Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data

David A. Wood

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Abstract


The manual picking of reservoir formation boundaries using limited available well-log data in multiple wells across gas and oil reservoirs tends to be subjective and unreliable. The reasons for this are typically caused by the combined effects of spatial boundary complexity and limited well-log data availability. Formation boundary characterization and classification can be improved when treated as a binary classification task based on two or three recorded well logs assisted by their calculated derivative and volatility attributes assessed by machine learning. Two example wellbores penetrating a complex reservoir boundary, one with gamma-ray, compressional-sonic, and bulk-density logs recorded, the other with just gamma-ray and bulk-density logs recorded, are used to illustrate a more rigorous proposed methodology. By combining attribute calculation, optimized feature selection, multi-k-fold cross validation, confusion matrices, feature-influence analysis, and machine learning models it is possible to improve the classification of the formation boundary. With just gamma-ray and bulk-density recorded well logs plus selected attributes. K-nearest neighbour, support vector classification, and extreme gradient boosting machine learning models are able to achieve high binary classification accuracy: greater than 0.97 for training/validation in one well; and greater than 0.94 for testing in another well. extreme gradient boosting feature-influence analysis reveals the attributes that are the most important in the formation boundary predictions but these are likely to vary from reservoir to reservoir. The results of the study suggest that well-log attribute analysis, combined with machine learning has the potential to provide a more systematic formation boundary definition than relying only on a few recorded well-log curves.

Document Type: Original article

Cited as: Wood., D. A. Well-log attributes assist in the determination of reservoir formation tops in wells with sparse well-log data. Advances in Geo-Energy Research, 2023, 8(1): 45-60. https://doi.org/10.46690/ager.2023.04.05


Keywords


Systematic well-top picking, well-log attributes, optimized feature selection, multi-k-fold validation, formation-boundary characterization, sparse well-log suites

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References


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DOI: https://doi.org/10.46690/ager.2023.04.05

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