Laminated Sands Analysis Module
Purpose
The Laminated Sands Analysis module allows users to better model thin-bedded sand/shale systems to account for the inability of logging tools to measure with sufficient vertical resolution.
Primary Outputs
The following curves are the primary interpretations made in this module.
| Curve Name | Description |
|---|---|
| VOL_DISP | Volume of dispersed clay |
| VOL_LAM | Volume of laminated clay |
| VOL_STR | Volume of structural clay |
| VOL_SAND | Volume of sand |
| SW_LSA | Sw from laminated sand analysis |
| PHI_SAND_LSA | Corrected porosity from laminated sand analysis |
Screenshot showing the log tracks for the laminated sand analysis.Discussion
In petrophysics, Laminated Sand Analysis (LSA) is a specialized evaluation technique used to characterize reservoirs where thin sandstone and shale layers alternate at a scale finer than the vertical resolution of standard logging tools.
These formations are notorious for the "Low Resistivity Pay" problem.
Because conventional induction tools average the properties of the resistive, oil-bearing sand and the conductive shale, the overall resistivity reading is suppressed, often leading petrophysicists to incorrectly conclude that a zone is water-saturated or non-reservoir
1. The Core Challenge: Scale and Anisotropy
Standard logs typically have a vertical resolution of 2 to 3 feet. In a laminated system, beds may be only inches or centimeters thick. This mismatch creates two primary issues:
- Averaging Effect: Tools like the Gamma Ray (GR) and Resistivity (Rt) measure a "bulk" volume. High clay content in shale laminas drives the GR up and the resistivity down, masking the clean, porous sand
- Electrical Anisotropy: Laminated formations are transversely isotropic. This means resistivity measured parallel to the beds (Rhz) is much lower than resistivity measured perpendicular to them (Rvt). Conventional tools primarily measure Rhz, which is dominated by the conductive shale.
2. Analytical Models
To "unmix" these signals and find the true potential of the sand fraction , petrophysicists use two dominant models:
The Thomas-Stieber Model
This is a volumetric approach that uses a crossplot of total porosity versus shale volume. It assumes that shale can be distributed in three ways: laminated, dispersed (filling pores), or structural (replacing grains).
- In a purely laminated system, the data points fall on a straight line between the "clean sand" and "pure shale" endpoints.
- By plotting the logs on this diagram, the petrophysicist can determine the and the intrinsic porosity of those sand streaks.
Thomas-Stieber crossplot with trendline showing sand/shale porosity trend.The Tensor Resistivity Model
With the advent of triaxial induction tools, we can measure both Rhz and Rvt. The Tensor Model uses these measurements to solve for the true resistivity of the sand layers by treating the formation as a series of parallel resistors.
3. Workflow and Data Integration
A robust LSA workflow typically involves:
- High-Resolution Imaging: Using Borehole Image (BHI) logs to visually confirm lamination and calibrate the sand-shale count.9
- Parameter Selection: Identifying "pure" sand and shale properties from nearby thick beds.
- Saturation Calculation: Once sand resistivity is isolated, standard equations like Archie or Waxman-Smits are applied only to the sand fraction to determine the water saturation.
4. Significance
Laminated sand analysis can increase "net pay" estimates by 20% to 40% compared to conventional methods. It transforms "hidden" reserves into viable production targets, particularly in deepwater turbidite environments and deltaic settings.
Related Insights
DCA: Type well curves
In this video I demonstrate how to generate a well set filtered by a number of criteria and generate a multi-well type curve. Before starting this video you should already know how to load your data and create a DCA project. If not, please review those videos. Type well curves are generated by creating a decline that represents data from multiple wells.
DCA: Loading Production data
In this video I demonstrate how to load production and well header data for use in a decline curve analysis project. The first step is to gather your data. You’ll need: Production data – this can be in CSV, Excel, or IHS 298 formats. For spreadsheet formats you’ll need columns for API, Date, Oil, Gas, Water (optional), and days of production for that period (optional). Well header data – this can be in CSV, Excel, or IHS 297 formats.
Sample data to get started
Need some sample data to get started? The files below are from data made public by the Wyoming Oil and Gas Commission. These will allow you to get started with petrophysics, mapping, and decline curve analysis. Well header data Formation tops data Deviation survey data Well log data (las files) Production data (csv) or (excel) Wyoming counties shapefile and projection Wyoming townships shapefile and projection Haven’t found the help guide that you are looking for?