Based on wireline logs, core data, and pressure information obtained directly during drilling, the various shale units within the Wolfcamp Formation in the Delaware Basin are known to be variably pressured with depth, and the pressure can change laterally within the same rock formation. Zones with anomalous high pressure are generally linked to wells with better production rates. Unknown overpressured areas are also considered a drilling hazard and being able to predict these cells is of high interest. Pore pressure prediction using on-shore seismic data is not trivial as the relationship between porosity and overpressure is complicated by a relatively complex geological history. In these environments, the typical variation observed in seismic velocities may not relate directly to changes in pressure; for example, the presence of gas, and the presence of TOC can both act to slow the velocity which mimics a pressure response that is actually erroneous.


Pressure variations can be difficult to measure in these low permeability formations, making calibration difficult. Pore pressure is a critical input to a geomechanical model and can impact the mechanical behaviour of the well. It is also desirable to map the high pressure areas before drilling decisions are made as wells that intersect these zones are more prolific producers. As such 3D seismic data are being used as a reconnaissance tool away from known well information. Consequently, comparing the predictions of different geomechanical models can be used to help calibrate the pore pressure model. A well-based workflow was developed which was able to predict the pore pressure and construct a geomechanical model which matched the wellbore measurements. This model was then tested on wells with the requisite log dataset and was able to replicate the observed mechanical wellbore behaviour, highlighting the accuracy of the pore pressure prediction. The resultant models were then applied to a high resolution 3D seismic inversion encompassing key elastic properties and facies prediction to produce a 3D understanding of the distribution of pressure and stress.

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