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| 1 | 基于小层约束地震反演技术在T油田的应用显示文摘为了实现地震储层预测技术向油田开发领域的延伸,了解储层展布体系,提出了基于开发小层的储层精细地震反演技术。首先利用高分辨率层序地层学理论指导小层划分,确保地层格架的等时性和合理性。在此基础上以开发小层为约束,以测井资料为出发点,根据地震响应的趋势进行波阻抗反演,预测储层展布规律。在T油田的应用证明,基于小层约束的地震反演能够提高地震资料的纵向分辨能力,有效提高储层预测的精度,可更加清楚地揭示小层内储层的分布特征,为油藏评价、油田开发方案编制和实施提供翔实的基础资料。 | 王兆峰 魏小东 陈鑫 张大伟 于文文 | 2012 | 石油地球物理勘探2012，47，4： | 7 |
| 2 | 地震储层预测技术在塔里木盆地雅克拉地区的应用显示文摘塔里木盆地雅克拉地区油气资源丰富,但成藏规律不明显,针对该区侏罗系砂岩储层的展布及物性开展地震综合预测研究。通过叠后纯波地震资料,开展拟声波反演、频谱成像技术刻画砂砾岩体的纵横向分布。在此基础上开展叠前弹性波阻抗反演,包括目标储层段的纵横波阻抗,拉梅系数等分布特征及高频吸收衰减流体检测技术,分析不同岩性、物性、含油气性的地震响应特征。综合地震储层预测技术,研究侏罗系下统砂砾岩体有效储层的分布,寻找油气勘探有利靶区。 | 胡金祥 王萍 纪金海 李春雷 张艳红 | 2012 | 世界地质2012，31，2： | 6 |
| 3 | 高分辨率层序格架约束的地震反演及应用显示文摘在油藏勘探开发中,利用地震波阻抗反演技术预测储层空间分布特征,自应用以来发挥着非常重要的作用。特别是在油藏开发中后期,需要对薄层、薄互层等油层进一步开发时,该项技术显得尤为重要。尽管该方法以测井和地质等资料为约束,但依据传统的构造解释方法建立的初始构造模型不够准确,仍然无法降低地震反演的多解性,井间储层分布与后验井存在较大差别,预测出的薄层、薄互层及储层岩性的可信度较低。本文利用测井资料进行高分辨层序地层划分,依据单井合成地震记录将划分结果标定在地震剖面上,并完成各层序界面的地震层位解释,井震结合建立高分辨率层序地层格架。结果表明,将该地层格架作为初始模型进行测井约束的地震资料波阻抗反演,对不同旋回发育的薄层或薄互层储层,横向预测不会以一套厚储层呈现,提高了储层反演预测储层分布的精度。通过大庆长垣高台子油田中的具体应用,展示了高分辨率层序地层格架约束下的波阻抗反演技术在薄层、薄互层储层预测中的广阔应用前景。 | 曹彤 郭少斌 | 2013 | 科技导报2013，31，9： | 3 |
| 4 | A seismic texture coherence algorithm and its application显示文摘The first generation coherence algorithm(the C1 algorithm) that calculates the coherence of seismic data in-line and cross-line was developed using statistical cross-correlation theory, and it has the limitation that the technique can only be applied to horizons. Based on the texture technique, the texture coherence algorithm uses seismic information in different directions and differences among multiple traces. It can not only calculate seismic coherence in in-line and cross-line directions but also in all other directions. In this study, we suggested first an optimization method and a criterion for constructing the gray level co-occurrence matrix of the seismic texture coherence algorithm. Then the co-occurrence matrix was prepared to evaluate differences among multiple traces. Compared with the C1 algorithm, the seismic texture coherence algorithm suggested in this paper is better than the C1 in its information extraction and application. Furthermore, it implements the multi-direction information fusion and it, also has the advantage of simplicity and effectiveness, and improves the resolution of the seismic profile. Application of the method to field data shows that the texture coherence attribute is superior to that of C1 and that it has merits in identification of faults and channels. | Chuai Xiaoyu Wang Shangxu Yuan Sanyi Chen Wei Meng Xiangcui | 2014 | Petroleum Science2014，11，2： | 0 |
| 5 | Model-constrained and data-driven double-supervision acoustic impedance inversion显示文摘Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method. | Dong-Feng Zhao Na-Xia Yang Jin-Liang Xiong Guo-Fa Li Shu-Wen Guo | 2023 | Petroleum Science2023，20，5： | 0 |
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