Methods for seismic Sweet Spot Identification, Characterization and Prediction in Shale Plays

Gading, M.; Wensaas, Lars; Collins, Paul

doi:10.1190/urtec2013-142

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Chen (2014) determined the inversion-sensitive parameters through a joint analysis of TOC measurements and geophysical inputs, which established the most suitable equation to allow inversion for TOC distribution based on the prestack seismic inversion density cube, in the Jiaoshiba area of the Sichuan Basin. Gading et al (2013) pointed out that the description (scope, thickness and abundance) of shale reservoirs may be obtained through the analysis of seismic data, based on the relationship between TOC and rock properties established in petrophysical analysis. Nieto et al (2013) came up with a workflow to predict lithology, which starts from a lithofacies classification based on rock physics, and then set up an a priori distribution of each facies, and the posterior distribution of each facies was inverted based on a Bayesian scheme using seismic data.…”

Section: Introductionmentioning

confidence: 99%

Intelligent prediction and integral analysis of shale oil and gas sweet spots

Qian

Liu

et al. 2018

Pet. Sci.

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Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multidisciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.

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Section: Introductionmentioning

confidence: 99%

Intelligent prediction and integral analysis of shale oil and gas sweet spots

Qian

Liu

et al. 2018

Pet. Sci.

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“…In recent years, unconventional oil and gas has received extensive attention. Unconventional oil and gas distribution is often characterized by thin reservoir thickness, and the exploration and development continue to deepen, how to use the existing data to improve the resolution of the thin layer, and then achieve high‐resolution reservoir prediction, which has become an important problem (Gading et al., 2013; Song, 2018). The shale oil of the Paleogene Shahejie Formation in the Jiyang depression is large in scale and has been studied to a lesser extent, which is now in the focus stage (Song et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

High‐resolution reservoir prediction method based on data‐driven and model‐based approaches

ZeYang,

Wei,

XiaoHong

et al. 2024

Geophysical Prospecting

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The Jiyang depression in the southeastern part of the Bohai Bay Basin has a relatively large scale set of shale oil in the Paleogene Shahejie Formation, but the complex internal components lead to narrow frequency bands, low resolution and difficulty in reservoir information extraction. Impedance is important information for reservoir characterization, and how to predict high‐resolution impedance using available information is particularly important. Deep learning, known for its effectiveness in addressing non‐linear problems, has found extensive applications in various fields of oil and gas exploration. However, the challenges of overfitting and poor generalization persist due to the limited availability of training datasets. Besides, existing methods often use networks to solve a single problem in fact, deep learning can deal with a series of problems intelligently. In order to partially solve the above problems, an intelligent storage prediction network framework is proposed in this paper. Physical information is introduced to realize data‐driven and model‐based approaches, thus solving the problem of difficult construction of training datasets. The processing part accomplishes the high‐resolution processing of seismic records, thus solving the problems of narrow bandwidth and low resolution. Initial model constraints are introduced so as to obtain more stable inversion results. Finally, the well data is compared and analysed to identify and predict the lithology and complete the intelligent prediction of unconventional reservoirs. The results are compared with the traditional model‐driven inversion method, revealing that the approach presented in this paper exhibits higher resolution in predicting dolomite. This contributes to the establishment of a robust data foundation for reservoir evaluation.

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