A New Petrophysically Constrained Predrill Pore Pressure Prediction Method for the Deepwater Gulf of Mexico: A Real-Time Case Study

Abstract

Improper planning and execution of deepwater drilling programs can lead to high costs and unsafe conditions. Proper well planning requires reliable estimates of the expected pore fluid pressure and formation strength prior to drilling. Such pressure predictions are based on integrated seismic and offset well data. A new, rock model-based approach especially suited for deepwater pore pressure imaging is introduced here and applied in an example of a deepwater Gulf of Mexico well. P- and S- velocities were determined both at an offset well and for the future drilling location, using prestack seismic full waveform inversion. Both predicted velocities were later verified with log measurements. Using the new model, a significant pore pressure increase at depth was predicted before drilling the well and verified while drilling (Figure 1). For the entire well, the predicted and measured pore pressure gradients agree within half a pound per gallon equivalent mudweight (Figure 1). The shear velocity, and the extracted shear modulus, proved to be excellent indicators of low effective stresses, corresponding to overpressured formations (Figure 2). Introduction It has been common practice to predict pore pressure before drilling from conventional seismic stacking velocities with a normal compaction trend analysis using, for example, the well-known Eaton approach (Eaton, 1972). Velocities that appear to be slower than the ‘normal velocities’ are indicative of overpressure, which then is quantified using an empirical equation. However, there are several problems with this approach. First, conventional seismic stacking velocities are usually unsuitable for pressure prediction since they are not "rock or propagation velocities" (Al-Chalabi, 1994). Second, these velocities lack resolution in depth. Third, in a deepwater environment, sediment loading often has been so fast that pressures in these sediments are above hydrostatic (geopressured) right below the mud line - unlike, for example, on the continental shelf of the Gulf of Mexico (Dutta, 1997). This prevents development of a normal compaction trend, thus invalidating the entire approach in the deepwater. Our new approach is trendline independent and uses a deepwater rock model for geopressure analysis. The model (or approach) is based on several seismic attributes, such as velocities and amplitudes and is calibrated with offset well information. Pore pressure is calculated as the difference between overburden stress and effective stress. The effective stress affects the grain-to-grain contacts of clastic, sedimentary rock, and consequently, the velocities of seismic waves propagating through such rock (Domenico, 1984; Dutta, 1997). The rock model has various components: relations between porosity, lithology and velocity, clay dehydration, and transformations relating both density and Poisson's ratios of the sediments to effective stresses acting on the matrix framework. The key inputs that drive the rock model are velocities (P and S) obtained from a variety of velocity tools. Iterative velocity calibration and interpretation are two essential steps in the prediction process to ensure that the velocity fields are within the realm of expected rock or propagation velocities. In the following study, we demonstrate how the P- and S- velocities used in a Gulf of Mexico example were derived using prestack waveform inversion and we describe the rock model in more detail.

Worldwide Pore Pressure Prediction: Case Studies and Methods

“…People had recognized that stacking seismic velocity does not provide the accuracy and resolution for pore pressure prediction. More advanced ways to obtain adequate interval velocities have been developed, e.g., Dip move out (DMO) and 3D prestack time migration (Kan, 1999), prestack waveform inversion of time-migrated seismic gathers (Dutta, 2001), reflection tomography (Sayers, 2002), Grid-based tomographic inversion techniques (Liaw, 2008), were applied. To maintain the consistency between seismic velocity and geological structure and well data, trend-kriging of well data was applied to improve the seismic velocity to be used for pore pressure prediction (Noeth et al, 2006;Bachrach et al, 2007).…”

“…In 2001, Dutta (2001 applied the rock model-based approach to predict pore pressure predrill and while drilling for a deepwater well in GoM.…”

“…Since deepwater operations is very expensive in term of rig time as well as the consequence of any drilling accident, the tolerance to uncertainty of pore pressure model is very low. To address the inherent uncertainties of the predrill pore pressure model, so as to reduce potential drilling risk, real-time pore pressure monitoring through leveraging logging while drilling (LWD) data acquired in real-time (Dutta, 2001;Hayes, 2001;Freitag, 2004) is routinely carried out in GoM. To allow automatical updating of the predrill model ahead of bit, Malinverno et al (2004) presented a method to use a Bayesian updating scheme to update seismic based predrill model with newly available data such as sonic log data, pressure measurements, etc.…”

Using Nonparametric Optimal Transformations—Field Applications in the Middle East

“…Pennebaker (1968) presented an empirical correlation that is similar to that of Matthews and Kelly except that K was correlated with depth regardless of pore pressure. More recently, several authors (Wydrinski et al, 1998;Draou and Osisanya, 2000;Badri et al, 2001;Ostermeier et al, 2001;Dutta et al, 2001) suggested methods for fracture gradient calculations for fields located in the Gulf of Mexico (Ostermeier et al, 2001;Dutta et al, 2001), South China Sea (Wydrinski et al, 1998), and North Africa (Draou and Osisanya, 2000;Badri et al, 2001). Christman (1973) introduced an equation that relates the matrix stress ratio to the bulk density of the sediments.…”