Machine-learning algorithms have been used by geoscientists to infer geologic and physical properties from hydrocarbon exploration and development wells for more than 40 years. These techniques historically utilize digital well-log information, which, like any remotely sensed measurement, have resolution limitations. Core is the only subsurface data that is true to geologic scale and heterogeneity. However, core description and analysis are time-intensive, and therefore most core data are not utilized to their full potential. Quadrant 204 on the United Kingdom Continental Shelf has publicly available open-source core and well log data. This study utilizes this dataset and machine-learning models to predict lithology and facies at the centimeter scale. We selected 12 wells from the Q204 region with well-log and core data from the Schiehallion, Foinaven, Loyal, and Alligin hydrocarbon fields. We interpreted training data from 659 m of core at the sub-centimeter scale, utilizing a lithology-based labeling scheme (five classes) and a depositional-process-based facies labeling scheme (six classes). Utilizing a ‘color-channel-log’ (CCL) that summarizes the core image at each depth interval, our best performing trained model predicts the correct lithology with 69% accuracy (i.e., the predicted lithology output from the model is the same as the interpreted lithology) and predicts individual lithology classes of sandstone and mudstone with over 80% accuracy. The CCL data require less compute power than core image data and generate more accurate results. While the process-based facies labels better characterize turbidites and hybrid-event-bed stratigraphy, the machine-learning based predictions were not as accurate as compared to lithology. In all cases, the standard well-log data cannot accurately predict lithology or facies at the centimeter level. The machine-learning workflow developed for this study can unlock warehouses full of high-resolution data in a multitude of geological settings. The workflow can be applied to other geographic areas and deposit types where large quantities of photographed core material are available. This research establishes an open-source, python-based machine-learning workflow to analyze open-source core image data in a scalable, reproducible way. We anticipate that this study will serve as a baseline for future research and analysis of borehole and core data.

/pdf/centimeter-scale-lithology-and-facies-prediction-in-cored-1991b7mn5l.pdf

Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

Correct lithofacies interpretation sourced from wireline log data is an essential source of prior information for joint seismic inversion for facies and impedances, among other application...

Rock-physics machine learning toolkit for joint litho-fluid facies classification and compaction modeling

Recent advances in machine learning (ML) have transformed the landscape of energy exploration, including hydrocarbon, CO2 storage, and hydrogen. However, building competent ML models for reservoir characterization necessitates specific in-depth knowledge in order to fine-tune the models and achieve the best predictions, limiting the accessibility of machine learning in geosciences. To mitigate this issue, we implemented the recently emerged automated machine learning (AutoML) approach to perform an algorithm search for conducting an unconventional reservoir characterization with a more optimized and accessible workflow than traditional ML approaches. In this study, over 1000 wells from Alberta's Athabasca Oil Sands were analyzed to predict various key reservoir properties such as lithofacies, porosity, volume of shale, and bitumen mass percentage. Our proposed workflow consists of two stages of AutoML predictions, including (1) the first stage focuses on predicting the volume of shale and porosity by using conventional well log data, and (2) the second stage combines the predicted outputs with well log data to predict the lithofacies and bitumen percentage. The findings show that out of the ten different models tested for predicting the porosity (78% in accuracy), the volume of shale (80.5%), bitumen percentage (67.3%), and lithofacies classification (98%), distributed random forest, and gradient boosting machine emerged as the best models. When compared to the manually fine-tuned conventional machine learning algorithms, the AutoML-based algorithms provide a notable improvement on reservoir property predictions, with higher weighted average f1-scores of up to 15-20% in the classification problem and 5-10% in the adjusted-R2 score for the regression problems in the blind test dataset, and it is achieved only after ~ 400 s of training and testing processes. In addition, from the feature ranking extraction technique, there is a good agreement with domain experts regarding the most significant input parameters in each prediction. Therefore, it is evidence that the AutoML workflow has proven powerful in performing advanced petrophysical analysis and reservoir characterization with minimal time and human intervention, allowing more accessibility to domain experts while maintaining the model's explainability. Integration of AutoML and subject matter experts could advance artificial intelligence technology implementation in optimizing data-driven energy geosciences. 

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization


 Drill core is a vital resource for subsurface characterisation and informs process understanding. However, it is expensive to collect and as a result, the geoscience community increasingly relies on data from legacy core to address today\x92s energy challenges. Many countries store geological materials collected over decades in national archives. In the UK, over 600 km of drill core is currently stored at the UK national core repository which covers a breadth of the UK\x92s geology, including those targeted for resources, energy and waste storage. The challenge is to maximise the value of these analogue archives and new core when deposited - improving access to materials and associated data, whilst simultaneously maximising preservation to ensure optimised use, now and in the future. This paper summarises the BGS approach to characterise drill core more efficiently and consistently using a multiple-technique core scanning approach set within a project-specific core scanning workflow to increase core data acquisition and complement traditional core characterisation practices. Thus, creating a digital record of the core, preserving it beyond its physical lifetime and improving accessibility. This paper highlights the benefits and challenges of this long-term endeavour, especially in making the data open access and discoverable.

Unlocking national treasures: The core scanning approach

Interpretation of unconsolidated Quaternary sedimentary core is difficult if key diagnostic features are obscured or not present, therefore traditional facies analysis is challenging. However, sediment texture remains a universal attribute which can be used to interpret sedimentary core. Here we present an automated classification workflow which implements Extreme Gradient Boosting and Bayesian Optimization of hyperparameters to differentiate estuarine sub-depositional environments. We use 19 textural attributes, measured using laser particle size analysis of surface sediment samples from the Ravenglass Estuary, Cumbria, northwest England, to make unbiased classification of sub-depositional environment and estuarine zone. Two predictive models created using the automated workflow are presented and evaluated using a suite of evaluation metrics, confusion matrices, and spatial analysis to understand their geological implications. Model 1 keeps all sub-depositional environments discrete and has an overall accuracy of 68.96%. Model 2 merges related sub-depositional environments to form inner-coarse and outer-estuary zones and has an overall accuracy of 84.14%. Both models have been applied to textural data obtained at 5 cm intervals from a Holocene core drilled through a tidal bar in the Ravenglass estuarine succession, NW England, to classify palaeo sub-depositional environment. Predictive output of the models suggests that the core consistently experienced inner estuary deposition; all inner estuary environments are represented in the core. The workflow presented here could be applied to datasets from other marginal marine depositional systems to enhance the interpretation of their subsurface deposits. Ultimately, detailed interpretations of ancient, buried deposits could be made using models derived from analogous modern systems. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022JF006891

Automated Classification of Estuarine Sub‐Depositional Environment Using Sediment Texture

Core samples – cylindrical rock samples taken from subsurface boreholes – are commonly used by Earth scientists to study geologic history and processes. Core is usually cut into one-meter segments, slabbed lengthwise to expose a flat surface, and stored in cardboard or wooden boxes which are then photographed to enable remote inspection. Unlike other common sources of borehole data (e.g., well logs Rider & Kennedy, 2011), core is the only data that preserves true geologic scale and heterogeneity.

/pdf/corebreakout-subsurface-core-images-to-depth-registered-2eoxq32w3b.pdf

CoreBreakout: Subsurface Core Images to Depth-Registered Datasets

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Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

CoreBreakout: Subsurface Core Images to Depth-Registered Datasets