Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing ...Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.展开更多
Suakin-1 and Suakin-2 wells are in the Sudanese Red Sea segment where the hydrocarbon generation had been proved by previous studies, however, no reasonable reserve was evidenced due to the complexities of the surroun...Suakin-1 and Suakin-2 wells are in the Sudanese Red Sea segment where the hydrocarbon generation had been proved by previous studies, however, no reasonable reserve was evidenced due to the complexities of the surrounding salt structures. Six seismic lines were tied to Suakin-1 and -2 to delineate the controlling salt tectonics. The salt evacuation (Roho) and other salt bodies were recognized and matched with similar salt structures in analogous stratigraphic conditions as the Gulf of Mexico and Angola margin. While a previous inconsistent interpretation in the study area marked the high amplitude horizon of the Lower Zeit formation as the top of the Dungunab formation. Three seismic features indicated the presence of salt dome (autochthonous): velocity pull-up, dragging of the sedimentary layers forming mini basins around the third feature, which is the relative transparency of the seismic signal in two piercing like bodies. This interpretation similarly demarcated that the salt escaped east-wards, thus the mapped welded salt is believed to be formed after the salt evacuation. A 3D seismic with a far offset and wide range of azimuth is recommended for detailed imaging.展开更多
文摘Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.
文摘Suakin-1 and Suakin-2 wells are in the Sudanese Red Sea segment where the hydrocarbon generation had been proved by previous studies, however, no reasonable reserve was evidenced due to the complexities of the surrounding salt structures. Six seismic lines were tied to Suakin-1 and -2 to delineate the controlling salt tectonics. The salt evacuation (Roho) and other salt bodies were recognized and matched with similar salt structures in analogous stratigraphic conditions as the Gulf of Mexico and Angola margin. While a previous inconsistent interpretation in the study area marked the high amplitude horizon of the Lower Zeit formation as the top of the Dungunab formation. Three seismic features indicated the presence of salt dome (autochthonous): velocity pull-up, dragging of the sedimentary layers forming mini basins around the third feature, which is the relative transparency of the seismic signal in two piercing like bodies. This interpretation similarly demarcated that the salt escaped east-wards, thus the mapped welded salt is believed to be formed after the salt evacuation. A 3D seismic with a far offset and wide range of azimuth is recommended for detailed imaging.
