Evaluation of machine learning methods for lithology classification using geophysical data

Bressan, Thiago Santi; Souza, Marcelo Kehl de; Girelli, Tiago Jonatan; Chemale, Farid

doi:10.1016/j.cageo.2020.104475

Cited by 148 publications

(49 citation statements)

References 37 publications

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“…In particular, the CNN was the firstly applied to a two-dimensional matrix of production history. In the case of SVM, it is one of conventional machine learning algorithms, which are not based on the concept of neural network, and has shown reliable performance in facies classification [38][39][40]. In this study, it has been verified against the TI classification problem.…”

Section: Introductionmentioning

confidence: 85%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

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Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

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Section: Introductionmentioning

confidence: 85%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

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“…The optimum parameter settings used in BO for XGBoost model selection are shown in Table 2. The Number of estimators were randomly chosen in the interval [10,100]. The Max depth was randomly chosen in the interval [1,20].…”

Section: Tuning Processmentioning

confidence: 99%

“…The Subsample was randomly chosen in the interval [0.1, 1]. The Min child weight was randomly chosen in the interval [1,10]. The Gamma was randomly chosen in the interval [0.1, 0.6].…”

Section: Tuning Processmentioning

confidence: 99%

“…Compared with the direct method, using well logs to identify lithology is a universal indirect method, which is more accurate, effective and economical. Until now, there have been many lithology identification methods associated with logs, including the cross plotting method, traditional statistical analysis and several machine learning methods [8][9][10]. The cross plotting takes a considerable amount of time of an experienced analyst, especially in the heterogeneous reservoir.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Sun

Jiang

et al. 2020

Energies

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The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties.

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“…The authors of [9] have made comparisons of machine learning algorithms using scikitlearning framework (MLPClassifier, the DecisionTreeClassifier, the RandomForestClassifier, and the SVC) for data from offshore wells. Algorithms have been applied to three standard data templates and a practical data template in a lithology classification problem for wells from International Ocean Discovery Program (IODP) Expeditions.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

et al. 2021

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Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

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Evaluation of machine learning methods for lithology classification using geophysical data

Cited by 148 publications

References 37 publications

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

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