Tampilkan postingan dengan label Petroleum. Tampilkan semua postingan
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Sabtu, 30 April 2016

The common practice in the oil industry is to make source rock maturity maps in terms of vitrinite reflectance (%Ro). However, vitrinite reflectance does not actually tell us to what degree the source rock has converted its generation potential to hydrocarbons. VR is merely a thermal stress (the combined effects of temperature and time) indicator, and a very poor one at that. To know how much of the kerogen has converted to hydrocarbons we not only need to know thermal stress, but also the kinetic behavior of the source rock, which depends on the organo-facies (Pepper and Corvi, 1995).    

This figure shows the fractional conversion (transformation ratio) of kerogen of different organo facies as a function of vitrinite reflectance (thermal stress). We see at 0.8%Ro, each of the standard kerogen facies has experienced very different degree of conversion, 70%, 60%, 40%, 20% and 0% respectively. 

Vitrinite Ro measurements are also not reliable and affected by many things, insufficient readings, suppression due to deposition/diagenetic environments (arguable by pressure as well), subjectivity and experience of the lab personnel, recycled sediments, samples from cavings, etc. In some marine environment, vitrinite macerals are very rare, and in older basins it simply does not exist.

I would like to recommend that we take a good look at one of the most commonly available measurements, hydrogen index (HI), as a maturity indicator. HI decreases from its initial immature value gradually to zero as the kerogen is converted to hydrocarbons. It is a direct measure of how much of the potential of the kerogen has left yet to be converted. Obviously initial values can vary from source rock to source rock, and even within a single source rock facies, but most of that can be filtered out by removing samples with low TOC, and by removing the lower values at each depth/location, as we typically have abundance of samples. This works very well in case of good marine source rocks, (most of the unconventional areas in the US), and especially at higher maturities.

Below is an example of mapping maturity using hydrogen index. This is the Bakken formation in the Williston basin. The color variation based on hydrogen index clearly shows the decrease of HI toward the deeper part of the basin. But the shape of the maturity window do not conform exactly to depth contours as the two more mature areas are also affected by thermal anomalies. 

There are several advantages of using HI as a maturity indicator. Most importantly, it is a direct measure of conversion, so it accounts for the effect of kinetics. Two different source rocks may require different thermal stress to get to the same transformation, but we know exactly how much is left. Most good marine source rocks starts off with an initial HI of about 600 mg/gTOC, so we we see 300, the conversion is about 50%, and when we measure 50, we have over 90% conversion. Secondly, it works well where Ro data is poor or absent - in very rich source rocks, in carbonate source rocks, and old source rocks. It is abundant, inexpensive. The instruments are very accurate and consistent. There is no subjectivity involved.   

Minggu, 10 Januari 2016

The description of a forthcoming specialist conference on basin modeling includes the text:

"BPSM (Basin and Petroleum System Modeling) has become an indispensable tool in frontier basins to identify risk, reduce uncertainty, and identify new potential areas. This technology has become more important over time as a result of increased understanding of processes and the rapid development of computing power. Both the hardware and the software are evolving to quantify more complex processes"

One could only assess the veracity of the first part of this statement by carrying out a survey of companies to see how many use basin modeling as part of their evaluation process and how many consider it "indispensable".  What I think can be said is that, if basin models are "identifying risk and reducing uncertainty", then that isn't showing up in exploration success rates. Industry surveys show that frontier basin success rates have not changed much over the last 20 years, remaining less than 10% for a commercial discovery. I would also dispute whether the ability to "quantify more complex processes" has made a difference - increasing the complexity of a model does not mean increased predictive power. In fact, often the reverse is true because of a greater tendency to fit the "noise" in the system rather than the "signal" (see Nate Silver's excellent book "The Signal and the Noise: The art and science of prediction")

Some of us think that the lack of improvement in the predictive power of basin models is because they only partly address the two things most affecting the chance of a prospect receiving charge: The kitchen yield in relation to the volume of the migration pathway to the trap and the interaction of trap closure height and seal capacity (it doesn't matter whether we are speaking of fault seal or top seal). This is a topic that will be taken up elsewhere but of note are the presentations and papers of Richard Bishop (e.g. Bishop et al., 2015) and two other entries in this blog on traps being filled/not filled and traps leaking and spilling at the same time (in relation to the latter, see also the paper by Sales et al. 1997).

