Automatic Determination of Sedimentary Units from Well Data

Bubnova, Anna; Ors, Fabien; Rivoirard, Jacques; Cojan, Isabelle; Romary, Thomas

doi:10.1007/s11004-019-09793-w

Cited by 6 publications

(2 citation statements)

References 18 publications

(1 reference statement)

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“…The two most similar clusters are then merged into a new cluster and their distances are updated. The algorithm stops when all the objects merge into a single cluster [51,52].…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.

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“…The two most similar clusters are then merged into a new cluster and their distances are updated. The algorithm stops when all the objects merge into a single cluster [51,52].…”

Section: Hierarchical Cluster Analysis (Hca)mentioning

confidence: 99%

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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“…In previous studies, ML methods have been modified to include 3D spatial information for sets of dense drill hole data, such as brownfields exploration or mining situations. For example, the method of Fouedjio et al (2017) uses geostatistical parameters to encode the joint spatial continuity structure of multiple variables, Romary et al (2015) include spatial proximity as a condition for clustering and Bubnova et al (2020) uses spatial data as a connectivity constraint for clustering. All these methods have been developed and tested for dense drilling situations and are less reliable in greenfields mineral exploration because of the large distances between drill holes.…”

Section: Introductionmentioning

confidence: 99%

Improving Automated Geological Logging of Drill Holes by Incorporating Multiscale Spatial Methods

2020

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Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and amplitude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type.

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Bedding Angle Identification from BIF Marker Shales via Modified Dynamic Time Warping

et al. 2021

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Automatic Determination of Sedimentary Units from Well Data

Cited by 6 publications

References 18 publications

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Improving Automated Geological Logging of Drill Holes by Incorporating Multiscale Spatial Methods

Bedding Angle Identification from BIF Marker Shales via Modified Dynamic Time Warping

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