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Address Basin Hydrocarbon Charge Risk

There are three risks that must be addressed prior to drilling a well: trap and seal, reservoir, and hydrocarbon charge. These three components must be in-place for an exploration, or production well to be successful. Assessing the trap and reservoir risks are routine tasks of earth scientists in all hydrocarbon exploration companies.
Seismic, well, and outcrop data provide an abundance of information that explorationists use to determine the quality and viability of these two constituents. Assessing the hydrocarbon charge risk, however, is not a routine in all oil companies. This important function is usually limited to a few geologists or a special group.The reason for the oversight of such an important part of the puzzle is two-fold: terminology and an understanding of the science. As geologists, we have all taken courses in structure, stratigraphy, and petrology. These courses qualify us to make an informed judgment about the trapping configurations and the reservoirs that may be found in a basin. However, not many of us have taken courses in organic geochemistry, rock and fluid dynamics, and thermodynamics. These things are at the core of a hydrocarbon charge risk evaluation. The terms are unfamiliar and we do not have a good handle on these aspects of geoscience.

What Is Basin Modeling?

Traditionally, basin modelling has been used by the oil and gas industry to study the physical and thermal histories of basins. The models lead to an estimation of the timing of hydrocarbon generation and expulsion.

In recent years, however, basin modelling has expanded to include evaluation of secondary migration and trapping of hydrocarbons within basins.

What Is Hydrocarbon Charge?

Simply, and most usefully, hydrocarbon charge is the filling, with hydrocarbon fluids, of traps (prospects) within a basin.

Whereas trapping configurations and reservoir characteristics of a prospect may be very local in scale, the hydrocarbon charge of a prospect may involve a significant portion of the basin. All hydrocarbons that migrate to a prospect, in physical and temporal space, make up the hydrocarbon charge of that prospect. A prospect may be gas charged, oil charged, under-charged, or the area may have no hydrocarbon charge.

Why Should Basin Modelling Be Used?

Here is a very simple scenario. A geologist and geophysicist look at a seismic line in a frontier basin and see an undrilled structure. They are able to map its areal extent and establish an area of closure. No faulting is evident. The risk of trap failure is low. The seismic data gives them some information about the lithology and thickness of the potential reservoir sections. The risk of reservoir failure is moderate. Through a simple volume calculation they determine that the structure can hold a commercial quantity of hydrocarbons. Should a well be drilled? A comprehensive evaluation of the hydrocarbon charge risk using basin modelling could possibly give the answer.

Here is how. The only data available to the basin modeller in the above scenario are the seismic data. From these data we are able to construct a 'guess' about the geologic development of the basin (evidence of erosions, age of sediments, tectonic history, etc.). We call this a conceptual model. We may also determine, using seismic stratigraphy, which seismic intervals represent periods of geologic time favorable for the development of a source rock. We add these 'potential source rocks' to our conceptual model. We make use of modern analogs to add a thermal history to the conceptual model. We submit the conceptual model to a computer program (BasinMod 1-D) that mathematically reconstructs the geologic history of the basin applying common physical laws to the basin as it develops. The results (a mathematical model) tell us about the thermal maturity of the basin (Figure 1: BurHist.gif), if our 'potential source rock' generated and expelled hydrocarbons, when it generated and expelled hydrocarbons, and the type of hydrocarbons generated and expelled. So, returning to our scenario, the basin modelling results may tell us that the basin is thermally immature and any 'potential source rock' has not generated hydrocarbons. They may tell us that the basin is thermally mature and a potential source rock generated and expelled hydrocarbons . . . millions of years before the trap was formed. They may tell us that the early charge, generally oil, occurred prior to the structuring event, however, the structure was in place to receive a late gas charge. Regardless of the results, we have one more piece of the puzzle to use in the overall risk assessment.

What if well data are present?

