Tuesday, November 30, 2010

End of the Rainbow

[Note: A version of this blog entry appears in World Oil (December, 2010)]

Over the last 12 months this column has covered topics from shale gas to seismic migration. When it comes to seismic data used for hydrocarbon exploration and production, the pot of gold, so to speak, is the Reflection Coefficient (RC) arising from layer boundaries deep in the earth. The RC indirectly contains information about rock and fluid properties. I say indirectly, because the RC mathematically just depends on velocity and density, but these in turn depend on porosity, pressure, oil saturation, and other reservoir parameters. One key parameter the RC does not even claim to supply is permeability, although it is sometimes estimated using an elaborate workflow involving well logs, core, and seismic (See this blog entry). Another, more direct, seismic permeability estimator is receiving attention these days and that is what I would like to talk about.

We won't be writing down any equations, but reflection coefficients are mathematical in origin. The simplest case involves a seismic wave traveling perpendicular to a layer boundary in an earth characterized by velocity and density. When this wave hits the boundary it splits into reflected and transmitted parts. The size of each is determined by two Boundary Conditions (BC) at the interface, conservation of energy and continuity of amplitude. The two BCs lead to two equations in two unknowns, the reflection and transmission coefficients. Because it is based on a simple model of the earth where each point is described by P-wave speed and density, the RC found in this way depends only on these parameters. Importantly, the RC in this case is just a number, like 0.12, and it is the same number if a 10 Hz wave is involved or 100 Hz.

Now imagine a more complicated view of rock involving such things as porosity, mineralogy, permeability, and pore fluid properties (modulus, density, saturations, viscosity). A theory of waves traveling through such a porous, fluid-saturated rock was developed in the 1950s by Maurice Biot. It is the foundation of poro-elasticity theory and the subject of hundreds of papers making readers worldwide thankful he had a short name. Some odd predictions came from the Biot theory, in particular a new kind of P-wave. The usual kind of P-wave (called fast P) is a disturbance traveling through the mineral frame of the rock, but influenced by the pore fluid. New wave (slow P) is a sound wave in the pore fluid, but influenced by the rock frame. In particular, the slow wave has to twist and turn through the pore space compressing and decompressing fluid and thus has a natural connection to permeability. Before long the slow wave was seen in the lab and the theory set on firm experimental footing.

By the 1960s, researchers figured out how to calculate the reflection coefficient for an interface separating two Biot layers. Since there are three waves in each layer (fast P, slow P, and S) there are 3 reflected and 3 transmitted wave types, meaning we need 6 boundary conditions to solve everything. I won't rattle them off, but there are indeed 6 BCs and the reflection coefficient was duly found, although it is enormously complicated. It would take several pages of small type equations to write it down. As you can imagine, it took a while for people to understand the Biot Reflection Coefficient (BRC), a process by no means completed.

One tantalizing feature of the BRC is its dependence on permeability and pore fluid viscosity. This holds the hope of mapping things of direct use by reservoir engineers, and doing it without punching a hole in the ground. But things are not as easy as that. These important properties are competing with porosity, mineralogy, and other rock properties to influence the BRC. If the BRC were just a number, like the classic RC, then there would be little hope of unraveling all this.

But the BRC is not just a number, it is dispersive (a function of frequency). This means that a low frequency wave will see a different BRC than a high frequency one. It may not seem like this is much help, but there has luckily been a decade or two of research and development on something called Spectral Decomposition (SD). Like white sunlight bent and split by water droplets to form a rainbow of colors, SD pulls apart a broadband seismic trace into its constituent frequencies. This fancy trick has lead to a universe of seismic attributes revealing ever more geological detail in 3D seismic data.

One result of SD applied around the world is a growing realization that seismic data is always a strong function of frequency. We shoot seismic data with a bandwidth of about 10-100 Hz, but looking at, say, the 20 Hz part we see quite a different picture than 30 Hz, or 40 Hz. The main reason for this is a complex interference pattern set up by classical reflection coefficients in the earth. But researchers and companies are also thinking about mining this behavior for the frequency-dependent Biot reflection coefficient.

The BRC is naturally suited to high-porosity conventional sandstone reservoirs. But shale also has some very interesting properties that may be illuminated by the BRC. We now understand that a vast spectrum of rock type goes by the name of 'shale'. These rocks tend to have low (but variable) permeability, and anomalous attenuation affected by fluid viscosity that is dramatically different for gas, condensate, and oil.

There is much work to do in following this rainbow, but unraveling the many competing effects is a next logical step in seismic reservoir characterization. Stay tuned for Biot Attributes.

A fond farewell... With this column my year as a World Oil columnist comes to an end. Other duties call, including a book project titled A Practical Guide to Seismic Dispersion, requiring my full attention for the next few months. I have the deepest appreciation for the WO editorial staff who gave me this exceptional opportunity, and to the many readers who wrote with their thoughts on the column. You can keep up with me, as always, through my Seismos blog. Adios.