I would like to highlight one other aspect of this discussion: the controls on the occurrence of oil or gas in a trap and our ability (or lack thereof !) to predict it. The intrinsic link between trap fill and phase has already been discussed by Bishop (2015) and Sales et al. (1997). However, in Sales et al. (1997) excess supply of both oil and gas is assumed as precursor to the discussion. Bishop (2015) considers that such excess is implied by the observation that nearly all traps are filled to their spill or leak point. I would argue that not only the total amounts of gas and oil are important here but their relative amounts, i.e, the gas to liquids ratio of the incoming fluid. This, together with the pressure and temperature of the trap and the mutual miscibility of the gas and oil (dependent on their compositions), determines whether the fill of any individual trap is single or dual phase.

Firstly, let's look at the relative masses of oil and gas expelled from the standard Pepper and Corvi (1995) source rock types (cumulative):

Although maturity is often thought of as the strongest control on the amounts of oil and gas expelled by a kitchen, source rock type is a stronger control within most of the maturity range. Furthermore, the amounts of oil and gas retained in the source rock vs. expelled has a major impact on expelled fluid gas to liquids ratio (GLR):

With the default P&C (1995) retained oil and gas amount settings (100 mg/g/TOC and 20 mg/g/TOC respectively) a marine clastic (B) kerogen expels a fluid with GLR of ~ 1100 scfs/bbl at 50% kerogen conversion and ~ 2200 scfs/bbls at full conversion. The GLR for fluvio-deltaic source rocks is very sensitive to the hydrogen index input chosen but for the standard kerogen is ~ 4400 scfs/bbl at 50% and 8800 scfs/bbl at full conversion (as a point of reference, the system-wide GLR for the Taranaki Basin of New Zealand is ~ 10,000 scfs/bbl). 

Note that source rock type and expulsion/retention settings are INPUTS to basin models not OUTPUTS, so we can already see that basin modelling per se may not be good at predicting GLR

What happens when we put these fluids into a migration system (the culmination of which is our target trap) ? First lets look at how the mass/volume of oil vs. gas translates into phase and for this we need to use some standard bubble point and dew point curves: the ones in the diagram below are for UK North Sea oils and gases based on empirical observations (Glaso, 1980, England et al. 2002). There are many factors which affect the position and shape of these curves but that is a topic for another day and they are reasonable for our present purposes. The figure shows how the GLR at 50% kerogen conversion sits in relation to these curves for the P&C (1995) kerogen types:

The symbols here show the phase state of fluids in traps at different depths (assumes hydrostatic pressure) and the black bars highlight the intersection with the dew point/bubble point curves.

If we charge our system from a standard "B" type source (50%) conversion and all those fluids arrive in a trap, we can expect it to contain monophase oil if deeper than about 3100m and dual phase oil and gas if shallower than that. On the other hand, if our charge is from a standard "D/E" source all traps shallower than about 5100m would contain dual phase fluids. If we have a very gas prone type F (upper flood plain or paleozoic coals for example) we will hardly ever encounter anything other than gas. Similarly, if the source is a very oil prone lacustrine "C"  or marine carbonate "A" (not shown in the figure) we will find mostly oil filled traps. Once again there are factors such as migration lag and in-trap alteration which will modify these conclusions in specific circumstances. However, their generality is borne out by the relative frequency of oil vs. gas discoveries in petroleum systems driven by one of the end-member source types. As examples one may cite the oil dominance in offshore Angola or the Bohai Basin of China (C type source) and the gas dominance on the outer Exmouth Plateau of Australia (F type source).

Now let's see how this plays out in a migration plumbing system. The diagram below shows a stylised series of three stacked reservoir/seal pairs with the top seal capacity varying both vertically and laterally for reservoirs 2 and 3 as shown. The actual values are not important here - it is the closure height to seal capacity ratio which matters - but the seal capacity does increase with depth as we might expect as the rocks compact. We are going to inject fluids with varying GLR into the base of the system (this whole exercise is done in Zetaware Trinity). 

