According to the
Society of Exploration Geophysicists (SEG) Wiki page, direct hydrocarbon
indicators (DHIs) are:
“…an anomalous type of seismic amplitude that may occur
due to the presence of hydrocarbons. They occur due to a change in pore fluids,
which cause a change in the bulk rock’s elastic properties.”
According to the National
Science Foundation:
“Seismic direct hydrocarbon indicators (DHIs) are
anomalous seismic responses caused by the presence of hydrocarbons. DHIs occur
when a change in pore fluids causes a change in the elastic properties of the
bulk rock which is seismically detectable (i.e. there is a “fluid effect”).
DHIs display one or more types of characteristics that are consistent with
hydrocarbons filling pores in a rock matrix.”
DHIs are often based on the different acoustic properties of different reservoir fluids of different densities and bulk moduli, as shown below.
Note that DHIs are basically
seismic amplitude anomalies. Some anomalies are amplitude increases (bright
spots), and others are decreases (dim spots).
DHIs are common in relatively
young, unconsolidated siliciclastic sediments with large impedance across
lithologic boundaries. They are used in exploration wells to help mitigate
risk. To the untrained eye, seismic sections are just a bunch of squiggly
lines, but to a trained interpreter, they can reveal a lot about the rocks in
the subsurface and the fluids in those rocks.
Citing an AAPG article on an
Introduction to Seismic Interpretation, ExxonMobil, and other sources, SEG Wiki
notes:
“The acoustic impedance is determined by the P-wave
velocity and density of a rock; also related to the mineralogy, porosity, pore
fluids, temperature, and pressure. Impedance will change based on
the fluids in the pores, which can be filled with water, oil, or gas. As the
pore space is filled with gas, the Vp lowers while Vs remains unaffected;
therefore, it affects the reflection coefficient at the top and bottom of a
reservoir, which are known as DHIs. In general, sands tend to compact faster
than shales, as they have a higher impedance; yet, water sands have about the
same impedance as shale, meaning the amplitude of reflections are weaker. Oil
sands have a lower impedance than water sands and shales; while gas sands have
a lower impedance than oil sands. If encased by shales, especially the gas
sands, they would have a higher reflection amplitude due to the opposite
polarities. These are differentiable based on their amplitude response. Gas is
compressible, whereas water is not. Therefore, the presence of either will
lower the P-wave velocity. The difference in impedance tends to lower as we go
deeper, as the amplitude response will become less diagnostic. The greater the
impedance between the sand and the shale, the greater the anomaly.
Time-to-depth conversion is very important in seismic interpretation. Velocity measures the relationship between time and depth, with the formula shown below.
Velocity is controlled by
geology, specifically age, depth, and lithology. Time-to-depth conversion can
reduce data noise, validate structural interpretations, and aid economic
evaluations. Before, during, and after conversion, the following steps are
routinely performed: 1) Check available data and its quality. 2)
Consider velocity structure. 3) Determine best method of conversion. 4) Perform
conversion. 5) Check calculated depths and make corrections.
Amplitude Variation with Offset (AVO)
Amplitude variation with
offset, or AVO, is a method of analysis that can lead to DHIs. The goal is to
determine the velocity, density, porosity, lithology, thickness, and fluid
contents of a rock. For AVO to be successful, the fluid type in the rock must
be known. AVO is prone to misinterpretation since it relies heavily on just
P-wave analysis. In AVO analysis, different amplitude responses may be
interpreted as DHIs. These include bright spots, dim spots, flat spots, phase
change, gas chimneys, and shadow effects. It is important to remember that AVO
in itself is not the same as a DHI, but something that can lead to one if other
knowledge supports it.
Bright Spots
Bright spots refer to an
increase in amplitude, which may be associated with a hydrocarbon accumulation.
They can indicate gas in the pore space and are usually greater in
unconsolidated clastic rocks. Bright spots have higher amplitude than
background values. They are a common DHI.
Dim Spots
Dim spots are used to
indicate sandstones and may be caused by highly consolidated sands with a much
greater acoustic impedance than the overlying shale. The
velocity and density of the sandstone will decrease if hydrocarbons are present
in these cases. Dim spots have lower amplitude than background values.
Flat Spots
Flat spots usually suggest
fluid contacts (gas-oil, oil-water, or gas-water). Flat spots are notoriously
difficult to find and may be misinterpreted. Low saturated gas could cause them
as well. They show up as flat spots that cross existing stratigraphy,
contrasting with the surrounding dips.
Phase Change
A phase change, phase or
polarity reversal, occurs “when the overlying reservoir has a lower velocity
of the reservoir rock.” These are also notoriously difficult to find but
often occur at boundaries where seal or caprock meets reservoir rock. The
seismic sections below show interpreted bright spots, dim spots, flat spots,
and phase changes.
