Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Significance
Overpressures is one of the factors with a significant influence on the safety of oil/gas exploration, drilling and production. While pressure prediction in petroleum exploration is well articulated in the literature, most existing studies have concentrated on limited to moderately overpressure formations attributed to disequilibrium compaction. The Xihu depression, located in the East China sea basin, is a prime oil/gas production region. This depression has several vertically placed overpressure systems, one of the main indicators of the presence of natural gas in the region. Thus, overpressure prediction is of great significance to oil/gas exploration not only in the Xihu depression but in other oil fields as well.
Reliable pressure prediction requires a thorough knowledge of the origin and distribution of the overpressure, which is classified into five categories: fluid expansion, pressure transfer, tectonic compression, disequilibrium compaction and diagenesis. Fluid expansion caused by natural gas generated from the source rocks is the dominant overpressure mechanism in the Xihu region. Compared with conventional overpressures in uncompacted formations, the resulting overpressures caused by gas expansion manifest as thick mud-coated thin sand (MCS) high-pressure structure, which is still not fully addressed in prediction. Such complex MCS structures, gas-producing overpressures, high porosities and the influence of high gas content on seismic velocities render traditional prediction methods unsuitable.
Rock physics templates are widely used to predict overpressures. However, the main challenge to this approach is elastic nonlinearity. Since attenuation and acoustic velocity from rock physics exhibit uncertain dependence on formation pressures, it could be more useful if pressure predictions were based on nonlinear seismic inversions based on applying a combination of statistical and deterministic methods. To address these challenges, Dr. Yifan Cheng and Professor Li-Yun Fu from China University of Petroleum (East China) employed some petrophysical models into non-traditional Caianiello convolutional neural networks (CNNs) to combine deterministic and statistical overpressure prediction mechanisms. The work is currently published in the journal, Journal of Petroleum Science and Engineering.
In their approach, a frequency-dependent quality factor Q-pressure petrophysical model was employed owing to the high sensitivity of acoustic attenuation to overpressures. First, the physics-informed CCNNs were established for overpressure prediction, followed by the geological setting of the Xihu region based on the available information reading the origin and distribution of the overpressures. The well-based empirical petrophysical model was constructed by correlating pressures and well quality factors. The reliability of the seismic data was established and the model was validated. Finally, the overpressure of the Xihu depression was predicted using the proposed method.
The authors demonstrated that by relating overpressures to seismic properties, petrophysical model could improve the predictive ability of physics-informed CCNNs. Such convolution neurons allowed strong extraction of features as well as powerful learning ability desirable to enhance overpressure prediction for Xihu depression. The presented scheme successfully validated the applicability of the physics-informed CCNNs inversion scheme. The reliability of the prediction away from the training wells was dependent on the geological complexity of the areas being studied. However, it was possible to improve the representation information in the trained CCNNs by feeding new well data gradually during oil field development.
In summary, Professor Li-Yun Fu and Dr. Yifan Cheng investigated the origin and distribution of overpressures in the Xihu depression to improve the reliability and accuracy of overpressure prediction. The presented CCNNs-based seismic inversion scheme comprised deep learning for neural wavelets, petrophysical modeling reservoir, deconvolution-based inversion, well-seismic correlation analysis and input-signal reconstruction to enhance the reliability of the initial pressure model. In a statement to Advances in Engineering, Professor Li-Yun Fu pointed out that the study would pave the way for improved overpressure prediction in oil/gas exploration and development.
Reference
Cheng, Y., & Fu, L. (2022). Nonlinear seismic inversion by physics-informed Caianiello convolutional neural networks for overpressure prediction of source rocks in the offshore Xihu Depression, East China. Journal of Petroleum Science and Engineering, 215, 110654.