For example, if we inject enough of a fluid with a GLR of 3000 scfs/bbl it will begin to migrate vertically at the second trap up-dip and then laterally within reservoir 2 where it leaks again at the most up-dip trap to reach reservoir 3:

Here are the patterns of oil and gas obtained with varying input GLRs (nb: input GLR varies from chart to chart but is held constant during the migration fill process):

Note that we change from expressing GLR as a GOR (scfs/bbl) to a CGR (bbls/MMscf) once it exceeds 3000 scfs/bbl.

We can note several things from this:

1. At low input GLR gas does not displace oil up-dip: it can't do so if the system remains single phase
2. At very high input GLR we do not drop out an oil rim at any realistic depth. However for gas condensates with CGR of about 50 bbls/MMscf or higher oil rims do begin to drop out and may even lead to oil filled traps (the oil found here would be saturated with gas). Commercial oil pools can be (and are) found in dew point systems - although they may also sometimes be present as "nuisance" oil rims to commercial gas pools. The distribution of oil and gas in traps can be complex in all but the most oil or gas dominated systems
4.  Discovering an oil or gas pool or even several does not necessarily define the system as "oil prone" or "gas prone". Compare the patterns of oil and gas occurrence for the 3000 scfs/bbl and 50 bbls/MMscf (= 20,000 scfs/bbl) input cases in the figure. This has not stopped some frontier basins with one or two oil or gas discoveries being labelled as "gassy" or "oily". In reality, a close look at the fluid properties and geochemistry is needed to make this call.

Next let's see what happens when we have a more realistic charge scenario, with the input GLR increasing as maturity of the source increases:

This is for a standard P&C type D/E source rock varying in maturity from a vitrinite reflectance equivalent of 0.85% to 1.6% Ro. At low to moderate maturity the trap fill is dominated by oil but volumes are also low so that only the first few traps in the migration system receive charge (in many cases we will never find these pools because they are deep and with low gas content will not have associated seismic DHIs).

There is naturally more gas in the migration pathway as the source matures. However, notice that even at maturities above 1.3% Ro (the conventional "top gas window") it is possible to find oil. We might, for example, drill the middle trap, find that it contains gas or oil+gas and then deepen the well to find oil. Again, the decision about what to do should hinge on what the fluid property and geochemistry data for the first discovered fluid tell us about the petroleum system. The highest proportion of oil containing traps occur when the source is low mature but this also means fewer trap overall have received charge. If only oil is commercial in our area of interest, we trade off reduced phase risk against an increased risk of finding nothing at all.

This raises the question of charge sufficiency: Bishop (2015) observes that charge is not the limiting factor for trap fill even in systems apparently charged by lean source rocks.  I suspect that the source rock quality and yield has been underestimated in many of these "lean" source rock cases because the true source - often deep in the kitchen - has never been drilled. This would explain why some of the data of Sluijk and Nederlof (1984) represent instances where more hydrocarbons were found in traps than were generated in the corresponding kitchen.

Studies such as those of Sluijk and Nederlof (1984), Biteau et al. (2010) and others cited by Bishop (2015) suggest that the supply of HCs to a trap may commonly be 1 - 2 orders of magnitude higher than the amount needed to fill it. However, we cannot conclude from this that charge sufficiency for an individual trap is never a problem: The next figure shows the distribution of oil and gas in our artifical migration pathway for scenarios in which the kitchen expels 28, 57 and 115 mmbboe/km2. For the purposes of this example we assume no migration losses other than those required to fill each trap in the pathway. In reality, some hydrocarbons will also be lost in reaching the critical saturation threshold in the rocks around the source interval itself and in sub-seismic waste zones

In the low yield case many traps, including those we would be most likely to drill, never receive charge. Although basins without sufficient charge may be rare, for every basin there must be a point at which hydrocarbons run out - equivalent to the maximum migration "range". It might be further from the kitchen than we expect but it must exist. This should be thought of not as a sharp line (even though it is sometimes drawn that way on play chance maps) but rather as a zone of increased probability of drilling a dry hole (nb: this means drilling into an empty trap, not a partly filled trap, since this is statistically unlikely  - see http://petroleumsystem.blogspot.com/2012/08/probability-of-trap-not-filled-to.html).

It is also interesting to consider the impact of phase separation and where it occurs along the pathway: If vertical migration happens early in the sequence phase separation also occurs earlier and the volumetric expansion of gas with reducing pressure means that the same mass of hydrocarbons equates to a much larger volume. This in turn means a greater lateral migration range compared to situations in which most migration happens in deeper carrier beds.

