Wednesday, November 28, 2012

How useful is vitrinite reflectance for calibrating paleotemperature ?

As basin modellers we are all taught to calibrate our models to present-day temperatures and then to paleo-temperatures using various paleo-thermometers, the most popular of which is vitrinite reflectance. We can spend a lot of time trying for find heat flow histories that match our vitrinite data. Leaving aside for the moment the fact that vitrinite is a maximum temperature recorder, how useful are the data anyway ? Unfortunately, the answer is often "not very useful at all".

Here are some published data for several wells in the Bozhong sub basin of the Bohai Basin (courtesy of  a literature search by Zhiyong: Guo et al., 2011, Fig. 1b).

Without the line to lead the eye most of us would be hard pressed to see anything but a vague increase with depth here. Imagine trying to infer anything about paleo-temperature or heat flow trends ? Not all data sets are like this of course and one could propose various reasons for the scatter observed in any particular case (cavings, suppression in hydrogen rich organic matter, thrusting etc). But if cases like this occur frequently (and in my experience they do) how do we know when we CAN trust vitrinite data ? Here are some things that make me feel more comfortable about a set of vitrinite reflectance data:

1.       When it shows a good, logical  depth trend, i.e., is not too scattered. The trend of course should be logarithmic not linear with depth, assuming a simple burial and thermal regime (i.e., no major erosions, no major transient thermal effects)

2.       When the source organic matter is humic, is not hydrogen rich and is not embedded in a matrix of H rich amorphous organic matter

3.       When the early diagenetic environment for the section covered is relatively uniform in character, e.g. a long period of deltaic conditions without major alteration in climate or periods of dessication. (moving from coals into lacustrine sediments or vice-versa as we do in many Tertiary rift grabens spells trouble for Ro-depth curves)

4.       When the time section does not span major periods of plant evolution, e.g. from the Cretaceous to the Tertiary covering the displacement of the gymnosperms by the angiosperms, beginning in the tropics (although this doesn’t seem to be a big influence on vitrinite precursors)

5.      When a single experienced analyst has provided the data, when they are the average of multiple measurements, when he or she is looking at a consistent population of macerals and has provided a description of the populations along with the standard deviations and a text statement of confidence

6.     When the trends agree broadly with the thermal history and with other indicators of maturity such as Rock Eval Tmax, spore colour, clay mineral diagenesis, extract paraffin profile etc.

7.     When it is true vitrinite being measured, i.e., we aren’t in the Silurian or older !

8.     When we have some way of controlling the influence of cavings (good mud logger reports, avoid data near casing shoes etc) and of identifying detrital (re-deposited) vitrinite (petrographer experience)

Here are some data for two wells in a area where all of the above criteria are met:

The error bars are standard deviation from multiple measurements. It’s great when it works out like this, but note that the range of values is quite narrow here – we are only just into the conventional oil window.

Once we have vitrinite reflectance data that we are confident in using, we still have to work out what it means for kerogen maturation. I’m no petrographer but as I understand it Ro is the mean or maximum specular reflectance in oil of a subjectively identified vitrinite particle. The intensity of specular reflectance is a function of surface electron mobility which in turn depends on the degree of surface aromatisation (Pi-electrons are more mobile). Ro increases with thermal stress because the degree of aromatisation increases with thermal stress. However, formation of aromatics is not a function of temperature alone: Obviously there has to be cyclic structures to begin with, e.g. the lignitic structural elements of plants, and these will vary in concentration. Also, aromatisation is an oxidation process which can and is accomplished by anaerobic bacteria at low temperature. Pentacyclic plant terpenoid alkenes are aromatised within metres of the surface in ever-wet sediments of the Amazon basin (Lohman, 1988). This early diagenetic aromatisation can be of similar magnitude to that related to thermal stress. Furthermore, it will vary according to many environmental factors. Hence, the starting point for thermally mediated aromatisation (and hence Ro) will also vary throughout a sediment column, causing scatter in depth vs Ro trends. This will be most obvious in terrestrial sequences – like the one in the first example above (and there also good examples from the same basin in Hao et al. 2007)

Happy Modeling,

Guo Y.H., Zou X.H., Ling Y.X, Li, J.P, Wang F.L and Wang, J. (2011) New understandings of hydrocarbon accumulation in Penglai 19-3 oilfield, the Bohai waters. (in Chinesee), Oil and Gas Geology, 6, 327-332

Hao, F., Zou H.Y., Gong, Z.S. and Deng Y.H. (2007), 1-13. (2007). Petroleum migration and accumulation in the Bozhong sub-basin, Bohaid Bay Basin, China: Significance of preferential petroleum migration pathways (PPMP) for the for the formation of large oilfieds in lacustrine basins. Mar. Petrol. Geol, 24, 1-13.

