A Revolutionary Workflow for Petroleum Reservoir Evaluation: Integrating VNIR-LWIR Spectroscopy and Machine Learning

In the field of geological exploration, finding the most critical samples quickly and accurately from hundreds of meters long rock cores has always been a huge challenge. A study on oil and gas reservoirs in Saudi Arabia has demonstrated a promising solution.

In the field of geological exploration, rapidly and accurately identifying the most critical samples from hundreds of meters of core has long been a significant challenge. A study focusing on hydrocarbon reservoirs in Saudi Arabia demonstrates a highly promising solution.

Note:

geological explorationIt refers to a series of comprehensive investigations and research work carried out to investigate the underground geological conditions and explore mineral resources (such as oil and gas, minerals, groundwater, etc.). For the oil and gas field, the core task of geological exploration is to determine favorable areas for oil and gas accumulation and ultimately locate oil and gas reservoirs that are available for exploitation.

oil and gas reservoirIt is a rock formation underground that has storage space (pores, cracks) and fluid permeability (permeability), and oil and gas are stored in these spaces. It is one of the core elements for the formation of oil and gas reservoirs. The most common rock types for oil and gas reservoirs are sandstone and carbonate rocks (such as limestone and dolomite).

To discover, delineate, and evaluate commercially valuable oil and gas reservoirs through geological exploration.

Note:

Geological Explorationrefers to a series of comprehensive investigations and research activities conducted to understand subsurface geological conditions and explore for mineral resources (such as oil, gas, minerals, groundwater, etc.). In the context of the petroleum industry, the core objective of geological exploration is to identify areas favorable for hydrocarbon accumulation and ultimately locate exploitable oil and gas reservoirs.

A Hydrocarbon Reservoiris a subsurface rock unit possessing storage space (pores, fractures) and the ability to allow fluid flow (permeability); hydrocarbons are stored within these spaces. It is one of the core elements for the formation of a hydrocarbon accumulation. The most common rock types for hydrocarbon reservoirs are sandstones and carbonates (e.g., limestone, dolomite).

The purpose of geological exploration is to discover, delineate, and evaluate commercially viable hydrocarbon reservoirs.

Comprehensive lithostratigraphic column chart of exposed lithofacies in the study area (Wadi Daqlah)/ the generalized lithostratigraphy of exposed facies in the study area (Wadi Daqlah)

Spectral Range

The core technology foundation of this study is full band hyperspectral imaging, which covers a wide range of spectra from visible light to long wave infrared:

Visible Near Infrared (VNIR, 400-1000 nm)Mainly sensitive to transition metal elements such as iron ions (Fe ² ⁺) in rocks. By calculating the iron index, the chemical composition changes of rocks can be quickly evaluated.

Short wave infrared (SWIR, 1000-2500 nm)The characteristic absorption valleys of calcite and dolomite in this region exhibit a small but stable offset of approximately 20 nanometers (calcite~2345 nm, dolomite~2325 nm). This difference is the key to quickly and accurately drawing mineral distribution maps.

Medium wave infrared (MWIR, 3-7 μ m)andLong wave infrared (LWIR, 8-14 μ m)Contains more complex molecular vibrational information of carbonate minerals. This study was able to capture subtle differences related to rock crystal structure and grain size that could not be identified solely by SWIR by fusing data from these three infrared ranges, thus successfully distinguishing dolomite with different structures.

「Spectral Range」

The core technical foundation of this study is full-range hyperspectral imaging, covering a broad spectrum from the visible to the long-wave infrared:

Visible-Near Infrared (VNIR, 400-1000 nm): This range is primarily sensitive to transition metal elements such as ferrous iron (Fe² ⁺) in rocks. Calculating an iron index allows for rapid assessment of changes in the rock's chemical composition.

Short-Wave Infrared (SWIR, 1000-2500 nm): A key finding of the study is the consistent, approximately 20-nanometer shift in the characteristic absorption trough positions between calcite and dolomite in this region (calcite ~2345 nm, dolomite ~2325 nm). This difference is crucial for rapidly and accurately mapping mineral distribution.

Mid-Wave Infrared (MWIR, 3-7μm)andLong-Wave Infrared (LWIR, 8-14Mm): These ranges contain more complex molecular vibration information from carbonate minerals. By integrating data from these three infrared intervals, the study was able to detect subtle differences related to rock crystal structure and grain size that are indistinguishable using SWIR alone, thereby successfully differentiating dolomites with varying textures.

Spectral characteristics of dolomite (green) and calcite (blue): The characteristic absorption peak position of dolomite is about 2325nm, and the characteristic absorption peak position of calcite is about 2345nm.