In the previous scenario, no well data were available. The conceptual geologic model we built for our basin is just one possible model out of many. For example, ages may be incorrect, the amount of eroded section may be wrong, and so on. Our model results, in the previous case, come from creating best and worst case scenarios. Basin modelling is at its peak when well data are present. Why? Because now we can compare our mathematical results to real data taken from the well. For example, we can compare our calculated temperature vs. depth to measured bottom-hole temperatures (Figure 2: TempDep.gif). If the two do not compare favorably, then we know our conceptual geologic model does not adequately represent the real world. In this case, we rethink, and rebuild, our conceptual model and compare the new mathematical results with the measured well data. Depending on the amount of measured data available from the well, the process of comparison-rethink-rebuild may occur several times during the course of an evaluation. This optimization of the mathematical model is called calibration and it is the most important aspect of basin modelling. When we have constructed a conceptual geologic model yielding mathematical results that compare favorably with many independent groups of measured well data, then our conceptual model is an accurate representation of the real basin. We can then use this model as a template for looking at other areas of the basin where wells are not present and maintain a relatively high degree of accuracy. As more wells are drilled in the basin, new data become available, more calibration can occur, and the conceptual model is further refined.

What type of well data can we use for calibration?

Well data used for calibration in basin modelling can be classified into three categories: Physical, Thermal, and Geochemical. Physical data include porosity, permeability, pore pressure, mud weights, etc. Thermal data include temperature data from BHT's, DST's, RFT's and the like. Geochemical data include pyrolysis (Rock-Eval) data, maturity data (Ro, TAI, SCI, etc.), and biomarker ratios.

Are There Any Other Geochemical Data we Can Use in Basin Modeling?

In recent years, new techniques have emerged that tell us something about how and when various types of kerogens convert to hydrocarbons. The branch of science that deals with this is called kinetics. Each kerogen has a unique combination of activation energy levels at which it 'transforms' into hydrocarbons. It serves our purpose to think of the varying energy levels as changes in temperature. As the temperature increases, the energy level increases. Generally, Type I kerogens have a very narrow energy range in which they transform, whereas, Type III kerogens have a much broader range. The importance of kinetics to basin modelling is two-fold. We can better model timing of hydrocarbon generation and we can begin to investigate types and volumes of hydrocarbons generated.

Timing of Hydrocarbon Generation

Historically, basin modellers used thermal maturity based on vitrinite reflectance to estimate the timing of oil and gas generation. We have all heard the statement that a rock is in the 'oil window' or 'gas window'. However, two problems exist here. First, although vitrinite reflectance is a measurable quantity, the relationship between vitrinite reflectance data and oil and gas generation zones is basically empirical and may not be the same in every basin. In fact, 'oil window' and 'gas window' can be somewhat misleading because a source rock can have a vitrinite reflectance value that puts it in the 'oil window' , however it will not be generating oil. Vitrinite reflectance data are a one way street. The vitrinite reflectance of a rock can never decrease. An analogy would be our own chronological age. We begin as children ('immature'), continue through adulthood ('mature'), and wind up in old age ('overmature'). The problem is we can take something that is overmature and put it in an immature surrounding and it is still overmature. To continue with the analogy; we walk into a kindergarten class room and realize the setting is for children. If a 90 year old man is sitting in the class, do the surroundings make him chronologically younger? No, he is still 90. In a like fashion, a source rock with a reflectance of .9 Ro (in the oil window), but at the surface, still has a reflectance of .9 Ro. However, it is not actively generating hydrocarbons.

The second problem in using vitrinite reflectance data to estimate timing of hydrocarbon generation is that it is independent of rock or kerogen type. It is strictly a time-temperature index. A prodelta shale with a Type III kerogen can have the same maturity (oil window/gas window) as a basin floor shale with a Type II kerogen. Does this mean they are both producing hydrocarbons? No!
Kinetics allows us to deal with these two problems to better model the timing of generation of hydrocarbons. First, kinetic properties of kerogens can be measured in a laboratory and, unlike vitrinite reflectance data, tell us the distribution of activation energies at which the kerogens transform to hydrocarbons. Second, the distribution of activation energies are unique to each kerogen. Therefore, two kerogens may behave differently at the same energy level. We return to our earlier analogy of the 90 year old man to demonstrate. The 90 year old man is now joined by a second 90 year old man. The first 90 year old man is weak and stares through feeble eyes as the second 90 year old man jogs into the room. The second man runs around the room several times, does 50 one-armed push-ups, then jogs out. Both men are the same maturity (90 years), but the second man can do more at that maturity than can the first man. The Type III kerogen in the prodelta shale and the Type II kerogen in the basinal shale are at the same maturity, however the Type II kerogen may have completed its transformation to hydrocarbons while the Type III kerogen is only partially complete.

