Direct hydrocarbon indicators

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Figure 1. Different examples of finding Direct Hydrocarbon Indicators. Credit: GEO ExPro [1]

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. [2] DHIs are mainly common in relatively young, unconsolidated siliciclastic sediments that have large impedance across lithologic boundaries. They have various types of characteristics, which can identified by the relationship between depth and acoustic impedance. DHIs are used in hydrocarbon exploration wells, mainly to reduce the geological risks.

Overview of Data Analysis

Figure 2. Acoustic Properties of Different Reservoir Fluids. Credit: IRIS [2]

Before analysis, we must consider where our potential traps could be, the volume of such traps, as well as our uncertainties and risks. [3]

Rock properties trend with depth

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.[4] 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.[3] Oil sands have a lower impedance than water sands and shales; while gas sands have a lower impedance than oil sands. [2] If encased by shales, especially the gas sands, they would have a higher reflection amplitude due to the opposite polarities.[3] 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

Figure 3. Time-to-depth conversion phases for production. Credit: IRIS [5]

Time-to-depth conversion is important when it comes to exploration, development, and production. Velocity is key, as it measures the relationship between the travel time (seconds) and depth (meters) in our seismic data. Such data is measured in vertical units of two-way travel time; therefore, we can find velocity for our seismic data with the following formula:


Velocity is controlled by the geology of the region, such as age, depth, and lithology; it can be estimated through well logs or seismic processing. This conversion is most importantly used for well planning and modeling for the purpose of production; it is used to validate structural interpretations, get rid of data noise, and perform economic calculations. Before performing the depth conversion, there is a process to go through:

  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. [5]

Identifying traps

Figure 4. Different types of Hydrocarbon traps. Credit: Egy Petroleum [6]
Figure 4.1. Trap Analysis: Leak Point. Credit: Geological Society of London [7]

Structural fold traps

  • Anticlines (as hydrocarbons migrate to anticlines, the traps fill down; if the trap is overfilled, it reaches a synclinal leak point).
  • High side fault blocks
  • Low side roll overs

Stratigraphic traps

  • Sand pinch-outs
  • Unconformity traps

Combination traps (structural and stratigraphic)

  • Deep water channel crossing at an anticline [3]

DHI and AVO analysis

Figure 5. Amplitude variations with offset (AVO) reduce the risk of exploration. AVO anomalies resulted in gas discovery. Credit: Atlas Exploration [8]

Amplitude variation with offset (AVO) is a method in which geophysicists can try to determine the velocity, density, porosity, lithology, thickness, and fluid contents of a rock. In order to make a successful AVO analysis, the fluid content must be known. It is based on the Knott-Zoeppritz equations. [9] One of the main factors for AVO is the seismic response to fluid saturation. [10]

AVO is prone to misinterpretation, as it has a limitation to only using P-wave energy, which leads to failure to provide a unique solution. However, AVO analysis uses a source-generated shear wave energy that allows for the differentiation of gas saturations, as the impact of incident angle is different for different kinds of fluids.[3] The most common misinterpretation is the failure to distinguish a high saturation gas filled reservoir from fizz gas, a reservoir which contains partial gas saturation.

As some DHIs may be challenging to find, it is critical that before interpretation, there is time spent deciding what the rock properties behaviors are and their manifestations. As the acoustic impedance between a reservoir and overlying seal change, we see different amplitude responses:

Bright spots

Figure 6. Expected acoustic impedance trends and responses to hydrocarbons. Credit: GeoScienceWorld [11]

Bright spot is referred to as a spot with a local increase of amplitude associated with hydrocarbon accumulations.[4] In its original implementation, the phrase referred to amplitudes of the Common MidPoint stack; modern implementations are pre-stack (AVO and AVOAz). These amplitude highs can be caused by an increase of reflection coefficient by a gas in the pore space. It is used to identify the increase in amplitude rather than the presence of hydrocarbons, and it is usually greater in unconsolidated clastic rocks.[10] Acoustic impedance is lower in the sands than in the shales, as the pore space is filled with water.[4] As hydrocarbons are added to pore spaces, the velocity and density of the sand decreases. Due to this, the impedance contrast at the top of the sand increases, making the reflection stronger and more negative; thus, it becomes "brighter."

Dim spots

Dim spots are caused by highly consolidated sands with a much greater acoustic impedance than the overlying shale.[4] As seen on Figure 7, the top of the sandstone shows a strong peak. Adding hydrocarbons to the pore space may cause the velocity and density of the sandstone to decrease; however, it won't decrease enough to reverse the polarity of the reflection coefficient.[4] The hydrocarbon reduces the acoustic impedance and the reflection coefficient, thus it produces a "dim spot."[10]

Figure 7. Different amplitude responses on seismic data.