Lohmann F. ( 1988) Aromatisations microbiennes de tritepenes vegetaux.
Ph.D. Thesis, Univ. Strasbourg.

Wednesday, September 26, 2012

Importance of Temperature in Unconventional Plays

It has been observed that some of the best liquid producing areas in the Niobrara shale are associated with thermal anomalies, in both the Power River and DJ basins, the Wattenberg field, for example. Here I would like to propose a few possible reasons behind this observation. Some may be well known but some may have not been so obvious,
  1. The thermal maturity of the Niobrara is relatively low, and areas with higher geothermal gradient would have relatively higher maturity. Higher maturity provides better oil with lower viscosity that makes it easier to flow. Higher maturity also means higher GOR that helps with pressure, ie. better drive and production rate. Higher maturity increases the intra-kerogen porosity generated in the late maturity stage to help retain more oil. ... ... 
  2. Higher present day reservoir temperature also means lower viscosity - if you ever bought motor oil for your car - you understand that. Viscosity is really important in an unconventional play as it has large enough variability to counter the extreme low permeability - yes, Darcy's law still applies .
  3. Temperature may also affect a shale play by influencing interfacial tension (IFT). Lower interfacial tension can help it two ways. It can increase relative permeability, and increase oil saturation, especially in the more water wet parts of the reservoirs (silty zones). Temperature also affects contact angle between the fluids, although the effect is less well studied. 
The Eagle Ford shale does not produce well to the west toward the Mexico border. Temperature could be one of the reasons. At the same maturity, the present day temperature may be lower there compared to the eastern counties.    

Although there are many factors that go into a shale play. As we have plenty of temperature data in typical onshore basins in the US, when evaluating an area, it may be worth while to make a temperature or geo-thermal gradient map. Everything else being equal, ares of higher temperatures may be more favorable. 

Friday, August 3, 2012

Probability of a Trap Not Filled to Capacity

We often estimate how much charge (hydrocarbons) is available to a trap, based on assumptions of source rock potential, maturity and migration losses, etc. The purpose is to see if the trap could be potentially not receiving enough charge relative to its capacity. Aside from huge uncertainties involved in such an exercise, I would argue here that it is relatively rare that a trap would be under charged.

Let's think about a basin that has a mature source rock providing hydrocarbons from the kitchen. Hydrocarbons may migratie up-dip in different directions. Along the fill and spill pathway, the volume may eventually run out, and the last trap is not filled to spill point. All traps further up dip will not receive any charge, and all traps down-dip from this trap are filled to spill point. Let's say there are 10 of these migration fairways and each has 10 traps, there can be only one trap along each fairway that is not filled to spill. Counting all possibilities of charge volumes, the probability of a trap receiving charge but not filled to spill is only one in 10 (10% probability for any trap being half full).
In a system with a good source rock, more of the traps are filled, while with a poor source fewer traps are filled. But in either case, most traps that receive charge are full.

The same is true in a vertically drained system, where traps are limited by seal capacity. Vertically through the stacked reservoirs along each fill-leak path, there can be only one trap that is not filled to seal capacity. All others are either not filled at all, or are filled to seal capacity.

Of course, nature is more complex and we may have a mixture of fill-spill, and fill leak scenarios in a typical basin. Trap geometry and seal capacity are also not constant through geological time. We may often have situations where trap capacity is not limited by structural closure, nor seal capacity, but by fault juxtaposition, etc. The above analysis should be valid in all possible scenarios, including the example mentioned in the earlier post (see below) where a trap can both leak and spill at the same time. Migration cannot continue unless the trap is filled to "capacity".

