Pressure prediction has long been one of subject of research focuses in petroleum geology
and exploration, but is traditionally limited to moderately overpressured formations due to …

Deep-learning (DL) methods have shown promising performance in predicting acoustic
impedance from seismic data that is typically considered as an ill-posed problem for …

Building subsurface models is a very important but challenging task in hydrocarbon
exploration and development. The subsurface elastic properties are usually sourced from …

We describe an effective method for joining the benefits of inversion of different kinds of
measurements. We show the simultaneous joint inversion objective function, which allows …

摘要对于陆相沉积环境下的复杂隐蔽岩性储层, 由于观测信息不准确, 如信息重叠,
信息缺失和噪音污染, 以及岩石物理关系模糊等原因, 储层横向预测存在不惟一性 …

Deep learning (DL) seismic simulations have become a leading-edge field that could
provide an effective alternative to traditional numerical solvers. We have developed a small …

Porosity prediction from seismic data is of considerable importance in reservoir quality
assessment, geological model building, and flow unit delineation. Deep learning …

Acoustic impedance (AI) is a key parameter frequently used for characterizing reservoirs in
the oil and gas industry. The absolute AI can be divided into background and high …

摘要常规三维地面地震反演不可避免的存在多解性和分辨率不高的缺陷, 而油藏地球物理阶段
丰富的多尺度地震资料为减小多解性, 提高分辨率提供了可能. 基于贝叶斯反演理论 …

复杂构造地震成像主要取决于叠前地震数据品质, 偏移速度可靠性和偏移算子成像精度.
库车坳陷异常复杂的近地表条件导致极低信噪比的地震采集数据. 该区逆冲推覆高陡构造刺穿盐 …