Gas Chimneys (an Indirect Hydrocarbon Indicator)
Gas chimneys occur where gas
has leaked up from a lower formation, usually along faults, and result in lower
velocity of the rocks above, usually shale. Aside from identifying leaked gas,
they generally don’t have economic value as DHIs but may be considered indirect
hydrocarbon indicators. See the slide above for an example.
Shadow Effects
Shadow effects may be caused
by hydrocarbons lowering the velocity. They often occur above and below a
bright spot due to the high-amplitude processing.
Pitfalls of DHIs
SEG Wiki lists the following
pitfalls of exploring with DHIs. These underscore the importance of not relying
on DHIs alone since there are situations where DHIs are vulnerable to
misinterpretation.
· Problem
in differentiating the wells with gas buildups and wells with low-saturation
gas (fizz gas), which are considered dry holes. These are principally costly
due to their locations and lack of infrastructure.
· Dry
holes are often interpreted as false positives; which are often found in tight
reservoirs and thick wet sands.
· Low-saturation
gas phenomenon is often related to a break in a reservoir seal and is due to
residual gas that assemble a high amplitude effect similar to a commercial
saturation.
· Flat
reflections may be caused by unusual lithologic variations rather than fluid
contacts.
· Rocks
with low impedance could be mistaken for hydrocarbons, such as coal beds, low
density shale, ash, mud volcano, etc.
· Polarity
of the data could be incorrect, causing a bright amplitude in a high impedance
zone.
· Superposition
of seismic reflections and tuning effects.
· Signal
contamination due to noise.
As a geologist involved in
oil & gas exploration and development, I have looked at many seismic lines,
both uninterpreted and interpreted. I am not very competent in interpreting
seismic lines. I often relied on interpreted sections. Earlier in my career, I
looked at a lot of 2D seismic lines with very small bumps, known as “eyebrows,”
that indicated prospective erosional remnant traps in an unconformity play.
Later, I utilized lots of interpreted seismic lines for geosteering wells. Most
were fairly accurate at predicting geology, even when it was quite complex.
However, the resolution was sometimes not good enough to see smaller faults or
folds, so there are some limitations when a higher resolution is
preferable.
The Growing Use and Growing Success of DHIs
A May 2025 article in AAPG
Bulletin by authors from ExxonMobil notes that exploring for more challenging
geological traps with subtle geophysical responses, such as the now prolific
play offshore Guyana and Suriname, has benefited from DHIs. Ecopetrol’s Sirius
discovery in Colombia is another example. It is projected to potentially triple
the country’s reserves, which had dropped in recent years due to depletion.
ExxonMobil developed a statistical approach guided by machine learning that has
led to significant improvements in predicting the presence of economic
hydrocarbons.
“More recently, the DHI evaluation process adopted
expectation-based metrics and now leverages machine learning for scoring, which
leads to improved accuracy, while mitigating human bias. These adaptations
allow the process to be more broadly applicable across a global portfolio of
prospects. Discernibility, an innovative metric, describes the expectations and
confidence in geophysical observations and helps guide both risking and
resource estimation. A significant challenge to predictability involves reconciling
contradictory information between geological and geophysical observations.
Applying the new integrated workflow, geoscientists integrate geological and
geophysical observations through a Bayesian framework guided by discernibility.
This framework allows for integration of all observations into a single COS
{chance of success} value in a simple, repeatable manner.”
ExxonMobil’s prospect
maturation process is shown below. DHIs come early in the process in the
geologic risking phase. Below the process chart are the three risk groups and
six geologic elements of its nine-element geologic risk evaluation process.
The map below shows
ExxonMobil’s wildcat drilling since 1994, predominantly in offshore plays, and
the heavy correlation with the presence of DHIs.
ExxonMobil revised its
prospect maturation flow and its DHI attribute classification system in 2021,
with the historical and revised DHI attributes shown in the charts below.
Below are ExxonMobil’s
revised quality attributes and data examples of its revised DHI attributes.
ExxonMobil’s system involves
integrating geological chance of success (GCOS) and DHIs to derive DHI
discernibility and integrated chance of success (iCOS).
“In the iCOS framework, discernibility becomes the key
metric for informing how much modification of the GCOS prior by DHI
observations is warranted.”
ExxonMobil’s DHI
discernibility matrix and iCOS integration graph are shown below.
Volumetric parameters and the
weighting of the column height of the fluid contact are shown below.
Below is shown a fluid
contact followed by a DHI interpretation of the Liza prospect area in the
Guyana Basin, followed by four individual well prospects (A, B, C, and D)
evaluated according to the iCOS system.
