Tuesday July 14, 2026  Edition 119

The word “salary” is of Latin origin and means “salt-related” because salt was part of payment for work during ancient times. Salt in petroleum basins is the best cap rock for hydrocarbon pay-zones, but it comes with its complexities as well. In Core Elements #82, I reviewed a two-volume book on salt geology. In this edition, we will look at two recent papers from the AAPG Bulletin related to salt in petroleum basins.

Let’s take a look!

Rasoul Sorkhabi

Editor, Core Elements

Subsiding Gas Fields with Top Salt Seal Rocks

Sub-salt reservoir in The Netherlands (AAPG Bulletin, 2026)

A paper by George Marketos and colleagues in the AAPG Bulletin reports on a computer simulation that investigates the subsiding gas fields capped by salt formations.

About subsiding gas fields:

• Natural gas fields that undergo subsidence have been reported within various basins.
• In most cases, the subsiding gas fields have a top salt cap (seal) rock.
• There should be some relationship between salt movement and surface subsidence; however, surface subsidence in these fields has scarcely been studied.

What they did: The researchers conducted finite elements models to understand how the flow of salt creep affects surface subsidence.

Parameters included in the simulation were:

• Halite creep (fluid laws)
• Young’s module and Poisson's Ratio for the North Sea and underlying stratigraphic lithologies:
  • The Chalk Group (Cretaceous)
  • Triassic-Jurassic sediments
  • Zechstein Salt (Upper Permian)
  • Ten Boer Claystone (Upper Permian)
  • The Rotliegend Sandstone Reservoir (Lower to Middle Permian)
  • Carboniferous Shale

Case study:

• The researchers used a salt-capped gas field in the Netherlands.
• The reservoir lies at a depth of 3,400 meters and is capped by 1,000-meter-thick salt.
• It was productive from 1986 through 2021 and operated by Nederlandse Aardolie Maatschappij (NAM).

What they found:

• The results of numerical simulation compared with field subsidence measurements indicate a time dependence in subsidence evolution, in which the subsidence rates remain relatively constant, while pore pressure depletion rates decrease.
• This mismatch suggests that salt flow can account for part of the time dependent subsidence observed, but it cannot fully explain the field.

The authors suggest the following as candidates for additional mechanisms:

1. Inelastic reservoir rock behavior or stress-strain behavior showing a continuously decreasing hardening rate due to plastic yield or decreasing elastic modulus
2. Creep of the reservoir rock
3. Highly complex pore pressure equilibration behavior inside the reservoir and surrounding rock units

Why it matters: This study is relevant to many hydrocarbon fields capped by salt formation. For example:

• The Gulf of Mexico (America)
• Offshore Angola
• The Santos and Campos basins offshore Brazil

Go deeper: Read the full article here.

Improving Seismic Prediction of Sub-salt Formation

Image of sub-salt and pre-salt (AAPG Explorer)

Salt offers an excellent cap rock for hydrocarbon reservoirs; however, salt also makes seismic imaging of the underlying reservoir difficult.

Jessica de Souza Moreira and colleagues from Brazil published an interesting paper in the AAPG Bulletin that helps with one aspect of this long-standing challenge.

P-wave velocity model: An important factor contributing to seismic interpretation of sub-salt formations is the accurate prediction of P-wave velocity in various lithologies.

Case study:

• The Santos Basin offshore Brazil contains significant oil and gas fields underlying Aptian-age salt.
• The researchers specifically studied the pre-salt Aptian-age carbonates of the Barra Velha Formation.

What they did:

• The researchers used digital image analysis of scanned thin sections, as well as provided data on pore geometry (pore radius and perimeter-to-area ratio) and pore types (vuggy, interparticle, and intraparticle)
• They determined rock porosity on plugs from wells.
• They applied two methods of machine learning:
  • Multiple linear regression (MLR)
  • Elastic Net to predict P-wave velocity using key variables: Plug porosity, mineral compressibility, pore geometry, and pore types.

What they found:

• Interparticle pores correlate with higher P-wave velocities and intraparticle pores correlate with lower P-wave velocities. Vuggy pores had a slight positive trend.
• Initial regression with porosity and mineral compressibility yielded a weak correlation (adjusted coefficient of 0.188).
• Adding pore geometry and pore type greatly improved the model (correlation 0.934).
• Whereas Elastic Net functions better with complex data sets with many parameters, MLR outperformed in the training and blind testing phases, demonstrating higher correlation values and greater predictive accuracy.

Solution to the Mineralogy Puzzle

The U.S. Flag with nine minerals (Rasoul Sorkhabi)

In Core Elements #118, I proposed a mineralogy puzzle in celebration of the Fourth of July and the United States’ 250th anniversary: Name the nine minerals depicted on the U.S. flag.

Geologist Jerry Yunker sent the correct solution:

1. Kyanite
2. Sapphire
3. Lazurite
4. Ruby
5. Quartz
6. Garnet
7. Selenite
8. Hematite
9. Calcite

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