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

在地質(zhì)勘探領(lǐng)域，如何快速、準(zhǔn)確地從數(shù)百米長(zhǎng)的巖心中找到最關(guān)鍵的那幾塊樣本，一直是個(gè)巨大挑戰(zhàn)。一項(xiàng)針對(duì)沙特阿拉伯油氣儲(chǔ)層的研究，展示了一種具有前景的解決方案。

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.

注：

地質(zhì)勘探是指為了查明地下地質(zhì)情況，探尋礦產(chǎn)資源（如油氣、礦產(chǎn)、地下水等）而開展的一系列綜合性調(diào)查與研究工作。對(duì)于油氣領(lǐng)域而言，地質(zhì)勘探的核心任務(wù)是確定油氣聚集的有利區(qū)域，并最終定位可供開采的油氣藏。

油氣儲(chǔ)層是地下具備儲(chǔ)集空間（孔隙、裂縫）和流體滲流能力（滲透性）的巖層，油氣就儲(chǔ)存于這些空間內(nèi)。它是油氣藏形成的核心要素之一。最常見的油氣儲(chǔ)層巖石類型是砂巖和碳酸鹽巖（如石灰?guī)r、白云巖）。

通過地質(zhì)勘探，來發(fā)現(xiàn)、圈定和評(píng)價(jià)具有商業(yè)價(jià)值的油氣儲(chǔ)層。

Note:

Geological Exploration refers 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 Reservoir is 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.

研究地區(qū)（Wadi Daqlah）出露巖相的綜合巖石地層柱狀圖 / the generalized lithostratigraphy of exposed facies in  the study area (Wadi Daqlah)

「光譜范圍」

在這項(xiàng)研究的核心技術(shù)基礎(chǔ)是全波段高光譜成像，光譜范圍覆蓋了從可見光到長(zhǎng)波紅外的廣闊區(qū)間：

可見光-近紅外 (VNIR, 400-1000 nm)：主要對(duì)巖石中的鐵離子（Fe2?）等過渡金屬元素敏感。通過計(jì)算鐵指數(shù)，可以快速評(píng)估巖石的化學(xué)組成變化。

短波紅外 (SWIR, 1000-2500 nm)：方解石和白云石在此區(qū)域的特征吸收谷位置，存在約20納米的微小但穩(wěn)定的偏移（方解石~2345 nm，白云石~2325 nm）。這個(gè)差異是快速、準(zhǔn)確繪制礦物分布圖的關(guān)鍵。

中波紅外 (MWIR, 3-7μm) 與長(zhǎng)波紅外 (LWIR, 8-14μm)：包含了碳酸鹽礦物更復(fù)雜的分子振動(dòng)信息。本研究通過融合這三個(gè)紅外區(qū)間的數(shù)據(jù)，能夠捕捉到單靠SWIR無法識(shí)別的、與巖石晶體結(jié)構(gòu)和粒度相關(guān)的細(xì)微差異，從而成功區(qū)分了不同結(jié)構(gòu)的白云巖。

「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) and Long-Wave Infrared (LWIR, 8-14μm): 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.

白云巖（綠）與方解石（藍(lán)）的光譜特征：白云巖的特征吸收峰位置約為2325nm，方解石的特征吸收峰位置約為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.

「基于高光譜成像的數(shù)據(jù)分析工作流程」

傳統(tǒng)地質(zhì)采樣可被視為“經(jīng)驗(yàn)驅(qū)動(dòng)"模式，嚴(yán)重依賴專家的肉眼觀察和主觀判斷。本研究提出一套更客觀的、更高效、可重復(fù)的“數(shù)據(jù)驅(qū)動(dòng)"解決方案，其工作流程清晰體現(xiàn)了從宏觀到微觀的分析邏輯：

第一步：全域掃描，繪制礦物地圖。研究團(tuán)隊(duì)首先利用SWIR波段特征，對(duì)50米長(zhǎng)的巖心進(jìn)行快速掃描，生成一張高精度的礦物分布圖，清晰界定出白云石化的目標(biāo)區(qū)域。

第二步：識(shí)別結(jié)構(gòu)差異。在鎖定白云石區(qū)域后，通過綜合SWIR、MWIR和LWIR的光譜信息，并采用主成分分析（PCA）算法，系統(tǒng)能夠放大那些與晶體結(jié)構(gòu)、粒度相關(guān)的細(xì)微光譜差異。這些差異是肉眼無法分辨的。

第三步：定位最佳采樣點(diǎn)。基于光譜差異，K-means聚類算法將白云石像素自動(dòng)劃分為4個(gè)類別。隨后，系統(tǒng)會(huì)計(jì)算出每個(gè)類別的光譜中心，并推薦最靠近這些中心的巖心位置作為具代表性的采樣點(diǎn)。

「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. 標(biāo)準(zhǔn)化混淆矩陣，展示基于巖相學(xué)分析與高光譜成像分析的白云巖結(jié)構(gòu)分類結(jié)果（對(duì)比）。B. 針對(duì)相同的巖芯取樣點(diǎn)樣品（該樣品同時(shí)用于ICP-OES實(shí)驗(yàn)室檢測(cè)），高光譜成像計(jì)算得出的鐵指數(shù)與鐵濃度之間的相關(guān)性分析圖。

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.不同類型白云巖沿巖芯的分類及分布情況。各類別白云巖在B.可見近紅外-短波紅外（VNIR-SWIR）波段、C.中波紅外（MWIR）波段、D.長(zhǎng)波紅外（LWIR）波段的代表性光譜。

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).

「結(jié)語」

綜上所述，這篇論文的亮點(diǎn)在于：它展示了一個(gè)從全域掃描到采樣的完整數(shù)據(jù)驅(qū)動(dòng)工作流程，為地質(zhì)研究提供了新思路；同時(shí)，它突破了常規(guī)，通過集成VNIR-SWIR-MWIR-LWIR多波段數(shù)據(jù)，實(shí)現(xiàn)了對(duì)碳酸鹽巖從礦物識(shí)別到結(jié)構(gòu)分類的精細(xì)刻畫。

這不僅對(duì)油氣行業(yè)有直接價(jià)值，也為未來在礦產(chǎn)勘查等領(lǐng)域的精細(xì)表征指明了方向。

如果您想了解可見光-近紅外、短波紅外波段，甚至是中波紅外或長(zhǎng)波紅外波段的高光譜成像系統(tǒng)，歡迎聯(lián)系我們！

「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!

論文 / Article：

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.