Richard Bishop, former president of AAPG, gave a presentation titled "Percent Trap Fill and Its Implications", in which he states: "Observations of hundreds fields in many different types of basins and source rocks shows that traps are full to either a leak point or spill point". I agree and perhaps there is a logical reason behind the observation. The implication here is that charge volume estimates may not be such a useful exercise. We should instead focus on the probability whether the trap can be charged, rather than whether it receives enough charge. Moreover, it may be also be useful to try to understand why if a trap is not filled to the structure closure, and its implications in finding accumulations in nearby traps.

Wednesday, July 25, 2012

Can a Trap Spill and Leak at the Same Time ?

There are several scenarios when a trap can both spill and leak from a structure trap. Understanding the physics may help us explain the distribution of oil and gas in a petroleum system and predict what may happen in a prospect. 

1) Spill oil and leak gas at the same time: 

In the scenario of the figure below, when the buoyancy force of the combined column reaches the seal capacity (Pc), additional gas will leak, because increasing gas column will increase buoyancy pressure and cause it to leak. But additional oil will spill as increasing oil column by reducing gas column will reduce buoyancy to below seal capacity and cause oil to spill.

leak and spill at the same time
This happens as long as oil and gas densities are different, and the seal capacity is able to hold if the column is entirely oil, but not if the column is entirely gas. ie.
As you can see, this can happen in a wide range of capillary pressures. For a 200 meter structure closure (H), and the typical subsurface oil density of 0.7g/cc and gas density of 0.3g/cc, any Pc between 199 psi and 85 psi will satisfy the condition.  The range is larger with a heavier oil and drier gas.

2) Leak and spill single phase: 

This maybe a bit controversial, but if leaking through the seal is governed by Darcy flow, the rate of leakage could be slower than rage of charge as seal permeability is orders of magnitude lower than that of the carrier bed. Leakage may happen from a smaller area at the crest of the structure, while charge may come from a larger area around the trap. If the charge rate is greater than the leakage rate, the column can build up and the excess fluid may spill. The charge rate may be limited by the generation rate in the kitchen. We can quantify these using reasonable assumptions of seal permeability, but I will spare you the details here.   

3) Changes through time: 

Many shallow (some are Pleistocene in age) oil fields exist in Tertiary basins, indicating charging can begin at very shallow depth. When the shallow traps begin to receive charge, the seal may be very weak. In the first 1 kilometer of burial, typical mud stone seal capacity is less than 50 meters of oil column, and the seal capacity increases with burial due to compaction and diagenesis, reaching two to three hundred meters at the depth of 3 kilometers

As the burial depth increases, seal capacity may become greater than the trap closure, and and additional change will result in spilling. 

4) Other situations: 

Structure tilting can reduce trap closure, causing a trap that was filled to seal capacity to start spilling. Faulting can also affect the seal. The densities (therefore the buoyancy) of fluids migrating into the trap change through the maturation process and affect seal capacity. Seal capacity is not only a function of the seal's pore throat size, and buoyancy, but is also a function of the interfacial tension between the fluids.  Although lighter fluids mean higher buoyancy pressure, their interfacial tension with water is also higher, meaning higher capillary pressure. Secondary effects, such as biodegradation, water washing, and PT changes can also change fluid properties. 

There is even a chance, that the oil under a gas cap in the above figure can leak at the oil/gas contact directly through the seal on the flank of the structure. This is because oil-water interfacial tension is weaker than gas-water, so it can leak with less buoyancy.

In the subsurface, thermogenic gas always contains some condensate liquid, and oil always contain some dissolved gas. When the leaked or spilled gas migrates to shallower depth, the condensate may drop out and form an oil rim. When the oil migrates up dip to a shallower trap at lower pressure, the dissolved gas may effervesce and form a gas cap.

Wednesday, June 27, 2012

Know What We Don't Know About Surface Temperatures

Surface temperature is the boundary condition for our thermal/heat flow models. The "state of the art" approach to estimating surface temperature involves the following:

1) a function that relates sea level surface temperature to latitude,
2) a carbon dioxide based climate model that relates paleo-mean annual surface temperature through geological time, and
3) a correction of surface temperature with water depth (or elevation)

This sounds very good in terms of having a quantitative model that include all effects of concern. Right? Well, lets look at how good these models are.

