Variogram Analysis Simplified: Part-5: Harnessing Geological Knowledge and Analog Data for Enhanced Variogram Modelling

Sparse data poses a significant challenge for horizontal variogram modelling, especially in subsurface environments, due to the limited spatial information available to infer the continuity of geological features. In this article i have tried to explain why sparse data is problematic and how geological information can be integrated to overcome these challenges.

This article is organized into the following sections to ensure a clear and easy understanding for readers.

1. Why Sparse Data is Challenging for Horizontal Variogram Modelling
2. Key Considerations for Using Analogs in Variogram Range Estimation
3. Depositional Environmental factors and their impact on reservoir continuity/Variogram
4. Factors influencing connectivity and reservoir development
5. Analog data compilations for Variogram range determination
6. Limitations of Using Analogs for Variogram Estimation
7. References

For detailed insight on various other aspects of Variogram please refer to the previous articles mentioned below.

Variogram Analysis Simplified Part-1. Variogram calculation and fitting a variogram model

Variogram Analysis Simplified: Part-2: Variogram Parameters & How they affect the result

Variogram Analysis Simplified: Part-3: Unlock the secrets of spatial patterns with Directional Variogram Analysis

Variogram Analysis Simplified: Part-4: Variogram Signatures & Its Importance

In this current article we will focus on sparse data challenges and how geological information and analog data can be integrated to reduce the uncertainity in the model.

1. Why Sparse Data is Challenging for Horizontal Variogram Modelling:

There are several challenges when handling sparse data, key ones are as follows,

1. Insufficient Data Pairs: A variogram requires calculating the variance of pairs of points at various distances (lags). Sparse data means fewer pairs of points are available to compute these variances, making it difficult to capture reliable spatial relationships.
2. Directional Anisotropy: In horizontal planes, many geological features (e.g., channels, faults) have directional anisotropy, meaning spatial correlation varies depending on the direction. Sparse data limits the ability to detect and model anisotropy accurately.
3. Non-Stationarity: Sparse data provides limited information, making it difficult to account for these large-scale trends or transitions in the subsurface.
4. Uncertainty in Long-Range Continuity: In sparse data environments, there is often a lack of data points at longer distances (larger lags), which makes it hard to accurately define the range of spatial continuity

Depositional environments and understanding key factors will help in modelling horizontal variograms in sparse data condition.

2. Key Considerations for Using Analogs in Variogram Range Estimation

When using analogs for variogram range estimation, several key factors must be considered to ensure accurate and meaningful results. Here are the main points to remember:

1. Geological Similarity: Ensure the analog area has similar geological characteristics to the target area. This includes lithology, depositional environment, and structural features.
2. Scale of Features: The scale of geological features in the analog should match those in the target area. For example, if you’re studying a fluvial system, choose an analog from a similar fluvial system with comparable channel dimensions, sediment types, and flow regimes.

It is always advisable to compare analog information with real data, like major direction with reservoir continuity seen from wells laterally, with Seismic attribute signatures etc.

3. Depositional Environmental factors and their impact on reservoir continuity/Variogram

There are several depositional environments controlled by various factors as represented below. Each of them shows different geological continuity and hence different anisotropic range,

One of the key factor influencing depositional environments is depositional energy, which refers to the strength and dynamics of the natural forces, such as water or wind, that transport and deposit sediments, ultimately shaping the characteristics of the resulting geological formations.

• High-Energy Environments: These environments tend to produce well-sorted, coarser sediments with less variability over short distances. Variograms will generally show long ranges of spatial continuity with smaller nugget effects (short-scale variation).
• Low-Energy Environments: Sediments here are typically finer-grained and may exhibit more variability over short distances, leading to variograms with shorter ranges and higher nugget effects.

Several examples of different energy level depositional enviornments are provided as below for reference.

Various other key depositional environment factors that affect the variogram range are provided in the below table. When considering depositional environments, it is essential to take a holistic approach, as no single factor is sufficient to fully inform the characteristics of the environment; instead, multiple interacting factors such as depositional energy, sediment supply, and transport mechanisms must be considered together for a comprehensive understanding

Following is a generalized idea on specific depositional environments and possible anisotropic continuity due to geological factors.

