Why we should NOT use percentages for migration losses

Migration loss is one of the least constrained parameters in petroleum system analysis and modeling. When you ask a geologist how much of the expelled hydrocarbon volumes could have been lost before reaching the traps, the answer typically is a wide range of percentages. I have heard of numbers as low as 20% or as high as 98%. Here I want to argue that is not a very useful way to look at migration loss, and perhaps even the wrong way.

In the follow hypothetical case we have 4 prospects in an area, each having the exact same geology (fetch area, geometry, migration distance, and complexity of the carrier beds, …), with the only exception the estimated expulsion volumes in each of the fetch areas are different as shown below because source variability.

Say we just drilled b and found 500 mmbls of oil in-place, and we estimate 1 billion barrels were expelled in its fetch area. Therefore 500 mmbls (or percentage wise 50 %) were lost in the rock volume between the source and reservoir. What would you then predict the volume of oil charge to be at prospect c, where 500 mmbls were expelled? Is it going to be 250 mmbls, since we would lose 50%, or zero since we would lose 500 mmbls as the two have the same geology?

I ask this question in my training class, while most my mostly explorer students agree it should be zero charge at c, but a few of them may take a bit more to be convinced. If the volume of the rock hydrocarbons migrated through are exactly the same, the lost volume (as residual saturation, and what is trapped in micro or macro traps along the way), required before hydrocarbons reach the trap, should be the same - ie 500 mmbls. For those who still insist on a percentage, I then ask them what would happen if the source rock expels only one barrel.

The implication is really important: In this case, half the number (2) of the traps would not receive charge if we assume the same migration loss volume. But if we used a percentage for efficiency, we would have predicted all traps would have oil. Using percentages would make prospects associated with poor source strength look better, and vise versa, other things being equal.

Since it is very difficult, or impossible, to estimate migration losses, we tend to spend very little time considering it. Based on the above analysis, I recommend running scenarios with different migration losses per unit fetch area to compare prospects. For a given loss volume, a certain number of prospects would not receive charge. More prospects would not receive charge when the assumed loss volume is increased. Prospects with high tolerances to migration loss scenarios tests are safer than those that are too sensitive. This may be an effective way to include migration loss in prospect ranking, even if we cannot accurately quantify this huge uncertainty.     

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,

AM

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.

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. 

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 10%.

 
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.

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.

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.

   

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.

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.

Is Timing of Hydrocarbon Generation Really Important ?

Haven't posted for a while now. Here I would like to challenge another good old petroleum system concept. We have been told that if a trap forms after oil generation is finished, it will not be able to receive charge, or it will receive only gas charge. It is one of the responsibilities of the basin modeler to demonstrate the timing of oil and gas generation, relative to timing of trap formation, perhaps using what is called a petroleum system event chart like this one.

Many of us now realize this is not necessarily true. Actually, if we believed this concept, we might have missed a lot of big important petroleum discoveries.   Below are some examples that contradict the theory:

1) Perhaps a good example is the Bohai Bay, where Phillips Petroleum made a big discovery in 1999 at only about 5000 ft in the Minghuazhen/Guantao formation ( check out the story here). What is interesting to me is that the reservoir was merely deposited about 5 million years ago. This would mean that the oil has to have been generated in the last couple of millions years if we allow the reservoir to be deep enough to have a seal. Right? Well, no, geo-history modeling shows that the Shahejie 3 source rock in the kitchen went through the oil window about 23 million years ago, currently at about 8 km deep and > 300 °C, and the reservoir contains low maturity oil!      

2) In deep water Gulf of Mexico, the Jurassic source rock is currently 35,000 to 45,000 ft deep near many big fields and models show oil generation occurred about 15-10 million years ago and the source is currently in the "gas window" under these fields. The Miocene reservoirs are deposited about 9 million years ago, yet they contains very low maturity oil. 

3) The Foinaven and Schiehallion fields in the West Shetlands basin contain under saturated oil in the Paleocene reservoirs. Again, the basin model shows that oil generation happened late Cretaceous, prior to the reservoir deposition. A "Motel model" (oil had to migrate to a parking lot and wait for trap formation) was used to explain the apparent timing mismatch (Lamers and Carmichael, 1999). Interesting, isn't it. 

I can list more examples, but suffice to say, this seems to become a norm rather than exception. Perhaps we should rethink about how important timing of generation is ? I actually argue that these fields are probably still receiving charge today.