Thus, source rock UEP can be thought of as a kind of "master variable" which controls not only the chance of finding a hydrocarbon filled trap but also - for mixed oil and gas systems particularly - the phase of hydrocarbons found in that trap. Furthermore, and again as discussed in other posts in this blog, UEP is a major control on charge timing. Hence, we see that many things we traditionally expect a basin model to tell us - the chance of a trap receiving charge, the timing of charge relative to trap formation, the phase state of the trapped HCs - are highly dependent on the inputs we choose for the source rock.

We can see also that the pattern of migration depends on top and fault seal capacity of each intermediate trap along the migration pathway: whether it leaks at the crest, leaks through fault juxtaposition or through a non-sealing fault plane. Have we any realistic chance of estimating this for a whole, three dimensional migration pathway (four dimensional if you also expect fault seal capacity to change over time )?  In his recent paper, Bishop (2015) discusses the inherent difficulty in determining whether a single trap is fill to the leak point, whether this is set by top or fault seal or by stratigraphic pinchout. I would add to this the observation that there have been many cases where the extent of compartmentalisation of discovered fields has been badly misread, even after extensive appraisal drilling. If we have trouble working out the plumbing of discovered and multiply drilled fields, what chance have we got of doing it for a whole migration pathway, especially since much of it will have, at best, coverage by 2D seismic ?

We can, I think, deal with this issue in several ways. Firstly, we can run multiple scenarios sampling the input space probabalistically or deterministically (or a combination as suggested by Bishop 2015). Secondly we can use our knowledge of compartmentalisation of discovered fields: There are several extant schemes or algorithms relating reservoir continuity to geological characteristics such as structural type, depositional environment, fault throw vs. net to gross, propensity for shale gauge etc. Nature is fractal so the same logic should apply to migration pathways: perhaps we can use such schemes to assign at least a relative efficiency to a migration pathway. Demaison and Huizinga (1994) referred to this with their low and high "impedance" systems but we can probably address the issue in a more detailed manner now, especially when we have 3D seismic attributes over some or all of the pathway.  I do not believe we can do it deterministically in basin models because we cannot provide such models with inputs of sufficient detail to define specific migration pathways.

Finally, it must be said that the migration "cartoons" used in this post to illustrate concepts take no account of the lack of mixing in many hydrocarbon pools. Calculations of in-reservoir mixing times (see Smalley et al. 2004 for example) suggest that they are often longer than the typical filling time. This was supported by the observations of Stainforth (2004) who argued that compositional grading of petroleum pools is the norm rather than the exception. My own experience includes fields which are clearly unmixed, as reported for the Forties Field (England, 1990) but also some that show remarkable homogeneity over large inter-well distances. The latter cases can arise when a trap has access to charge from multiple directions so that a natural "averaging" process occurs: different migration paths have different lengths and volumes. If we apply the same logic to the intermediate traps along a migration pathway it follows that the migration lag effect on fluid properties and phase - though a fundamental aspect of the migration process - may not always be significant in practice.

With this post I hope to have made the point that the prediction of phase  at the trap level is (a) fundamentally linked to the overall charge risk and therefore subject to similar uncertainties (b) inherently difficult in any mixed oil and gas charged petroleum system. I do not think this is a reason for pessimism or for not attempting to assign a phase risk to our prospects. Rather, given that it is hard to enough to find hydrocarbons in the first place - witness the low success rates in frontier basins - we should not worry about hydrocarbon phase at the trap level. If only one phase is likely to be economic we need to explore in basins where a dominance of that phase is likely, e.g. those likely to host very oil prone or very gas prone source rocks. Migration scenario testing can then help us home in on areas with the best chance of traps filled with the desired phase.

Once we are in a play or basin however, any hydrocarbon discovery is valuable, regardless of the phase: Examination of the fluids will tell us if we are in a fundamentally oil prone, gas prone or mixed system and guide our decision about what to do next - drill up-dip, down-dip, farm down or exit the play. Petroleum geochemistry has a major role to play here as compositional and isotope signatures exist for source type, relative maturity of expulsion, evaporative fractionation and secondary alteration by in-reservoir cracking, biodegradation and water-washing. All of these affect the GLR of trapped fluids.