The following graphs compare
the historical and revised systems of determining COS in the Guyana Basin
prospects via DHI scoring before and after drilling and accuracy analysis.
In July 2025, Barry Friedman
wrote in an article for AAPG, ‘How DHIs Are Driving Giant Discoveries,’ that
the use of AI algorithms combined with DHIs is increasing accuracy and leading
to big hydrocarbon discoveries.
“When coupled with artificial intelligence algorithms,
which automate and speed up identification of DHI characteristics, scientists
can more efficiently and accurately predict potential oil and gas reserves in
four specific areas:
· Identifying
the presence of potential reserves
· Improved
drilling decisions
· Assessing
specific insights of the prospect in terms of trap area, pay thickness and
sometimes porosity
· Reducing
overall geological risk
According to Henry S.
Pettingill, chairman of the Rose and Associates’ DHI Interpretation and Risking
Consortium:
“Approximately two-thirds of the reserves from all
deepwater giant discoveries were found using DHIs.”
He also notes that DHI gas
discoveries have outnumbered DHI oil discoveries because gas is easier to
detect than oil, since oil and water are more difficult to distinguish
seismically. However, in reservoirs with high gas-to-oil ratios (GORs), oil
detection is nearly as good as gas detection. He cites reservoirs in the Gulf
of Mexico, the Kutai Basin in Indonesia, and Guyana as good examples. However,
he also notes that many economic hydrocarbon accumulations are not amenable to
discovery through DHIs if:
“…seismic data quality (imaging) was not good enough to
resolve DHIs or because the rock properties of the sands and shales show
insufficient contrast in acoustic impedance, hence no amplitude anomaly.”
Pettingill notes that DHIs
have significantly improved success rates in frontier basins, but also notes
that they are not in themselves a shortcut to success. They must be used along
with geological interpretation.
“Typical frontier wildcat success rates are 25 percent
without DHIs, and 50 percent and upward with DHIs. In some plays it can be 80
percent.”
DHIs are also being explored for
carbonate reservoirs. Pettingill’s colleague, Rocky Rosen, noted that there are
three areas where DHIs are showing future promise:
· Ocean
bottom node seismic: “It’s very expensive but for imaging-challenged plays like
sub salt, there is nothing better, and the results keep getting better,” said
Roden.
· Full
waveform inversion seismic processing, including elastic FWI (eFWI): “Again,
it’s all about better imaging, and the advancements have been incredible,” he
said.
· Computing
power: “This follows Moore’s Law of integrated circuits, which states that the
semiconductor compute power doubles every two years,” Roden added.
Geologist Teresa Martins of
Galp, who utilized DHIs in its Mopane discovery in the Orange Basin Offshore Basin offshore
Namibia, which I wrote about just last week – see link.
“Right now, the role of DHIs is shifting from being a
‘cherry on top’ to a quantitative, probabilistic input in full-cycle risk
analysis. Instead of being used as isolated ‘bright spots,’ DHIs are
increasingly embedded in workflows that combine geology, petrophysics, and
seismic attributes to generate predictive, statistically backed models of
reservoir presence and quality.”
Basically, improved imaging,
processing, AI integration, and overall exploration modeling is leading to the
ability to see DHIs in prospects where they could not be seen before, which
suggests that more prospects will be amenable to DHI analysis as time goes on.
The ability to better image sub-salt plays, in particular, is a breakthrough.
“Future breakthroughs, {Pettingill} believes, will
include continued resurgence of onshore DHIs, subsalt DHIs, dim spots and other
subtle DHIs, electromagnetic surveying in appropriate setting, advances in
seismic acquisition and processing and, that elephant in the room – AI/ machine
learning.”
References:
How
DHIs Are Driving Giant Discoveries. Barry Friedman. AAPG Explorer. July 2025. How DHIs Are Driving Giant
Discoveries
Direct
hydrocarbon indicators. Society of Exploration Geophysicists (SEG) Wiki. Direct hydrocarbon indicators - SEG
Wiki
Direct
Hydrocarbon Indicators. Sage. Earthscope. National Science Foundation. Direct Hydrocarbon Indicators-
Incorporated Research Institutions for Seismology
Integrated
and improved direct hydrocarbon indicators: A step forward in petroleum risk discrimination.
P. W. Monigle, T. S. Hedayati, and F. J. Goulding. AAPG Bulletin, v. 109, no. 5
(May 2025), pp. 617–636. BLTN24030_proof.pdf
Direct
hydrocarbon indicators (DHI). Hatem Radwan. Slide Share. Direct hydrocarbon indicators (DHI) |
PDF
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