Perhaps the companies which discovered the fields in these examples did not listen to their basin modelers ? How could they have predicted oil in the reservoirs based on the so called petroleum system event chart ? I think at least in the deep water of Gulf of Mexico, certain large oil company may have listened to their modelers and missed most of the action that lead to the discovery of many multi-billion barrel fields there.    

Transient Effects Revisited

Today I had a chance to check out the book "Fundamentals of basin and petroleum systems modeling" by Thomas Hantschel and Armin Kauerauf (Springer-Verlag, 2009). It seems that the transient effects may be still fundamentally misunderstood (and underestimated). Their fig. 3.4a on page 109 (shown below) shows a 1D model going through the deposition, hiatus, and erosion stages. With the assumption that the heat flow at the base of the sediment stays constant at 60 mW/m², the model predicts small  (±5 mW/m²) changes in heat flow in the sediment column. The authors conclude that the transient effect is smaller than that caused by radioactivity within the sediments. You may click on the image to see a version with better resolution.

When evaluating transient effects, it may not be appropriate to assume constant heat flow at base sediments.  You can see from the figure that the forced base boundary is limiting the extent of the transient effects. With a deeper boundary, the heat flow change should be more significant. More importantly, by setting the boundary at base of sediments,  it considers only the process of heating the sediments, but misses the problem that the deposition of the new layer also puts the entire lithosphere out of equilibrium by moving the surface boundary.

The figure below shows this concept. After adding the new sediments, to establish steady state thermal equilibrium again (green curve), temperature, therefore heat flow must change through out the entire lithosphere, not just within the new sediments. Secondly, since the entire lithosphere needs to be heated (not just the sediments) to reach the new equilibrium, it may take much, much longer (lithosphere is typically 10-20 times thicker than the sediments) than heating the sediments alone (see my previous post on this below).

Below is a model with same conditions as the Hantschel and Kauerauf's model, except that it does not assume a constant heat flow at the base of sediments. Rather the temperature at base of the lithosphere at 120 km is fixed at 1330 °C. The transient effects are much stronger compared to the figure at the top.

The following figure shows the predicted heat flow at the base of the sediment column  through time. You see that it is far from constant. From an initial 60 mW/m², basal heat flow decreases to 48 mW/m² at the end of the deposition period, and increases gradually during the hiatus. Then it increases to 72 mW/m² at the end of the erosion period.

This indicates a ±12 mW/m² change over 10 million years with deposition and erosion rates of 250 meters/my, a bit higher than the average deposition rate. However, the deep water of the Gulf of Mexico has deposition rates several times as high, and the heat flow at the base of sediment today is around 35 mW/m², while a steady state heat flow would have been about 50 mW/m².

In recently uplifted parts of North Africa, we see higher heat flows today. Follwing this analysis, it may be concluded that the heat flow prior to the uplift could be 10 mW/m² lower depending on erosion rates. See this post for details.

The basin modeling literature is littered with papers making assumptions of heat flow at the base of sediments independent of deposition/erosion rates. Where sedimentation rates are high, or vary significantly over time, the application of such thermal models can cause significant errors in estimating the maturity and timing of petroleum generation. To be fair to the authors, this was how I used to do it in the 90s. But I have learned my lessons from those who learned before me.

How Long Does a Sedimentation Induced Thermal Disequilibrium Last?

This figure shows how sedimentation rate affects heat flow. It is based on a simple 1D basin model, with a steady-state initial thermal condition. A shale (with typical shale properties assumed by basin modelers) layer is deposited between 100 and 99 million years ago followed by a hiatus till present day.

a fixed temperature of 1300 °C at 120 km below the basement and a fixed 10 °C at the sediment surface provide the boundary condition. In a typical basin with continued and varying deposition rate over 10's of million years, the temperature in the sediment column may be always in disequilibrium.

Two Types of Shale Gas?

Happy New Year!

It seems there are two types of shale gas:

Type 1: Shallow depth (few hundred to 2000m), sorption dominated, TOC critical (7 scf/ton for each 1% of TOC). Maturity important only to improve sorption capacity. May be biogenic origin or mixed origin. Mechanism and therefore estimation methods are similar to CBM.