All comments/criticisms etc. are welcome,



Bishop R.S. (2015). Implications of source overcharge for prospect assessment. Interpretation, 3, 93-107, AAPG

Biteau et al. (2010). The why and wherefores of the SPI-PSY method for calculating the world hydrocarbon yet-to-find figures. EAGE First Break, 28, 53-64

Demaison G. and Huizinga B. (1991). Genetic classification of petroleum systems. AAPG. Bull., 75, 1626-1643

England W.A. (1990). The organic geochemistry of petroleum reservoirs. Org. Geochem., 16, 415-425

England W.A. (2002) Empirical correlations to predict gas/gas-condensate phase behaviour in sedimentary basins. Org. Geochem., 33, 665-673

Glaso O. (1980) Generalised pressure-volume-temperature correlations. SPE 8016, 785-795

Pepper A.S. and Corvi P.J. (1995) Simple kinetic models of petroleum formation: Part 1: oil and gas generation from kerogen. Marine and Petroleum Geology, 12, 291-319 (see also part II and III of this series of papers)

Sales J.K. (1997) Seal strength vs. trap closure - a fundamental control on the distribution of oil and gas. In: Seals, traps and the petroleum system, AAPG memoir 67, 57-83

Sluijk D. and Nederlof M.H. (1984). Worldwide geological experienceas as as systematic basis for prospect appraisal. In: Demaison G and Murris R.J. eds. Petroleum geochemistry and basin evaluation. AAPG Memoir, 35, 15-26.

Smalley et al. (2004). Rates of reservoir fluid mixing: implications for interpretation of fluid data. In: Cubitt J.M., England W.A. and Larter S. (eds.) Understanding petroleum reservoirs: towards an integrated reservoir engineering and geochemical approach. Geol. Soc. Lon. Spec. Pub237, 99-113

Stainforth J.G. (2004). New insights into reservoir filling and mixing processes. In: Cubitt J.M., England W.A. and Larter S. (eds.) Understanding petroleum reservoirs: towards an integrated reservoir engineering and geochemical approach. Geol. Soc. Lon. Spec. Pub. 237, 115-132

See also the blog posts:



  BPSM has become an indispensable tool in frontier basins to identify risk, reduce uncertainty, and identify new potential areas. This technology has become more important over time as a result of increased understanding of processes and the rapid development of computing power. Both the hardware and the software are evolving to quantify more complex processes. - See more at: http://www.aapg.org/events/research/hedbergs/details/articleid/11906/the-future-of-basin-and-petroleum-systems-modeling#2410254-description
  BPSM has become an indispensable tool in frontier basins to identify risk, reduce uncertainty, and identify new potential areas. This technology has become more important over time as a result of increased understanding of processes and the rapid development of computing power. Both the hardware and the software are evolving to quantify more complex processes. - See more at: http://www.aapg.org/events/research/hedbergs/details/articleid/11906/the-future-of-basin-and-petroleum-systems-modeling#2410254-description

Minggu, 07 Juni 2015

In an earlier post, I argued that there may be significant lateral migration within shale reservoirs that can lead to higher maturity fluids produced from lower maturity areas, and even occasionally dry gas production in the oil window. In this post, I would like to propose that shale reservoirs also need seals to work. 

Sedimentary rocks have a wide range of pore sizes. In a conventional reservoir, HC saturation builds up due to higher capillary pressure caused by the buoyancy of the column (Schowalter, 1979). Saturation is highest at the crest of the reservoir. 

In a shale reservoir, there may not be an effective column. The increase in saturation and capillary pressure is caused by generation of hydrocarbons. However, it will also require the presence of tight rock facies (above, below and laterally) to prevent migration out of the shale due to the increased capillary pressure. From MICP studies on shales, we see that shales have a wide range of displacement pressures (Pd), from 200 psi to >10,000 psi. The typical tight facies may have a Pd of ~6,000 psi mercury-air (~320 psi oil-water). After saturating the adsorptive kerogen, the generated HC fluid begins to fill the zones with larger pores (the reservoirs with low Pd) first. As saturation in the reservoirs builds up due to continued generation, capillary pressure increases, as hydrocarbons invade progressively smaller pores. Saturation may reach >50% when the capillary pressure exceeds the Pd of the seal and migration out of the shale begins. Obviously, without the sealing facies, a homogeneous rock cannot retain high saturation.