Basin modellers build a specific kerogen, or kerogens, into the conceptual model. We then use the measured kinetic parameters for the kerogens to model the transformation history of each individual source rock. This is a more accurate methodology for the estimation of the timing of hydrocarbon generation than using maturity (Figure 3: CumHc.gif 600x600 pixels).

Type and Volume of Hydrocarbons

Kinetics, coupled with other geochemical measurements, provides the ability to predict the type and volume of hydrocarbons generated. Geochemists have known for some time that different types of kerogens produce different types of hydrocarbons. Generally, a Type I kerogen is a prolific oil producer while a Type III kerogen produces less oil and more gas. Also, generally, the API gravity of a hydrocarbon fluid increases from early generation to late generation. This is because the earliest fluids generated tend to be the heaviest compounds (C15+) and subsequent generative products become lighter until we end with the lightest, methane. Whereas kinetics data can give us the information about the energy level needed to transform the kerogen into oil and gas, or, into the different components (C15+, C6-C15, C2-C5, and C1), Rock-Eval data tells us about the generative potential of this kerogen. The final piece of geochemical data needed to complete the evaluation is total organic carbon (TOC). This tells us how rich, in organic material, is the rock. Simply put, Rock-Eval and TOC data tell us how much we are going to get; kinetics tells us how we are going to get it. These data, along with density information of hydrocarbon fluids, allow the basin modeller to predict the type and volume of hydrocarbons available in different parts of a basin at any time of its development.

Two Dimensional Basin Modelling

One-dimensional basin modelling is most useful in providing information about the timing of hydrocarbon generation and the type of products that are generated. Two-dimensional basin modelling is most useful in providing information about the petroleum system. The ability to model a series of cross-sections in a basin allows, for the first time, some insight into how hydrocarbon fluids migrate and are trapped.

Input data are similar between the two model types. However, two-dimensional modelling requires incorporation of lateral changes in rock, source rock, and thermal properties. Lateral changes in these properties mean that each point along the cross-section undergoes a different burial and thermal history. Porosity of a formation in one area might be reduced due to normal compaction. The same formation a few kilometers away might have a lower permeability which does not allow the pore fluids to escape rapidly. This maintains porosity. Changing depths and thermal properties mean the source rock will generate and expel hydrocarbons at different times. All these factors control when and where hydrocarbons move.

Two dimensional basin modelling also introduces a new calibrant; the present distribution of hydrocarbons in the basin. Each of the critical components, source rock, migration paths, reservoirs, seals, and traps have undergone a geologic history that yields the petroleum distribution we see today. When we explore, we see a snapshot of a dynamic petroleum system. Some of the hydrocarbons we identify from well data as "shows" may be in the process of migrating to another part of the basin. We caught them in transit. Even trapped hydrocarbons are only temporarily stalled. Structural trap failure, seal failure, and reservoir destruction have been well documented. We are lucky when we find a situation where all the components in this dynamic system are, for the present, in place. The results of a two dimensional modelling exercise should show hydrocarbons in traps and reservoirs where they are known to exist. If not, then some aspect of the geologic model is incorrect. The solution is, like one-dimensional modelling, to rethink and rebuild our geologic model. In this way we will develop risk parameters for the basin; not only for hydrocarbon charge, but also for reservoirs, seals, and traps.

For more information on Basin Modelling,
check out the Platte River Associates, Inc. home page.


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