Type 2: Deep depth (>2000 m), compression (free) gas dominated, porosity critical (20 scf/t for each 1% porosity unit) TOC less important. High maturity very important not only to improve sorption capacity, generate the gas but to reduce liquid volume which reduces sorption and lowers relative permeability. Higher pressure improves scf/ton value for the same porosity.

Shale gas evaluation requires a comprehensive model that takes into account the following: (a) a burial and thermal history model to predict maturity and porosity; (b) the Langmuir sorption model to calculate the amount of sorption gas in the organic matter; and (c) a pvt model to calculate in situ free/compression gas and dissolved gas in the residual oil. In general, the behavior of such a model looks like the following:

These curves are shale gas capacity based on 5% TOC and 1.8% VRo. The curves will also vary with pressure gradient, thermal gradient.

Why you should not use heat flow as input in your basin model

It is still common practice to use heat flow as input to basin models. It is really a bad practice, especially when the heat flow is supplied at the base of the sediment column.  Modelers usually fit a heat flow to match temperature data and then use the same heat flow in the kitchen or even over geological time. The problem is heat flow is a function of deposition rate (so called transient effects) , which changes laterally (the kitchen area usually has higher sedimentation rates), as well as in time. Yes I am talking about basement heat flow. We recommend using 1330 °C at base of lithosphere as the boundary condition. This will automatically determine the heat flow in the kitchen and its variation in time. Here are a couple of examples:

This figure shows heat flow (at base of sediment column) vs time for the Gulf of Mexico deep water areas. The rapid drop in heat flow in Miocene is caused by rapid deposition. By assuming 1330 °C at base of lithosphere, basin models will automatically determine heat flow based on sedimentation rate as well as the conductivity of the rocks being deposited. Faster deposition rates with a lower conductivity rock will depress heat flow more. Heat flow will slowly equilibrate to steady state if there has been no deposition for about 40 million years.

The second example here is from North Africa, which has undergone significant uplift and erosion during the Tertiary. Heat flow calculated based on 1330 °C at base lithosphere shows how heat flow increases during periods of erosion.
You may check out this earlier post to see how this approach can help determine heat flow in the kitchen area without wells. In most situations, vitrinite reflectance data (and other thermal indicators) are not sensitive enough in determining the paleo-heat flow as deeper burial at present day overprints any impact of cooler temperatures in the past.

A caveat of looking at Rock-Eval data from cuttings

I would like to share a recent experience I had working with a Jurassic source rock.

We had hundreds of rock-eval data of the source rock all telling us it is a pretty gas prone source (HI ranging 50 to 250 mg/gTOC). However, in the sub-basin all we have found so far are low GOR oil accumulations. The problem turns out to be due to the way the source rock is typically sampled - from cuttings. Cuttings are usually a mixed bag of samples from over 30 ft or more. In this case (or is it more prevelent?), it does not capture the actual source rock, which are coal seams much less than 2 meter thick each. These coals actually have 50% TOC and HI up to 500 mg/gTOC! Over the 200 meter source interval there may be only 10 meters of net coal. But it is equivalent to a 100 meter thick good type II oil prone source rock!

I can imagine that as long as the source rock is not unitform (how many of them are?), Rock-Eval data from cuttings may tend to downgrade the source rock to some degree, making it less oil prone.

I hope this story may help remind other petroleum system modelers to use some caution when working with "real data". Happy Holidays!

A Look at Shell's Genex model

The recent paper by John Stainforth (Marine and Petroleum Geology 26, 2009, pp. 552–572) gave us a hint of how Shell models hydrocarbon generation and expulsion. I can't help but to comment a bit here. The paper begins by explaining the problems of other models, quote:

"Models for petroleum generation used by the industry are often limited by (a) sub-optimal laboratory pyrolysis methods for studying hydrocarbon generation, (b) over-simple models of petroleum generation, (c) inappropriate mathematical methods to derive kinetic parameters by fitting laboratory data, (d) primitive models of primary migration/expulsion and its coupling with petroleum generation, and (e) insufficient use of subsurface data to constrain the models. Problems (a), (b) and (c) lead to forced compensation effects between the activation energies and frequency factors of reaction kinetics that are wholly artificial, and which yield poor extrapolations to geological conditions. Simple switch or adsorption models of expulsion are insufficient to describe the residence time of species in source rocks. Yet, the residence time controls the thermal stresses to which the species are subjected for cracking to lighter species."