HC wet or partially HC wet pores may initially build up saturation without increasing capillary pressure.
From the above reasoning, higher Pd for the seals inter-bedded with the more porous zones at various scales ( millimeters, inches, to feet ) leads to higher saturation in reservoir intervals. Some shale plays may not work due to the lack of seals rather than the lack of porosity. Most studies on shale plays to date have focused on the porosity of the reservoirs, which ranges between 5 and 15% typically. If you agree with the above argument, perhaps it is also important to look at the seals (with less than 5% porosity) inter-bedded with, above and below the reservoir zones. 

Zhiyong He, ZetaWare, Inc.

Selasa, 02 Juni 2015

At a recent industry conference a poster summarised aspects of the petroleum systems in a particular basin. The authors noted that some reservoirs contained "dry gas" while others contained "wet gas". The boundary between the two was not defined but it was clear from the context that the distinction reflected the condensate content: gases having more than about 20 bbls/MMscf  of condensate were classified as "wet".

Wet vs. dry gas definitions and terminology can be confusing so I thought it might be worth posting a summary here. Firstly, let's look at the composition of a typical gas condensate:

This one is from the textbook on the phase behaviour of reservoir fluids by Pedersen and Christensen (2007). We can define four groups of compounds: Methane (C1) being the only member of the first group then ethane (C2), propane (C3) and the butanes (normal and iso) making up the remaining "permanent" gases, the "condensate" range with compounds having from 5 to 14 carbon atoms and the "oil" range consisting of compounds with 15 or more carbon atoms. The "condensate" and "oil" ranges are labelled that way because fluids with most of their liquid mass in those carbon number ranges tend to be gas-condensates and oils in the sub-surface respectively. The "permanent" gases are in the gas state at standard surface conditions of 1 atm pressure and 15 C (60 F)

The ideal condensate-gas ratio or "CGR" of a fluid is the ratio of the liquids to the gas species, usually expressed as their respective volumes under standard surface conditions. In the US, "oil field" units are used so that CGR is barrels of condensate per million standard cubic feet of gas (bbls/MMscf). In Europe the units are more commonly cubic metre gas per cubic metre liquids (M3/M3).

When speaking of wet vs. dry gas in the conventional E and P realm, mostly we are referring to the CGR. What is a "significant" CGR depends on the context, particularly the value it may add to a gas development. For example, for an LNG development based on a 5 trillion cubic feet (TCF) resource, a CGR of 10 bbls/MMscf would yield 50 million barrels of condensate (in ideal circumstances).

There are few standard definitions in the literature but (a) the state of New Mexico defines a "gas" well as one producing a fluid with less than 10 bbls/MMscf of liquids  (see item G6) and (b) the Encylcopedia Brittanica on-line defines a wet gas as anything containing more than 2.5 bbls/MMscf.

The standard reservoir engineering definition of a "dry gas" is one that yields ZERO liquids at surface temperature and pressure. On the phase (P vs T) diagram for such a gas, the isotherm of surface temperature does not intersect the phase curve at any point. Another way of saying this is that the cricondotherm for this fluid is lower than surface temperature. The corresponding definition of a "wet gas" is one that will yield some liquids at surface temperature and pressure but there is no pressure at which liquids will begin to condense at reservoir temperature. For a wet gas, the cricondotherm lies somewhere between the surface and reservoir temperature. A gas-condensate is a fluid for which a reduction in pressure at reservoir temperature will, at some point between initial reservoir and surface pressure, cause liquids to drop out.

In the realm of unconventionals, a somewhat different terminology - one used by the natural gas industry -  is prevalent. Gas wetness refers to the content of C2+, i.e. everything except methane is the "wet" stuff. These are called the "natural gas liquids" or "NGL" even though ethane through to the butanes (C2 - C4) are not liquids under standard surface conditions. The condensate fraction (C5+) is a sub-fraction of the NGL and, just to be extra confusing, the NGL are also called the "condensables". This comes about because during natural gas processing methane - the ultimate "dry gas" -  is separated from all other compounds by either cryogenic cooling or by absorption. The condensate fraction of the condensables (the C5+ bit remember !) can also be called "plant condensate" or "natural gasoline". Just for completeness, let me add that the "liquefied petroleum gas (LPG)" that we use in our barbequeue, car or for cooking at the vacation house is propane, butane or a mixture of the two.

