УВ-системы / PetroleumSystems Conford _ eng
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Risking Petroleum Systems
Chris Cornford (Integrated Geochemical Interpretation Ltd.)
Hallsannery, Bideford, Devon EX39 5HE, UK (chris@igiltd.com)
Introduction
Petroleum occurrences in a basin can be viewed from either end of the migration pathway. If viewed from the trap, then accumulations are grouped into PLAYS, where a play comprises a particular combination of reservoir, structure and seal independent of which interval sourced the petroleum. If viewed from the source rock then PETROLEUM SYSTEMS are defined (Magoon and Dow, 1994). Stated succinctly:
One trap type, many source rocks constitutes a Play
One source rock many trap types constitutes a Petroleum System
A somewhat abbreviated definition of the petroleum system approach is “…to account in terms of volumes, composition and process efficiencies for all petroleums expelled from a single pod of mature source rocks” (Figure 1). It is the emphasis on the word ‘efficiencies’ and the understanding derived therefrom that is arguably of greatest benefit to exploration success.
Few studies (e.g. Cornford, 1993) have addressed the application of risk to petroleum systems and the sensitivities of probabilistic predictions to input distributions. In this context, risk is defined as a measure of the uncertainty of the predictions, and may be expressed as probabilities. Two aspects of the petroleum system are susceptible to risk evaluation: the balance between the petroleum volumes generated and volumes discovered (system risk), and the certainty with which the source rock of the petroleum system correlates with the known accumulations (correlation risk). This paper concentrates on the system risk.
Figure 1: Petroleum system efficiencies and risking: oil and gas migrates from a ‘drainage area’ defined by a variety of boundaries, up-dip into a structure.
PETROLEUM SYSTEM
All“AcommercialPetroleumhydrocarbonSystem accountsfrominatermssingleofpodvolumes,of ature composition and processourefficienciese rock for all commercial oil and gas expelled from a single pod of mature source rock”
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CHARGE POLYGONS |
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Probability |
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Terrigenous kerogen (organofacies) polygon |
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RIN |
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T |
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Drainage polygon |
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Oil-mature polygon (oil-prone kerogen) |
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Gas-mature polygon (oil-prone kerogen) |
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Gas-mature polygon (gas-prone kerogen) |
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Marine kerogen (organofacies) polygon |
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OG |
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ER |
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CT |
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SP |
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RO |
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P |
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P50 = 1.33
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OIL |
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0 |
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1.0 |
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Present oil system efficiency (%)
Efficiency = discovered generated
• Volumes - bbls of oil, scf gas (m 3 oil or gas)
• Composition - gas/oil ratio, viscosity, sulphur-rich
• Efficiency = Volume Trapped Volume Generated
BASIN >> SYSTEM(Polygon>> PLAY.cdrin Charge)>> FIELD
Volumetric calculations and simple system efficiencies
The simple calculation of system efficiencies, SE, is based on the definition: |
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SE = in-place volume / volume generated |
Eqn. 1 |
The in-place volumes are based on field estimates, where uncertainty is associated with poor technical data and inadequate calculation techniques together with exaggeration for commercial reasons. These are difficult to risk. The in-place volumes may differ from the charge received by the trap as a result of loss due to leakage, intra-reservoir bacterial degradation, etc. The volume generated, VG, may be calculated from:
VG = a x t x y x (dsr / dhc ) |
Eqn. 2 |
where a = area of mature source rock (km2); t = average thickness of the source rock (km); dsr = source rock density; dhc = hydrocarbon density; y = source rock yield in kg/tonne. The petroleum charge (PC) to individual traps can then be calculated if the expulsion efficiency (EE) and migration efficiency (ME) are known:
PC = VG x EE x ME |
Eqn. 3 |
In terms of a petroleum system, the efficiency is based on the sum of all the charges as a fraction of the total generated within the single volume of mature source rock. Thus in the simple case the expulsion and migration efficiencies need not be known for a system efficiency calculation. If composition is to be used to calibrate the system efficiency, then differential expulsion of gas and oil, and, once phase separation has occurred, selective migration of gas and liquid must be considered.
Volumetric calculations and risked system efficiencies
If the properties of a petroleum system are to be risked, then the range of variation of the parameters defined in Equations 1-3 must be defined in terms of a triangular distribution (min-mean-max), a mean ± standard deviation (m±sd), or a spread of values defined by real data. In terms of the m±sd option, the mean can be arithmetic, geometric or harmonic, and the standard deviation can be applied as a normal or log-normal distribution. Alternatively some sort of skewedness and kurtosis can be imposed. The examples used below are based on the use of the @risk® add-in to Microsoft’s Excel® software.
Figure 2: Risking a typical charge equation means determining the distribution of each of the parameters affecting the volume of ‘in-place’ hydrocarbons.