Kinetics: the paper shows the calibration of kinetic models to some "natural data" (his fig. 9) from an unspecified location (calculating a transformation ratio from rock eval data is a tricky business, and we understand big oil companies need to keep secrets). Below are comparisons of the Shell models with some previously published models. Keep in mind that there is always a range of kinetics for each type and natural data tend to have a lot of scatters.

For type I source, oil conversion only, there does not seem to be a big difference between the Shell model, BP model the IFP model. The Bohai model is derived from subsurface data fitted with the Pepper and Corvi (1995) scheme. The Green River model is from Lawrence Livermore labs.

Here is comparison of the type II sources. For oil only conversion (** denotes oil only), the Shell model requires a higher maturity, but it is almost the same as the BP class DE (a more gas prone type II) facies. When I threw in some sub-surface data points I have available, all of the models are reasonable within the variability of data. Note the oil only and the bulk (oil + gas) curves for the BP facies bracket the data set.

Now, lets look at type III source rocks. This is interesting! The IFP kinetics published more than 20 years ago does a better job fitting the Shell data than Shell's own model. Again, if I throw in some of my own real data for a type III source, you can imagine what they look like. Gee, why are my data always more scattered?

Expulsion model: Shell's expulsion model assumes hydrocarbon expulsion is a diffusion process. I like the behavior of the model in terms of the implications on composition of the expelled fluids and the time lag it predicts. I am not sure that we need to compare that with the simple expulsion models some commercial software uses. For expulsion volumes, the choice of a simple threshold model in the commercial software is advantageous that it provides quick answers (volumes and GOR) well within the uncertainty of the data and allows scenario testing and even probabilistic modeling of charge volumes. The Shell model may predict a different position the residual oil may peak in the source, but if you plot some real data, the scatter is a lot bigger than the theoretical differences.

This figure shows retained S1/TOC over an interval in oil window (VRo=1.0-1.2, type II source). We can not really see evidence of any of the expulsion flow mechanisms - Darcy flow or diffusion. The retained oil is probably mostly adsorbed in the organic, as it shows S1 plotted by itself is more scattered. The average 100-120 mg/g TOC is what the simple expulsion model defaults to, which is a good practical approach without dwelling on the exact mechanism. There has to be some free hydrocarbons in the pores as well that may allow Darcy flow.

Some recent data set has cast a serious doubt in all the expulsion models, including diffusion. In the gas producing Barnett shale (an oil source), the total current retained gas is in excess of 100 mg/g TOC. This is several times more than any of the models predict. The shale has been uplifted and no active generation is occurring. 

This paper is good research, and may give us some insights into the processes, but I am not sure I see anything that will change the way we rank prospects which I assume is our job as a petroleum system analyst. The paper lists several theoretical advantages of the Shell model, for example, expulsion during uplifting, the predicted composition, GOR profiles etc. But it seems to me non of these will make the any difference when we apply the models in exploration. His figure 13b predicts type I source rock expelling 1000+ scf/bbl GOR oils at very low maturity (VR<0.7%). Even if it is true, are we really going to try to find some of these oil fields (if it is not clear to you, the volumes expelled before VR<7% is almost nil)? The typical situation is that we may have some Rock eval data from wells drilled on highs we assume are the equivalent source rock in the kitchen. But the uncertainty due to this assumption can be huge. In the Dampier sub-basin of NW shelf Australia, plenty of oil has been found, while all available source rock data show a type III gas prone type. The actual source rock is rarely penetrated. Even if it was, it would be too mature to derive kinetics or even original HI from it. Seismic data will have roughly a 100 m resolution at the depth of the source, so we do not even have a good estimates of its thickness. What is the point of worrying about minor differences in kinetics?

As for expulsion during uplifting, I am not sure we can prove it with geological data. Since there is definitely expulsion before uplifting, additional volumes expelled may be trivial compared to the volumes expelled before cooling, or to the uncertainty in calculating the volumes in an uplifted basin. In addition, the other models actually do still expel some volume because the typical kinetic models do not shut off right away.

The paper's criticism of Pepper and Corvi (1995b) in that they did not show gas expulsion during oil window may not be accurate. As far as I am aware, the Pepper and Corvi source facies are all tuned to give appropriate GOR ranges during oil window, even if it may not be obvious on the mass fraction graphs in the original paper.