Confused yet ? Now enter the geochemists: Gas wetness to a geochemist is defined as the molar ratio or percentage of the "wet" gases to the total of C1 to C5 gases with no consideration of the condensate species at all. . Errr...except that the pentanes - liquids in a cool room but gas in a hot room (37 C/97 F) -  are generally included. Thus, we calculate the wetness of mud gas (gas while drilling) as the sum of C2-C5/ the sum of C1-C5 and express it as a percentage.

The wetness of a gas (geochemical definition) and condensate-gas ratio are clearly related. However, the relationship is specific to a particular petroleum system and also to processes which may have altered the fluid during movement from source to trap and/or in the reservoir. The figure below shows how gas wetness relates to CGR for several different gas-condensate fields, each hosting stacked accumulations. It is obvious that the gases in field 3 have a very different character to those in the other fields. In particular there is almost no change in gas wetness for fluids varying in CGR from ~ 30 to ~ 75 bbls/MMscf. This implies a decoupling of the gas and liquid fractions of the charge with, for example, a fixed amount of liquids being diluted to variable extent by a near fixed composition gas. This in turn might imply different source kitchens and migration routes or some migration fractionation process.

Finally, it is worth noting that the phase behaviour of a gas-condensate system also depends on the composition of the gas and liquids fractions. The figure below shows gas chromatograms for several condensates with different compositions and corresponding to fluids with different CGRs (nb: no chromatogram is available for fluid "F")

Note that condensate E is slightly contaminated with an olefin based synthetic drilling mud.

The pressure at which a gas condensate begins to separate into oil and gas in the subsurface is called the "dew point" or "saturation pressure (Psat)". This pressure is a function of the CGR but also of the mutual miscibility of the liquid and gas components. The most important factor is the composition of the liquids (condensate). If they are very light, they will more easily enter the vapour phase so that we have a higher CGR for a given dew point. Conversely, the heavier the liquids, the lower will be the CGR for the same saturation pressure. The dew point pressure vs. CGR data for the condensates A - G above are shown in this figure (along with some data from the UK North Sea petroleum system and a published emprical correlation for the same area. (nB; gas-liquid ratio - GLR - is displayed - CGR = 1/GLR *1,000,000)

Note the wide variation related to condensate composition. In particular, note that the very light condensates B and C (yellow dots) show low dew point pressures even though they have a high CGR (~ 95 bbls/MMscf, or GLR ~ 10,000). This reflects the high mutual miscibility of the liquids and gases for these fluids. It is often assumed that finding a high CGR gas in a shallow reservoir increases the likelihood of a finding oil in the system. In fact, the reverse is true: If the liquids are light enough to remain in the vapour phase even in high concentration, there are few oil range molecules present. Condensates of type "G" (43 bbls/MMscf) are much more likely to be found in association with oil.

Jumat, 13 Februari 2015

There are currently contrasting views on the way strain is distributed within the lithosphere during rifting and the formation of passive continental margins, with direct implications for the subsidence and heat flow histories of the overlying sedimentary basins, and potentially also for the timing and degree of source rock maturity in these systems.

According to some authors, the asymmetry observed between most conjugate margin pairs (e.g. West Iberia-Newfoundland, East Coast USA-NW Africa, NE Brazil-West Africa and the Southern Australia-Antarctica) results from the activity of low-angle normal faults (detachments), which shift the region of pervasive upper crustal thinning and normal faulting (lower plate) from that of intense lower crust and mantle lithosphere thinning (upper plate; see Rosenbaum et al., 2008 and references therein). A paradoxical observation, nevertheless, is that in most margins the extension measured from normal fault throws appears to be much smaller than that inferred from subsidence and gravity modelling, thus implying ubiquitous upper-plate rift margin settings (the �Upper Plate Paradox�; Driscoll & Karner, 1998; Davis & Kusznir, 2004). 

