pore pressure imaging and analysis services are provided at various scales—regional or semiregional, prospect level, and wellbore scale.
The full prestack waveform inversion (PSWI) technique, using a wave equation–based forward modeling procedure, can convert any common midpoint (CMP) trace in the full-offset domain from P-wave seismic data into three pseudologs—P, S, and density—with frequency content well above the seismic frequency bandwidth. PSWI generates pseudowell logs and elastic rock properties from AVO-quality seismic data with resolution beyond that achieved using conventional velocity analysis techniques, such as tomography.
A fit-for-purpose velocity model is the key input to pressure inversion, and Schlumberger employs its industry-leading spatially continuous velocity analysis for the time-domain seismic imaging/velocity model builder (SCVA/VMB) process. Applied to 2D and 3D volumes, this method uses every seismic trace and time sample for low-frequency velocity model building. At prospect scale, Schlumberger uses the same velocity analysis procedure, but constrains it with proprietary rock physics models and well data, if available.
Because pore pressure imaging is based on mapping seismic velocity to pore pressure using a rock model, any uncertainty in velocity analysis impacts the pore pressure prediction, as well as the calibration of the pore pressure model. Schlumberger has developed a methodology to estimate the uncertainty in the pore pressure image using a stochastic Monte Carlo simulation technique and nonlinear optimization algorithms to propagate the uncertainty associated with velocity analysis and rock model calibration into the predicted pore pressure image. The final results of the uncertainty analysis are a 3D pore pressure image, accompanied by a 3D standard deviation derived from the stochastic simulation algorithm.