We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The chapter attempts to produce a few guidelines for pressure prediction. These guidelines for best practices are divided into three broad categories: subsurface geological habitat for pore pressure (geology), physics of pore pressure generation (models), and technology for subsurface prediction (tools). An integrated workflow should satisfy four important criteria: (1) the model should be geologically consistent, conforming to the geologic history; (2) model variability should be constrained by bounds derived from physical understanding of the rocks; (3) the model should agree with all available data from wells and seismic measurement; and (4) modeling uncertainty should be rigorously quantified. The chapter describes various approches for uncertainty quantification for pore pressure prediction.
This chapter discusses technologies that yield geologically plausible and physically possible interval velocities from surface seismic data. The workflow is termed RPGVM, rock physics guided velocity modeling. The approach can be used on any algorithm for interval velocity computations – be it conventional or based on inversions such as tomography and FWI. The goal is to define the parameter base associated with a particular inversion approach so that the inferred velocity model is constrained by rock physics and bounds of pore pressure. Applications from the Gulf of Mexico and offshore India are described. The chapter shows the value of rock physics templates for deriving velocity models with anisotropic tomography.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.