Sunday, November 7, 2010

Snap, Crackle, Pop

[Note: A version of this blog entry appears in World Oil (November, 2010)]

In an earlier column about shale gas (May, 2010) I mentioned microseismic data, but had no room there to develop the subject. Here is our chance.

Recall that conventional seismic is the result of generating waves with an active source, such as vibroseis or explosives, at the earth's surface. The waves bounce around in the subsurface and those that return are measured by surface sensors. This is the nature of 3D seismic, a multibillion dollar industry with nearly a century of development behind it. This kind of seismic data is processed using migration to create a subsurface image, as discussed in earlier columns.

Microseismic (MS) data is fundamentally different in three ways. First, the seismic source is a break or fracture in the rock, deep beneath the surface. In response to reservoir operations like fluid production, injection, or a frac job, stresses change and rocks crack, groan, and pop like the rigging of an old schooner. Unlike standard controlled source seismology, in MS each event is a small seismic source with an unknown (x,y,z) location and an unknown source time.

Second, microearthquakes are very weak sources with Richter magnitudes of 0 to -4. To put this in perspective, it would take about 8000 of those little -4 events to match the energy in an ordinary firecracker. Seismic waves from such a source are generally too weak to register at the earth surface due to scattering and absorption by weathered near-surface layers. Consequently, MS data are best recorded by downhole sensors below the weathering zone. This means that, unlike conventional seismic data, MS data acquisition requires monitoring boreholes. Furthermore, with surface seismic data we tell the source when to act and can begin recording data at that time. Since the MS sources can act anytime we need to listen continuously.

Third, what it means to process MS data and the kind of product created is essentially different than conventional seismic imaging. MS is fundamentally and unavoidably elastic, meaning we must deal with both primary and shear (P and S) waves generated by each MS source. A method of detecting P and S arrivals is needed and must be coordinated among all sensors to ensure that picked events are correctly associated with a common MS event. Assuming all of that is done correctly, we are left with a triangulation problem to locate the MS event location in 3D space. While conventional seismic creates a 3D image of the subsurface, microseismic generates a cloud of subsurface points.

There is more. From a long history of global earthquake science, we know that when rock ruptures, seismic waves are generated with different strength in various take-off directions away from the source. In other words, each MS event has not only a time and location, but also a radiation pattern. The radiation pattern is packed with information; it can be inverted to determine an equivalent set of forces that would create the same pattern, and this in turn can be used to determine the nature and orientation of the slippage that caused the MS event. Fractures that open horizontally can be discriminated from vertical ones, N-S trending fractures from those oriented E-W, etc. This is a vast amount of important information.

The heaviest use of MS data to date is frac job monitoring in shale gas plays. Although earlier MS work was done, some of it looking at mapping flow pathways related to conventional hydrocarbon production, shale gas has cranked up the effort by orders of magnitude. Service companies are forming MS divisions, small and nimble new service companies are springing up, and academic research efforts are underway.

So what is this vital information that MS tells us about frac jobs? Certainly we are not interested in the minutiae of a single event. It is the pattern of vast numbers of events that must matter. Shale is so tight that virtually no gas is produced from unfractured rock. Without MS technology it is simply assumed that the frac job has modified the desired volume of reservoir rock. With MS we can plot event locations and associate them with stages of the frac job to confirm the affected rock volume. This feedback, well after well, allows an operator to change practices and procedures to optimize frac coverage and thus maximize gas yield. The best operators and service companies are on a steep learning curve that is resulting in estimated ultimate recoveries increasing dramatically in just the last 2-3 years.

As good as this is, we are still left with an uneasy feeling, like the old joke about a man looking for his keys under a street lamp because that’s where the light is best. Frac job monitoring only happens where the well is drilled. But we also want to know if we are drilling in the best location, in the mythical Sweet Spot. Here, too, MS has the potential to help.

Over the last two decades the seismic industry has made tremendous advances in data quality through acquisition and processing research. In parallel, the entire field of 3D seismic attributes has developed until they number in the hundreds: coherence, curvature, variance, spice, and too many more to name. Many of these afford extraordinary views of the subsurface, including long linear features that appear to be fracture fairways and trends. But as anyone who interprets satellite imagery will tell you, lineaments (as they call them) are darn near everywhere and you only know what they mean by ground checking.

Where can we find ground truth about the many conflicting fracture indicators we get from 3D seismic? Consider a 3D seismic volume in which a horizontal shale well is drilled. The well is fractured and MS data acquired. Frac jobs tend to open up the rock first along pre-existing zones of weakness like natural fractures. By integrating MS data into the seismic volume, we can explore the universe of 3D attributes looking for a connection. Is there an attribute, or combination of attributes, that can highlight the natural fracture trends indicated by the frac job? If so, we have something new in the world: A validated fracture-mapping tool based on 3D seismic.

The game’s afoot.