Flat spots

Flat spots represent a hydrocarbon contact seismic response where it is apparently flat. Such contact may be between gas and oil, oil and water, or gas and water. The hydrocarbon reservoir much be thicker than the vertical resolution in order to represent a flat spot. Flat spots are often difficult to find; the edge or base of channels, low angle faults, or processing artifacts can often be misconceived as flat spots. Flat spots may also be caused by low saturated gas in a reservoir.[12]

Phase change

Figure 8. Phase change example. Credit: GeoScienceWorld [11]

Phase change, also known as polarity reversal, occurs when the overlying reservoir has a lower velocity of the reservoir rock.[10] This can occur when a partially consolidated sand becomes wet. This causes the acoustic impedance to become slightly higher than the overlying shale. The top of the sand correlates to a weak positive reflection coefficient. As hydrocarbons are added to the pore space the velocity and density of the sandstone decrease; which also results in a decrease of the acoustic impedance to the point where it's slightly less than the overlying shale.[4] The reflection at the top then changes phase, changing from a peak to a weak trough, which can be seen in Figure 8. In a phase change there is no outstanding amplitude.[11] As seen in Figure 7, identifying phase changes can be challenging, as an interpreter might assume that the top of the sandstone will be represented 
by a continuous reflection.

Gas chimneys

Gas chimneys occurs when a defectively sealed hydrocarbon buildup leaks gas from a deeper level into the subsurface, which is usually along a fault plane. It usually results in a gas-bearing cloud. [13] This gas can cause the overlying rocks, mainly shale with permeable zones, to have a lower velocity. In seismic data, gas chimneys are frequently observed as areas of poor quality data or sags.[10] This can cause high difficulties in structural mapping in depth accurately. These saturations often don't have any economical value; however, they are helpful to find possible leak traps below.[13]

Shadow effects

Shadow effects is caused by the lowering of the velocity in a hydrocarbon buildup. This causes an increase in travel-times in the deeper reflections, which results in a reflection sink. They often occur above and below a bright spot due to the high amplitude processing.[10]

Pitfalls of DHIs

DHIs exploration can be very challenging and can result in failed wells. Some possible reasons for failure may be:

  • 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.[12]
  • 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. [2]
  • Superposition of seismic reflections and tuning effects.
  • Signal contamination due to noise.


  1. GEO ExPro, 2010, Andaman Basin, [1], accessed April 21, 2018.
  2. 2.0 2.1 2.2 2.3 Incorporated Research Institutions for Seismology, 2016, Direct Hydrocarbon Indicators, [2], accessed March 20, 2018.
  3. 3.0 3.1 3.2 3.3 3.4 ExxonMobil, 2005, Data Analysis, [3], accessed April 21, 2018.
  4. 4.0 4.1 4.2 4.3 4.4 4.5 Hart, B., 2011, An Introduction to Seismic Interpretation: AAPG Datapages.
  5. 5.0 5.1 Incorporated Research Institutions for Seismology, 2016, Time-Depth Conversion, [4], accessed April 21, 2018.
  6. Egy Petroleum, Traps and Types of Traps, [5], accessed April 21, 2018.
  7. Bretan, P., 2016, Trap Analysis: an automated approach for deriving column height predictions in fault-bounded traps, [6], accessed April 21, 2018.
  8. Atlas Exploration and Production LLC, 2000, Worldwide Experience in Prospect Generation and New Ventures, [7], accessed March 20, 2018.
  9. Grossman, J., 2003, AVO and AVA inversion challenges: a conceptual overview, [8], accessed March 20, 2018.
  10. 10.0 10.1 10.2 10.3 10.4 10.5 Rost, S., 2006, Amplitude Variation with Offset AVO and Direct Hydrocarbon Indicators DHI, [9], accessed April 21, 2018.
  11. 11.0 11.1 11.2 Abriel, W., Brown, A., 2014, Detection of hydrocarbons using non-bright-spot seismic techniques, [10] accessed April 21, 2018.
  12. 12.0 12.1 Roden, R., Forrest, M., Holeywell, R., Rose & Associates, Marathon Oil Company, 2012, Relating seismic interpretation to reserve / resource calculations, [11], accessed March 20, 2018.
  13. 13.0 13.1 M. Bacon, R. Simm, T. Redshaw, 2003, 3D Seismic Interpretation: The Press Syndicate of the University of Cambridge.

External Links

  • AAPG - [12]
  • Ikon Science - [13]
  • Paradigm Ltd. - [14]
  • Schlumberger Oil Glossary - [15]
  • SEG Wiki - [16]
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Direct hydrocarbon indicators
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