Search Results (2)

The exploration and development of marine geothermal energy is a field with significant potential, but it is also one that presents considerable challenges and costs. The assessment of marine geothermal reservoir potential is currently based on subjective analysis, and this study proposes an innovative clustering-based method to classify marine geothermal reservoirs systematically. The Yingqiong Basin was analysed to develop a machine learning framework to predict the potential of marine geothermal reservoirs (CPPOGR). The study integrated eight key geothermal features into a unified dataset, employing dimensionality reduction techniques (principal component analysis and sparse autoencoder) and SMOTE to balance the sample size. Machine learning classifiers, including XGBoost, BP Neural Networks, Support Vector Machines, K-Nearest Neighbours, and Random Forests, were utilised for prediction. The experimental results demonstrate that XGBoost is the most suitable classifier, achieving an excellent performance of 0.96 precision, 0.9556 recall, 0.9528 F1 score, and 0.9623 accuracy. These results demonstrate the effectiveness of the proposed CPPOGR in accurately classifying marine geothermal reservoirs based on intrinsic features. This study underscores the potential of integrating cluster analysis with machine learning for efficient reservoir characterisation, thereby offering a novel approach for marine geothermal resource assessment. Full article

Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources. Full article