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| 1 | Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data显示文摘Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction of rock elastic constants and well failure analysis during drilling operations.Direct measurement by advanced logging tools such as dipole sonic imager is not always possible.For older wells,such data are not available in most cases.Therefore,it is an alternate way to develop a reliable correlation to estimate the shear wave velocity from existing log and/or core data.The objective of this research is to investigate the nature of dependency of different reservoir parameters on the shear wave velocity(VS) of clastic sedimentary rocks,and to identify the parameter/variable which shows the highest level of dependency.In the study,data-driven connectionist models are developed using machine learning approach of least square support vector machine(LSSVM).The coupled simulated annealing(CSA) approach is utilized to optimize the tuning and kernel parameters in the model development.The performance of the simulation-based model is evaluated using statistical parameters.It is found that the most dependency predictor variable is the compressional wave velocity,followed by the rock porosity,bulk density and shale volume in turn.A new correlation is developed to estimate VS,which captures the most influential parameters of sedimentary rocks.The new correlation is verified and compared with existing models using measured data of sandstone,and it exhibits a minimal error and high correlation coefficient(R^(2)-0.96).The hybridized LSSVM-CSA connectionist model development strategy can be applied for further analysis to predict rock mechanical properties.Additionally,the improved correlation of VS can be adopted to estimate rock elastic constants and conduct wellbore failure analysis for safe drilling and field development decisions,reducing the exploration costs. | Mohammad Islam Miah | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 1 |
| 2 | Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering显示文摘In the last few decades,deep learning(DL)has afforded solutions to macroscopic problems in petroleum engineering,but mechanistic problems at the microscale have not benefited from it.Mechanism studies have been the strong demands for the emerging projects,such as the gas storage and hydrate production,and for some problems encountered in the storage process,which are common found as the chemical interaction between injected gas and mineral,and the formation of hydrate.Emerging advances in DL technology enable solving molecular dynamics(MD)with quantum accuracy.The conventional quantum chemical method is computational expensive,whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters.With the help of the DL force field,precision at the quantum chemistry level can be achieved in MD.Moreover,the DL force field promotes the computational speed compared with first-principles calculations.In this review,the basic knowledge of the molecular force field and deep neural network(DNN)is first introduced.Then,three representative opensource packages relevant to the DL force field are introduced.As the most common components in the development of oil and gas reservoirs,water and methane are studied from the aspects of computational efficiency and chemical reaction respectively,providing the foundation of oil and gas researches.However,in the oil and gas problems,the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD.Regarding the computational efficiency,it needs improvement via GPU and parallel accelerations to compete with classical MD.Even with such difficulties,the booming of this technique in the area of petroleum engineering can be predictable. | Jie Liu Tao Zhang Shuyu Sun | 2024 | Geoscience Frontiers2024，15，2： | 0 |
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