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The Charge Equation: (Risked) |
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Kg/tonne |
Volume x density = tonne |
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Kg |
TOC x HI x [(Area x Thickness) / ρrock] = kg GENERATED |
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Kg |
Efficiencies = Percentages |
Kg |
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GENERATED x Expulsion Efficiency x Migration Efficiency = CHARGE |
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Kg |
Efficiencies = Percentages |
Kg/ |
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m3 |
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m3 |
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(CHARGE x Entrapment x Preservation) / ρcil |
= ‘In Place’ Hydrocarbons |
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Risking source rock kitchen areas
The area of mature source rock, commonly termed the source rock ‘kitchen’ area, is contained within the early-mature contour on the base source rock surface (Figure 3). Maturity contours are defined in Cornford (1998) in terms of %Ro where they are also equivalenced to generation measured by Transformation Ratios. The early-mature boundary ranges from 0.45%Ro to 0.65%Ro depending on kerogen type.
Figure 3: Constructing a geobathymetric curve
Depth (km)
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
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Vitrinite Reflectance (%Ro) |
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Area between enclosing |
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contours (km |
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x 1000) |
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0.5 |
0.7 |
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40 |
60 |
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KEY: |
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IMMATURE |
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Uncertainty in %Ro |
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Uncertainty in seismic |
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(Outer contour |
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picking |
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uncertain) |
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3.00 |
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Sagging flank |
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EARLY |
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MATURE |
of basin |
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OIL |
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contour |
3.65 |
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OIL |
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Enclosing |
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MID MATURE |
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4.75 |
MATURE |
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4.25 |
LATE OIL |
Graben |
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EARLY |
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GAS |
deeps |
Note: |
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MID GAS |
Change in error |
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elipse with depth |
5.70 |
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LATE |
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GAS |
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6.25 |
Basal |
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contour |
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uncertain |
CROSS-SECTION BASED ON SEISMIC |
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2.0 |
Kitchen area based on base source |
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rock intersection with 0.5%Ro level |
2.5 |
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3.0km = 0.5%Ro |
3.0 |
3.65km = 0.7%Ro |
3.5 |
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4.0 |
4.25km = 1.0%Ro |
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4.5 |
4.75km = 1.3%Ro |
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5.0 |
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5.5 |
5.70km = 2.2%Ro |
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6.0 |
6.25km = 3.0%Ro |
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6.5 |
For a detailed maturity analysis the source rock kitchen is sub-divided into roughly concentric areas based on maturity contours as identified in Figure 3. In complex cases these boundaries will need to be mapped through time. Uncertainty in defining the kitchen area derives from uncertainty in maturity and uncertainty in the geometry of the basin, normally based on seismic picks tied to wells. With commonly available data, the error in maturity dominates the shallower kitchen contours, while errors in seismic picking dominate the deeper. The geobathymetric curve can be sampled as a basis for risking kitchen areas.
Risking source rock thicknesses
The distribution of values for source rock thickness will vary from the megametric uniformity of West Siberia to the kilometric variability of syn-tectonic erosional remnants of the Lower Cretaceous source intervals of offshore central West Africa. In the case of truly uniform thickness, a single value is sufficient, but with syn-tectonic sedimentation it is the basin geometry that determines the distribution of thickness, and distributions are normally strongly log-normal (Cornford, 1993). The optimum method we have developed is to create a histogram of the source rock thicknesses taken from grid sampling of a source rock isopachyte map. For a roughly square basin a 7 x 7 grid will produce 49 sampling points and a reliable histogram. To determine the most economical number of grid nodes, increase the size of the grid until it has little effect on the shape of the histogram.
Risking source rock yields and expulsion
Source rock yields can be measured as the pyrolysis S-2 values (kg hydrocarbon / tonne rock) if immature to early mature samples are available (Cornford, 1998). Histograms of such values may be used as a basis for input to risked calculations (Figure 4), and as a result is strongly dependant of the quality of the company geochemical database (Cornford et al., 1998). This figure shows the distribution of 156 values from 31 well intersections of the Upper Jurassic source rock in a North Sea area, with the S-2 statistics listed in Table 1.
Figure 4: Log-normal distribution of pyrolysis yields showing skewedness partially a function of maturity as determined by Rock-Eval Tmax.
20
Mean (all) = 16.06±10.45kg/tonne (n = 156)
15 |
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Mean immature = 21.5±10.6kg/t (n = 80) |
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Frequency |
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435 - 445 |
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T-max (°C) |
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10 |
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360 - 410 |
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410 - 435 |
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5 |
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445 - 455 |
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455 - 465 |
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465 - 550 |
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Rock-Eval S-2 (kg/tonne)
As can be seen the S-2 pyrolysis yields are only reliable on the immature samples, because with increasing maturity the S-2 values fall as oil and gas are generated to form the S-1 peak. If immature samples are not available, then the original petroleum potential must be estimated. This can be done in a number of ways:
Reconstruct the original S-2 values from maturity trends such as show in Table 1
Calculate original S-2 from visual estimates of kerogen type and measured TOC
If the TOC is known, use the definition of Hydrogen Index as S-2 / TOC to calculate the original S-2 yield.
Table 1: Statistics of maturity effects on Rock-Eval S-2 data.