4. Factors influencing connectivity and reservoir development

While considering the variogram parameters from recent analog key factor like reservoir connectivity in specific geologic scenario and related dimension ratios might be helpful for capturing the proper heterogeneity. Few key information for various specific depositional environments are provided as below.

Deltaic Environment:

Braided fluvial reservoir Environment:

Meandering fluvial reservoirs

Siliciclastic shorelines and barrier island reservoirs

Deep-water marine reservoirs

Carbonate reservoir

5. Analog data for Variogram determination :

Outlined below are several compilations of analog data specifically curated for different depositional environments. These analogs provide valuable insights and serve as reference models, helping to improve our understanding of reservoir properties such as permeability and porosity, particularly in regions where direct measurements are sparse. By comparing these environments, we can better estimate subsurface variations and enhance the accuracy of our geological models.

Above plot provides idea about Sand-body types which shows clear clustering and only limited overlap of dimensions. Two lines recording thickness to width ratios of 1:100 and 1:1000 are shown for reference.

Below details shows the ratio relationships of various depositional elements in various setting from various sources which also can be useful for considering the Major (Length), Minor (Width) and vertical range (Thickness) of any depositional environment.

There are few more Thickness Vs Width Plots provided for different channel group depositional environments for quick understanding of readers.

Utilizing Analog Data for Estimating Permeability Variograms

Given the scarcity of horizontal permeability measurements, we must supplement our estimation of permeability distributions by incorporating analogous data, either inferred from existing patterns or borrowed from similar environments. This approach allows us to fill in the gaps and develop a more comprehensive understanding of the permeability variations. Below analogs help us with guiding the permeability modeling when we are depnding on permeability distribution using variograms.

The following are the horizontal and vertical variograms of petrophysical parameters used to investigate conductivity at hydrogeology sites.

6. Limitations of Using Analogs for Variogram Estimation

• Differences in the scale of geological features between the analog and the target area can affect the accuracy of the variogram.
• The resolution of the data from the analog may not be sufficient to capture the necessary details for accurate variogram modeling.
• Geological processes and environmental conditions can change over time, making it difficult to find a contemporary analog that accurately represents past conditions.
• Human activities, such as land use changes or any other alteration, can alter the geological characteristics of the analog area.
• Various key depositional factors which can help us to understand probable variogram parameters are as follows.

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Please feel free to cite this article as:

Mahapatra, Mahabir Prasad (2024), Variogram Analysis Simplified: Part-5: Harnessing Geological Knowledge and Analog Data for Enhanced Variogram Modelling https://www.dhirubhai.net/pulse/variogram-analysis-simplified-part-5-harnessing-data-mahabir-prasad-6bupf

References:

1. Depositional Environments and Sedimentary Basins – Physical Geology (opentextbc.ca)
2. Deltaic reservoirs - AAPG Wiki
3. Carbonate reservoir - AAPG Wiki
4. Meandering fluvial reservoirs - AAPG Wiki
5. Braided fluvial reservoirs - AAPG Wiki
6. Siliciclastic shorelines and barrier island reservoirs - AAPG Wiki
7. Deep-water marine reservoirs - AAPG Wiki
8. Carbonate reservoir - AAPG Wiki
9. AAPG Datapages/Archives: Dimensions of Paralic Sandstone Bodies
10. Methodology for Integrating Analog Geologic Data in 3-D Variogram Modeling
11. Bahar. A et. al. (2001), Methodology to incorporate geological knowledge in variogram modeling, SPE 68704?
12. Geostatistical Reservoir Modeling (2nd Edition) by?Pyrcz, Michael J., Deutsch, Clayton V.
13. A Method to Integrate Geological Knowledge in Variogram Modeling of Facies: A Case Study of a Fluvial Deltaic Reservoir (scirp.org)
14. Width and Thickness of Fluvial Channel Bodies and Valley Fills in the Geological Record: A Literature Compilation and Classification
15. Geomorphology: An approach to determining subsurface reservoir dimensions
16. Geological Analysis on Three Dimensional Variogram Model

Recommended Reads:

1. Variability of Channel-Belt Dimensions and the Consequences for Alluvial Architecture: Observations from the Holocene Rhine-Meuse Delta (The Netherlands) and Lower Mississippi Valley (U.S.A.)
2. Characteristics of Pleistocene deep-water fan lobes and their application to an upper Miocene reservoir model, offshore East Kalimantan, Indonesia

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