The following is the model that relates sea level temperature to present day latitude. The scatter means at any given latitude, temperature is actually not the same due to things that affect climate. We may also take this to mean, that our model in predicting present day surface temperature using this function may have an error bar of ±10 °C. Did you know that?

If you are concerned now, that is only for present day estimate. If we look at the model for paleo climate, by Frakes et all (1992) and Hoffert and Covey (1992). What do you think the error bars are at estimating temperature at Cretaceous time?

The figure below shows the range of errors for mid Cretaceous of one of the models (Barron, 1983). Should I say ±10 °C again? 

Another factor studied by Hoffert and Covey 1992, is that when annual mean surface temperature increases by 10  °C. The temperature at the poles would increase by about 35  °C. So the poles were at 25  °C  in Late Cretaceous. Imagine palm trees. Nice Huh?

Now, Let's look at the effect of water depth. This nice little figure (Beardsmore and Cull, 2001)  shows the effect of water depth on temperature, as cold water is denser and sinks to the bottom, according to NOAA . Deeper water depth means colder sediment surface temperature.  
So the next question is, does this function work back in late Cretaceous? If there was no cold water at the poles, does that mean we had 25 degrees in the deep ocean? Does any of our models account for this? And, are we really sure that Cretaceous was warm? Check out this article and decide for your self.

We use quite a few models based on paleo-climate research to arrive at our surface temperature. Some of them probably have ±10 °C error bars, that's if you believe them. How does this uncertainty impact our calculations of maturity and volumes of oil generated? The rule of thumb is that reaction rate doubles every 10 °C increase in temperature. So you figure.

And I am sure you know how good we are at estimating paleo-water depth.

Wednesday, May 23, 2012

Important Factors in Migration Modeling and Charge Analysis

There is a tendency for researchers to focus on mechanisms and algorithms when it comes to migration modeling. You may have heard debates over Darcy vs IP, and finite element vs finite difference, local grid refinement, etc. at modeling conferences, such as the one later this month in Houston. My opinion is that all these do not matter (compared with other factors we often ignore). The theory of fluid flow in porous media have been figured out more than 60 years ago (Darcy, Hubbert). There is not really any debate that oil and gas will migration up dip as long as there is a carrier bed.

The biggest problem is that many carrier beds are often below seismic resolution. A 5 meter sand is typically not recognizable but will easily divert migration laterally. The presence and absence of carrier beds and their extent and connectivity are basically not observable in most cases and migration modeling based on different assumptions of these will yield very different answers.

The other large uncertainties are paleo-geometries. Typically basin modeling tools back strip layers of sediments to "determine" paleo-strucutre. This more often than not produce the wrong paleo-geometry. This is because (1) basins form before sediments are deposited, the sediments fills the lows in the basin (not the way basin models are constructed), and (2) that depositing 1 km of sediment does not cause basin to subside 1 km, it subsides much less because of isostacy (the mantle material is denser than sediment), and (3) the shape of the basin does not change following exact the shape of the newly deposited sediment layer, because the lithosphere has finite rigidity. For example, building a city like Houston does cause subsidence, but perhaps only a few centimeters. Not taking into account these first order effects will certainly give wrong answers to paleo-migration.

In reality, even present day geometries are often incorrect. Seismic interpretation are often very uncertain. Two interpreters may and often make different structure maps based on the same data. The structure maps can only be treated as "models" themselves, not data. Have you run a migration model using geometries based on 2D seismic and 3D seismic data? Are they not very different? I would even argue, that 3D seismic still is not enough to resolve migration paths exactly, a fault with 5 meter throw may still not be visible on seismic but it will change migration direction.

Seals are as important as carriers - they determine lateral vs vertical migration, yet the parameters that determine seal capacity are uncertain by a factor of 2 to several orders of magnitude. I will perhaps talk about seals in my next post.

Given these large uncertainties, our time is better spend trying to improve the geological model by making and requiring better maps, testing scenarios of carrier presence, extent and taking into account the first order large scale geological processes (isostacy, paleo-bathymetry, flexure, etc) rather than worrying about 3rd or 4th order things like flow mechanisms, and mathematical algorithms. We also should treat our modeling software as tools to help us think about the problems rather than giving us answers.