Pervasive depth-dependent stretching (DDS) is also implied in dynamic models of rifting to explain features such as the deposition of salt over extremely thinned crust (e.g. off western Angola) and the exhumation of continental mantle prior to breakup in magma-poor margins (e.g. the West Iberia Margin; Lavier & Manatschal, 2006; Huismans & Beaumont, 2011). In contrast, results from a recently published kinematic rift model suggest that the crustal structure and subsidence along most passive continental margins can be explained assuming an essentially depth-uniform strain distribution through time (Crosby et al., 2011). Alternative models have also been put forward to explain the apparent deficit of extension in the brittle upper crust, namely by Reston (2005) and Ranero & Perez-Gussiny� (2010), who argue that the amount of extension accommodated in normal faults may have been largely underestimated in earlier studies.

The figures below illustrate the results from two simple experiments in actively explored rift settings: (Figure 1) the North Sea; and (Figure 2) the Angola passive continental margin. The pseudo wells were built from published seismic data and assume a simplified stratigraphy, where the thin black layers correspond to the location of two hypothetical source rocks in each setting. For simplicity all models assume a constant temperature at the base lithosphere of 13300C, and the source rocks use a Type II, marine shale kerogen facies, with an initial TOC of 5% wt and HI of 500 mg/g TOC. The impact of varying the rift model assumptions is then evaluated in terms of the SR�s maturity.

The North Sea comprises a series of rift basins that formed over a sequence of extensional pulses between the Permian-Triassic and Early Cretaceous, interspersed with periods of thermal quiescence, volcanic activity and doming (Ziegler and Cloetingh, 2003). The Pseudo-well in this experiment was built from a NW-SE seismic constrained transect redrawn from Bell et al. (2014), at a location where the inferred total stretching factor (�) is 2; i.e. the crust, or the whole lithosphere, have been stretched to half their initial thickness during rifting (if the rift is assumed instantaneous; McKenzie, 1978). For the purposes of the experiment it is assumed that all extensional deformation took place during the Late Jurassic (160-150 Ma), except in the last scenario, where most thinning occurs during an earlier rift stage, in the Permian (260-250 Ma), in agreement with the published profile (see Bell et al., 2014 and references therein). 

The models show that changing the amount of lithosphere thinning within a reasonable range (black lines), imposing significant differential stretching between crust and mantle, has some impact on the timing/degree of maturity of the deeper, pre-rift SR. This results from differences in the post-rift thermal structure of the basin combined with rapid sediment burial. However, a similar effect is obtained by varying the steady state thickness of the lithosphere by only �10 km, often beyond realistic model constraints, and a greater impact is even predicted when distributing the extensional deformation over several rift events (or varying the duration of rifting). The maturity of the shallower SR is independent of the rift model, although some differences are noticed for variations in the thickness of the steady state lithosphere.


The Angola (deep) passive continental margin formed due to intense stretching during the Early Cretaceous (mostly Berriasian-Aptian) followed by a transition period of thick salt deposition (Aptian) and continental break-up (e.g. Teisserenc & Villemin 1990). The transect shown above is redrawn from Lentini et al. (2010), based on deep seismic reflection and refraction data. At the location of the Pseudo-well the present day thickness of the crust is 8 km, measured between the base of the sediments and the Moho. For the experiment it is assumed that all extensional deformation took place during the Early Cretaceous (145-135 Ma) and that the initial crustal thickness is 32 km (i.e. �crust= 4).
In the margins, where the lithosphere stretches to infinity prior to break-up, depth dependent stretching (DDS) may have a greater impact on the distribution of heat during and following rifting, and thus in the maturity of SR�s. In the experiment above this is observed when varying the amount of stretching in the mantle (�mantle) between a factor of 3 and 4. For higher stretching factors, in this particular setting, the increase in heat flow converges asymptotically. The models also show, however, that similar magnitude effects, or even more pronounced, are produced when varying the thickness of the lithosphere and/or the duration of the rifting events. As in the case of the North Sea experiment the maturity of the shallower SR is independent of the rift model.

In summary, the experiments discussed here show that the implications of assuming conceptually different rift models for the timing and degree of source rock maturity in these settings may be of the same order of magnitude, and thus indistinguishable, from those inherent to the uncertainty in the parameterization of the rift model, such as the thickness of the underlying lithosphere and the age and duration of the rift events. Moreover, it is likely that the maturity of most syn- and post-rift source rocks does not depend significantly on the rift model, but mostly on the rate of post-rift burial. As good practice, these effects should be tested in order to identify the key sensitivities of the basin model, at least within a first order approximation.

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