The Myth About Kinetics

The general practice among modelers is to use a published kinetic model for a given source rock type or organo-facies to predict the degree of maturation. Within each organo-facies (for example, type II or clay rich marine shales) an variation in kinetics behavior is expected. This is an uncertainty researchers try to reduce. There are several controversies surrounding the use of kinetic models.

• Custom kinetics: This is when we take a sample of an immature source rock and attempt to measure the kinetic parameters in the lab. Some people regard this as an improvement over generic published models. There are a couple of issues with custom kinetics, a) the wells we take samples from are usally on the highs or edges of basins, so the samples may not represent the real source rock in the kitchen. b) When lab derived kinetic models are extrapolated to geological time scales, they tend to not match well data very well. Some of the published models have corrected this effects and hence can account for the differences while custom kinetics may not.
• Compositional kinetics: This is where I think the researchers may have gone a bit overboard. The idea is that we can predict the composition of the fluids beyond just a simple oil and gas classification. The problem is that 1) the source rock facies in the kitchen may not be the same as where we have samples. Typically our well samples are a biased as they are drilled on higher structures rather than the actual kitchen. 2) We usually do not even have a good estimate of the basic source rock parameters, thickness, hydrogen index and TOC, let alone the variations laterally and vertically. The figure below shows a typical source interval with organo-facies changing over time. We also know this would change laterally into the kitchen area which we have no measurements. Our prediction of fluid types would depend on how we account for this vertical and spatial variability, rather than making a sophisticated compositional kinetics model.   

• Different ways to determine kinetics: There are also debates about what is the best way to measure kinetics, e.g. hydrous pyrolysis versus anhydrous, isothermal versus programmed heating rates. My experience with this is that the results may be slightly different, but the differences are smaller than that caused by the uncertainty that we have in extrapolating the samples to source rock kitchens, and uncertainty in estimating temperatures in the kitchen. 

In summary, we are better off thinking about the variability of the source rock in terms of depositional environment and account for the such variations in the source rock model rather than details in the kinetics. Let's face it, it is not possible to accurately predict the detailed composition of the fluids in the trap. The practical approach is actually to run multiple different scenarios and rank our prospects based on the range of predictions.

You see that building a more accurate speedometer will not improve our prediction of the time it would take us to drive from our home to downtown.

Darcy, Percolation or Ray Tracing?

I personally believe that the subsurface migration of hydrocarbons should follow Darcy's law. However, the numerical solution is so time consuming that it is practically impossible to build models at 3D seismic resolution. Anything less is not really geologically realistic. Structure details missed by 2D seismic will significantly affect migration direction. At geological time scales, most people believe that Percolation or Ray tracing is a good approximation of the migration process, and can handle 3D seismic resolution.

However, even 3D seismic resolution is not nearly enough. Vertically migrating oil and gas is easily diverted to a different direction by the existence of a carrier bed just a few inches thick. That is beyond well-log resolution. A petroleum system analyst should be practical about this, and think about where these carriers may exist and test different scenarios. The prospects that can be charged under more scenarios are assigned lower risk and vise versa.

Heat Flow Prediction

How should I go about estimating heat flow when the nearest well is 50 miles away? The common wisdom in basin modeling is to calibrate a heat flow from temperatures in a well, and use the same heat flow in the near by kitchen. The problem is that the kitchen usually has a different heat flow than where we drill wells. Heat flow is a function of mainly crust thickness and sedimentation rate, among a few other variables. So the correct thing to do is to estimate the crust thickness in the kitchen relative to the well location. The effect of the sedimentation rate should be taken care of using a 1D basin model, provided that the model includes the entire lithosphere. This will allow the best prediction of heat flow away from wells - even 50 miles away. Here is an example from NW Australia.

There are a couple of sources of surface heat flow data - one from surface probes, which measures heat flow in the first few meters under sea floor using a transient measurement. The technique has improved over the past few years. The other technique is using seismic bottom-simulating-reflection (BSR). These techniques may be used to provide a sense of relative regional heat flow variations, but should not be used directly in basin models to estimate temperatures at the reservoir or source depths. Aside from large uncertainties, in most offshore regions, the heat flow varies significantly with depth. The surface heat flow fluctuates over time due to short term ( 1 - 100 thousand year scale) changes in sedimentation rates.