Artificial Intelligence in Geosciences

Qiansheng Wei

Zishuai Li

Haolan Feng

Yueying Jiang

Yang Yang

Zhiguo Wang

Convolutional sparse coding network for sparse seismic time-frequency representation

用于稀疏地震时频表示的卷积稀疏编码网络

Cai Li

Fei Ma

Yuxiu Wang

Delong Zhang

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--A case study of Bohai A oilfield

基于大数据分析的河流相砂体连通性预测方法研究——以渤海A油田为例

Hassan Bagheri

Reza Mohebian

Ali Moradzadeh

Behnia Azizzadeh Mehmandost Olya

Pore Size Classification and Prediction Based on Distribution of Reservoir Fluid Volumes Utilizing Well Logs and Deep Learning Algorithm in a Complex Lithology

基于复杂岩性储层流体体积分布的测井数据与深度学习算法孔隙尺寸分类与预测

Machine learning (ML) to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields. Meandering, braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels. Three cored wellbores drilled through such a reservoir in a large oil field, with just four recorded well logs available, are used to classify four lithofacies using ML models. To augment the well-log data, six derivative and volatility attributes were calculated from the recorded gamma ray and density logs, providing sixteen log features for the ML models to select from. A novel, multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation. Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation. When the trained ML models were applied to a third well for testing, lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features. However, an accuracy of ∼0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well. A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with ∼0.6 accuracy. Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.

David A. Wood

Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence

优化特征选择辅助渐变河流序列中结合计算属性的稀疏测井数据岩相机器学习

Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms. Typically, the target variable is observed at a few sampling locations due to the relatively time-consuming and costly process of obtaining measurements. In contrast, auxiliary variables are often exhaustively observed within the region under study through the increasing development of remote sensing platforms and sensor networks. Supervised machine learning methods do not fully leverage this large amount of auxiliary spatial data. Indeed, in these methods, the training dataset includes only labeled data locations (where both target and auxiliary variables were measured). At the same time, unlabeled data locations (where auxiliary variables were measured but not the target variable) are not considered during the model training phase. Consequently, only a limited amount of auxiliary spatial data is utilized during the model training stage. As an alternative to supervised learning, semi-supervised learning, which learns from labeled as well as unlabeled data, can be used to address this problem. However, conventional semi-supervised learning techniques do not account for the specificities of spatial data. This paper introduces a spatial semi-supervised learning framework where geostatistics and machine learning are combined to harness a large amount of unlabeled spatial data in combination with typically a smaller set of labeled spatial data. The main idea consists of leveraging the target variable's spatial autocorrelation to generate pseudo labels at unlabeled data points that are geographically close to labeled data points. This is achieved through geostatistical conditional simulation, where an ensemble of pseudo labels is generated to account for the uncertainty in the pseudo labeling process. The observed labels are augmented by this ensemble of pseudo labels to create an ensemble of pseudo training datasets. A supervised machine learning model is then trained on each pseudo training dataset, followed by an aggregation of trained models. The proposed geostatistical semi-supervised learning method is applied to synthetic and real-world spatial datasets. Its predictive performance is compared with some classical supervised and semi-supervised machine learning methods. It appears that it can effectively leverage a large amount of unlabeled spatial data to improve the target variable's spatial prediction.

Francky Fouedjio

Hassan Talebi

Geostatistical semi-supervised learning for spatial prediction

地理统计半监督学习用于空间预测

Padala Raja Shekar

Aneesh Mathew

P.V. Yeswanth

S. Deivalakshmi

A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

结合深度CNN-RNN网络的印度Bardha流域降雨径流模拟

Guruh Samodra

None Ngadisih

Ferman Setia Nugroho

Benchmarking landslide inventory data handling strategies for landslide susceptibilitymodeling based on different random forest machine learning workflows

基于不同随机森林机器学习工作流的滑坡易发性建模中滑坡编目数据处理策略基准测试

Publication Information	Publisher: KeAi Communications Co.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%
Journal Fees	$800Details
Average review cycle	网友分享经验：21 Weeks

Artificial Intelligence in Geosciences

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Convolutional sparse coding network for sparse seismic time-frequency representation

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--A case study of Bohai A oilfield

Pore Size Classification and Prediction Based on Distribution of Reservoir Fluid Volumes Utilizing Well Logs and Deep Learning Algorithm in a Complex Lithology

Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence

Geostatistical semi-supervised learning for spatial prediction

A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India

Convolutional sparse coding network for sparse seismic time-frequency representation

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--A case study of Bohai A oilfield

Pore Size Classification and Prediction Based on Distribution of Reservoir Fluid Volumes Utilizing Well Logs and Deep Learning Algorithm in a Complex Lithology

Benchmarking landslide inventory data handling strategies for landslide susceptibilitymodeling based on different random forest machine learning workflows

ISSN	2666-5441
h-index	9