Spectral signature of dolomite (green) and calcite (blue) with characteristic absorption band position around 2325 nm (dashed green line) for dolomite and 2345 nm (dashed blue line) for calcites.

Workflow of Data Analysis Based on Hyperspectral Imaging

Traditional geological sampling can be seen as an "experience driven" model that heavily relies on expert visual observation and subjective judgment. This study proposes a more objective, efficient, and reproducible "data-driven" solution, whose workflow clearly reflects the analytical logic from macro to micro:

Step 1:Scan the entire area and draw mineral mapsThe research team first utilized SWIR band features to quickly scan a 50 meter long core, generating a high-precision mineral distribution map that clearly defined the target area of dolomitization.

Step 2:Identify structural differencesAfter locking in the dolomite region, by integrating the spectral information of SWIR, MWIR, and LWIR, and using principal component analysis (PCA) algorithm, the system can amplify the differences in fine light spectra related to crystal structure and particle size. These differences are indistinguishable to the naked eye.

Step 3:Locate the optimal sampling pointBased on spectral differences, the K-means clustering algorithm automatically divides dolomite pixels into four categories. Subsequently, the system calculates the spectral centers for each category and recommends the core locations closest to these centers as representative sampling points.

「Data-Driven Workflow Based on Hyperspectral Imaging」

Traditional geological sampling can be considered an 'experience-driven' model, heavily reliant on experts' visual observation and subjective judgment. This study proposes a more objective, efficient, and reproducible 'data-driven' solution. The workflow clearly demonstrates an analytical logic progressing from macro to micro:

1. Full-Scene Scanning and Mineral Mapping:The research team first utilized SWIR spectral features to rapidly scan the 50-meter core, generating a high-precision mineral distribution map that clearly delineated the target dolomitized zones.

2.Identifying Textural Differences: After identifying the dolomite regions, the system amplified subtle spectral differences related to crystal structure and grain size by integrating spectral information from SWIR, MWIR, and LWIR and applying Principal Component Analysis (PCA). These differences are entirely undetectable to the naked eye.

3. Locating Optimal Sampling Points: Based on the spectral differences, the K-means clustering algorithm automatically classified the dolomite pixels into four categories. The system then calculated the spectral centroid for each category and recommended the core locations closest to these centroids as the most representative sampling points.

A. Standardized confusion matrix, displaying the classification results of dolomite structure based on petrographic analysis and hyperspectral imaging analysis (comparison). B. Correlation analysis chart between iron index and iron concentration calculated by hyperspectral imaging for the same core sampling point sample (which is also used for ICP-OES laboratory testing).

A. Normalized confusion matrix showing the dolomite texture classification from petrographic analysis and HSI based classification. B. Correlation between HSI derived iron index and Fe concentration for the same plug samples used for ICP-OES lab measurements.

A. Classification and distribution of different types of dolomite along rock cores. Representative spectra of various types of dolomite in the B. visible near-infrared short wave infrared (VNIR-SWIR) band, C. medium wave infrared (MWIR) band, and D. long wave infrared (LWIR) band.

A. Classification and distribution of the different dolomite types along the drill core. Representative spectra for each class in the VNIR- SWIR (B.), MWIR (B) and LWIR(C).

Conclusion

In summary, the highlight of this paper is that it presents a complete data-driven workflow from global scanning to sampling, providing new ideas for geological research; At the same time, it breaks through convention by integrating VNIR-SWIR-MWIR-LWIR multi band data, achieving precise characterization of carbonate rocks from mineral identification to structural classification.

This not only has direct value for the oil and gas industry, but also points out the direction for future fine characterization in mineral exploration and other fields.

If you want to learn about hyperspectral imaging systems in the visible near infrared, shortwave infrared, and even mid wave infrared or longwave infrared bands, please feel free to contact us!

「Conclusion」

In summary, the highlights of this paper are twofold: it demonstrates a complete data-driven workflow from full-scene scanning to sampling, providing a new paradigm for geological research; furthermore, it breaks from convention by integrating VNIR-SWIR-MWIR-LWIR multi-band data to achieve fine characterization of carbonate rocks, progressing from mineral identification to texture classification.

This holds direct value for the petroleum industry and also points the way for detailed characterization in future applications such as mineral exploration.

If you are interested in hyperspectral imaging systems covering the VNIR, SWIR, or even MWIR and LWIR ranges, please do not hesitate to contact us!

paperArticle：

Gairola, G. S., Thiele, S. T., Khanna, P., Ramdani, A. I., Gloaguen, R., & Vahrenkamp, V. (2024). A data-driven hyperspectral method for sampling of diagenetic carbonate fabrics – A case study using an outcrop analogue of Jurassic Arab-D reservoirs, Saudi Arabia. Marine and Petroleum Geology, 161, 106691.