Maturity (oil) |
Tmax range |
S-2 (kg/tonne) 1 |
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Anomalous2 |
<410 |
17.65±10.23 |
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Immature |
410 – 435 |
21.41±10.56 |
(80) |
Early Mature |
435 – 445 |
9.93±4.64 (53) |
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Mid Mature |
445 – 455 |
10.34±2.61 |
(6) |
Late Mature |
455 – 465 |
7.00 -- (1) |
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Post mature |
>465 |
No data |
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TOTALS |
16.06±10.47 (156) |
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1 Mean ± standard deviation (number of values); 2 below reliable Tmax range – probably oil-stained
It is true that TOC values are less affected by maturity than the S2 yields: while S2 yields can fall to 10% of their initial immature values, TOCs typically half over the full maturity range. Lateral variations in kerogen type can be addressed using the organofacies approach to source rock modelling (Jones, 1987).
Pyrolysis results can also be used to calculate the expulsion efficiency (EE), defined as the fraction of the generated oil that is expelled from the source rock. The concept is that the decrease in Hydrogen Index (S2/TOC) is an index of transformation, and the Production Index (S1/(S1+S2)) is an index of retention. From this:
EE = (1-retained)/generated
With a large homogeneous pyrolysis data set, expulsion efficiencies can be risked (Figure 5).
Figure 5: Calculation of expulsion efficiencies from Rock-Eval pyrolysis data, with histograms displayed as distributions for specific depth intervals.
B a s e - D e p th ( m )
1000 |
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Frequency |
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10 |
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3.0 - 3.5km |
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1500 |
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0.0 |
0.1 |
0.2 |
0.3 |
0.4 |
0.5 |
0.6 |
0.7 |
0.8 |
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1.0 |
2000 |
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Frequency |
60 |
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Expulsion Efficiency (0-1) |
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50 |
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40 |
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3.5 - 4.0km |
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2500 |
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3000 |
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0.1 |
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0.8 |
0.9 |
1.0 |
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Expulsion Efficiency (0-1) |
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Frequency |
60 |
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4.0 - 4.5km |
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3500 |
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4000 |
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Frequency |
80 |
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Expulsion Efficiency (0-1) |
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HI (m g/gTOC) |
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PI(a/(a+ b)) |
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EE(%) () |
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T-m ax (°C) |
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Expulsion Efficiency (0-1) |
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The presentation will review the generalities of Petroleum Systems, and their inputs to E&P decision making. Emphasis is made of the non-linearities encountered on risking a petroleum system in terms of probabilistic system efficiencies, together with properties such as gas/oil ratios used for calibrating the system charge (Figure 6). Recognising and quantifying charges to reservoirs containing mixes of more than one petroleum system are discussed. A recent application of this approach has been quantifying generally well-understood petroleum systems in order to help clients 'mop up' satellite accumulations to existing fields - the economics of which are very attractive given the presence of infrastructure.
Figure 6: Risked petroleum system summary in terms of the total amounts of oil and gas generated, and the implied ultimate system efficiency.
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P5 0 |
= 2.69 |
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P5 0 = 279 x 109m3 |
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P5 0 |
= 51.2 x 1012m3 |
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16 |
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16 |
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(% ) |
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csi D |
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Calibrate system |
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Efficiency = discovered |
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12 |
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bility |
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et re |
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with gas/oil ratio |
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generated |
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|
|
|
|
||||
prob a |
8 |
|
|
|
GAS |
|
|
|
|
|
|
|
|
|
|
8 |
ab ro p |
|
re te |
|
OIL |
|
|
|
|
|
|
|
|
|
TOTAL |
|
|
|
ybilit |
||
4 |
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
||||
D isc |
|
|
|
|
|
|
|
|
|
|
|
|
HYDROCARBONS |
|
|
) (% |
||
|
0 |
300 |
400 |
20 |
40 |
60 |
80 |
100 |
120 |
140 |
0 |
1 |
2 |
3 |
4 |
5 |
0 |
|
|
200 |
6 |
|
|||||||||||||||
|
Oil generated (m3 x 109) |
|
Gas generated (m3 x 1012) |
|
|
Ultimate total system efficiency (%) |
|
|
||||||||||
|
|
|
|
|
|
|
|
References |
|
|
|
|
|
|
|
|
|
|
Cornford, C., 1993. Risked basin efficiency calculations for the Central Graben, North Sea. In: Extended Abstracts of Poster Sessions, 16th International Organic Geochemistry Meeting, Stavanger, K. Øygard, (Ed), pp. 80-86.
Cornford, C., 1998. Source rocks and hydrocarbons of the North Sea. Chapter 11 in: Petroleum Geology of the North Sea, Ed: K. Glennie, Blackwell Sci., Oxford, pp. 376-462
Cornford, C., Gardner, P., Burgess, C., 1998. Geochemical truths in large data sets. I: Geochemical screening data. Organic Geochemistry, vol. 29 (1-3), pp. 519-5
Jones, R.W., 1987. Organic facies. Advances in Petroleum Geochemistry, Vol. 2, J.Brooks and D. Welte (eds), Academic Press, London, pp. 1-90.
Magoon, L.B. and Dow, W.G., (eds), 1994. The Petroleum System from Source to Trap. AAPG Memoir 60, 655pp.