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The AAPG/Datapages Combined Publications Database
Houston Geological Society Bulletin
Abstract
Abstract: Applications of TheChimneyCube® in Hydrocarbon Exploration
By
dGB- USA
TheChimneyCube® is a new exploration tool (Statoil/dGB patent pending) that reveals vertical hydrocarbon migration paths. Practically, chimney cubes are used as indicators of where hydrocarbons originated, how they migrated into a prospect, and how they spilled or leaked from this prospect and created shallow gas pockets, mud volcanoes or pockmarks at the sea floor. Note that TheChimneyCube® is one product of dGB's object detection, interpretation, and visualization software package, d-Tect. Other seismic features that can be detected include faults, 4D effects, fractures, and salt bodies.
TheChimneyCube® uses a 3D volume of stacked seismic data with other prior information such as the interpreter's insight, well information and other geologic data, to highlight vertical zones of chaotic seismic character, usually associated with gas chimneys (Heggland et al. 2000). Seismic data are input into a neural network and a chimney cube is generated as its output. The chimney data needs to be integrated with regional, well, and prospect data to obtain optimum results.
The predictions of hydrocarbon phase, hydrocarbon charge and
seal effectiveness are critical risk factors in petroleum exploration.
Historically, most resources are devoted to delineating
the structural geometry and reservoir potential of a prospective
lead. Yet, in rank exploration wells, typically over 50% of dry
holes are due to either charge or seal failure. A chimney volume
used in conjunction with other data, can address these key risk
factors. Current applications of TheChimneyCube® include
detecting shallow gas hazards, distinguishing between charged
and non-charged prospects or fault
segments, supporting or
refining basin models, constraining seal risk, and predicting
hydrocarbon phase. Some of these applications are highlighted
in Aminzadeh et al. (2001).
Detect shallow gas hazards: TheChimneyCube® concept was originally developed for this application, following a blowout in the North Sea. Often it is difficult to distinguish lithologic from hydrocarbon related anomalies in the very shallow subsurface. Gas chimneys used in conjunction with shallow amplitude or AVO anomalies have been proved to be a useful tool in detecting shallow gas drilling hazards.
Distinguish between charged and non-charged prospects or
fault
segments: The hydrocarbon system is undercharged in
many hydrocarbon producing basins, such as the Gulf of
Mexico. Thus determining which
fault
segments and reservoir
intervals are receiving preferential hydrocarbon charge is critical.
Chimney cube data have been used in the GOM to high-grade
prospective
fault
segments and reservoir intervals that are
up-dip of the chimneys. Similarly the chimney cube can also distinguish
between faults that are major hydrocarbon pathways
and faults that have not received any hydrocarbon charge.
Support and refine basin models: Chimney cube data can be used to refine 2D basin models, by defining which faults may be more active, and suggesting areas that may be more prone to vertical migration of gas. The basin models can subsequently be modified and thus give more meaningful results. Often chimney data can also provide clues about charge efficiency.
Constrain risk on fault
seal, top seal, and lateral seal: Chimney
data can be a useful tool for distinguishing between leaking and
nonleaking faults, and pinpointing concerns about either
top-seal or lateral-seal risk.
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Prediction of hydrocarbon phase: is critical in areas such as
deepwater Nigeria and the northwest shelf of Australia, where
marketing of gas is much more costly. The traditional tools
employed to predict hydrocarbon phase, including source rock
facies variations and structural timing variations, and geophysical
modeling are often inconclusive. Understanding trap
integrity and its resultant effect on gas chimney character is a
promising approach. Our model, based on Sales (1997) and
O'Brien et al. (1998), divides traps into three main categories:
1. High Integrity Trap
(gas prone) Seal capacity for gas and oil
is greater than closure, thus traps spill oil and
trap
gas.
2. Moderate Integrity Trap
(oil prone) Seal capacity is less than
closure, thus
trap
leaks gas and minor oil.
3. Low Integrity Trap
(dry hole) Seal capacity is insufficient for
an economic accumulation.
The category in which a prospect falls is often difficult to predict
predrill. The character of the gas chimneys is often a key clue to
make this determination. Distinguishing between active and
relict seeps can be significant. By combining chimney cube data
with trap
geometry, most likely hydrocarbon fill (from amplitude
anomalies, pressure data, or regional data), and piston core
or other surface geochemical data, semiquantitative predictions
of the hydrocarbon phase can be made. A summary of the model
is shown in the table below.
Conclusions: Chinmey cube data, when used with fault
cube
and other geologic data (structural model,
fault
seal analysis,
basin models, pressure data, piston core and geochemical data)
and knowledge of the area, has proven to be a useful tool in
quantifying seal and charge uncertainty and detecting shallow
gas hazards.
References
Aminzadeh, F., de Groot, P., Berge, T., and Valenti, G., Using Gas Chimneys as an Exploration Tool, World Oil, 2001.
Heggland, R., Meldahl, P., de Groot, P., and Aminzadeh, F., Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico, American Oil and Gas Reporter, 2000.
O'Brien, et. al., 1998. Evaluating Trap
Integrity in the Vulcan Subbasin,
Timor Sea, Australia, using Integrated Remote-sensing
Geochemical Technologies. In Purcell & Purcell
(ed.) The Sedimentary Basins of Western Australia 2:
Proceedings West Australia Basin Symposium Perth
Western Australia, 1998, p. 237-254.
Sales, J. K., 1997, Seal Strength vs. Trap
Closure - A Fundamental
Control on the Distribution of Oil and Gas, In Surdam,
ed., Seals, Traps and the Petroleum System, AAPG
Memoir 67, p. 57-83.
Table 1. Summary of the model.
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