地质导向的实时岩石性质估计:统计岩石物理驱动的地震声阻抗和随钻超深方位电阻率反演

IF 2.1 4区 工程技术 Q3 ENERGY & FUELS SPE Reservoir Evaluation & Engineering Pub Date : 2023-06-01 DOI:10.2118/214407-pa
F. Ciabarri, C. Tarchiani, Gioele Alberelli, F. Chinellato, M. Mele, Junio Alfonso Marini, M. Nickel, H. Borgos, G. Dahl
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引用次数: 0

摘要

这项工作描述了在钻井过程中获取的地震声阻抗(AI)和超深方位电阻率(UDAR)测井数据的统计岩石物理驱动反演,以估计井眼周围的孔隙度、含水饱和度和相类型。尽管分辨率有限,但考虑到孔隙空间和流体饱和度在声波和电联合域中的耦合效应,结合电磁电阻率测井测量的地震数据改善了对岩石性质的描述。所提出的反演并没有明确地使用正演模型,而是从训练数据集中概率地推断出岩石物理性质与由此产生的地球物理响应之间的相关性。训练集是通过结合现有井眼信息和统计岩石物理建模方法生成的。在反演过程中,将地震人工智能测量数据与随钻测井(LWD)电磁电阻率数据放在一起,通过核密度估计(KDE)算法直接从训练数据集中推导出岩石性质的点向概率分布。采用非参数统计方法逼近岩石物性的非对称体积分布,并考虑含水饱和度与电阻率之间的特征非线性关系。给定训练集中样本的先验相分类模板,就可以对岩石物性的多模态、依赖于相的行为及其独特的相关性模式进行建模。依赖于相的参数化可以隐含地考虑岩性对声波和电阻率响应的影响,即使反演的目标属性只有孔隙度和饱和度。为了对估计的岩石性质提供一个现实的不确定性量化,描述了一个简单的贝叶斯框架来解释岩石物理建模误差,并将地震和电阻率数据的不确定性传播到反演结果中。为此,采用尺度校正策略,解决了测井资料与地震资料尺度差异带来的不确定性。所描述的反演的主要特点在于其基于查找表的快速实施,该查找表允许在获取和反演UDAR数据后实时估计岩石属性及其相关不确定性。这提供了一种强大、直接、快速的方法,可以毫不费力地集成到现有的工作流程中,以支持地质导向操作。该反演在碎屑含油油藏中进行了验证,在该油藏中,地质导向技术用于指导复杂构造环境中水平井的定位。结果表明,所提出的方法提供了对井筒周围岩石性质分布的真实估计,直至调查深度为50 m。这些信息对推动地质导向决策非常有用,也可以在钻井后用于更新或优化现有油藏模型。
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Real-Time Rock-Properties Estimation for Geosteering: Statistical Rock-Physics-Driven Inversion of Seismic Acoustic Impedance and LWD Ultradeep Azimuthal Resistivity
This work describes a statistical rock-physics-driven inversion of seismic acoustic impedance (AI) and ultradeep azimuthal resistivity (UDAR) log data, acquired while drilling, to estimate porosity, water saturation, and facies classes around the wellbore. Despite their limited resolution, seismic data integrated with electromagnetic resistivity log measurements improve the description of rock properties by considering the coupled effects of pore space and fluid saturation in the joint acoustic and electrical domains. The proposed inversion does not explicitly use a forward model, rather the correlation between the petrophysical properties and the resulting geophysical responses is inferred probabilistically from a training data set. The training set is generated by combining available borehole information with a statistical rock-physics modeling approach. In the inversion process, given colocated measurements of seismic AI and logging-while-drilling (LWD) electromagnetic resistivity data, the pointwise probability distribution of rock properties is derived directly from the training data set by applying the kernel density estimation (KDE) algorithm. A nonparametric statistical approach is used to approximate nonsymmetric volumetric distributions of petrophysical properties and to consider the characteristic nonlinear relationship linking water saturation with resistivity. Given an a priori facies classification template for the samples in the training set, it is possible to model the multimodal, facies-dependent behavior of the petrophysical properties, together with their distinctive correlation patterns. A facies-dependent parameterization allows the effect of lithology on acoustic and resistivity responses to be implicitly considered, even though the target properties of inversion are only porosity and saturation. To provide a realistic uncertainty quantification of the estimated rock properties, a plain Bayesian framework is described to account for rock-physics modeling error and to propagate seismic and resistivity data uncertainties to the inversion results. In this respect, the uncertainty related to the scale difference among the well-log data and seismic is addressed by adopting a scale reconciliation strategy. The main feature of the described inversion lies in its fast implementation based on a look-up table that allows rock properties, with their associated uncertainty, to be estimated in real time following the acquisition and inversion of UDAR data. This gives a robust, straightforward, and fast approach that can be effortlessly integrated into existing workflows to support geosteering operations. The inversion is validated on a clastic oil-bearing reservoir, where geosteering was used to guide the placement of a horizontal appraisal well in a complex structural setting. The results show that the proposed methodology provides realistic estimates of the rock-property distributions around the wellbore to depths of investigation of 50 m. These constitute useful information to drive geosteering decisions and can also be used, post-drilling, to update or optimize existing reservoir models.
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
68
审稿时长
12 months
期刊介绍: Covers the application of a wide range of topics, including reservoir characterization, geology and geophysics, core analysis, well logging, well testing, reservoir management, enhanced oil recovery, fluid mechanics, performance prediction, reservoir simulation, digital energy, uncertainty/risk assessment, information management, resource and reserve evaluation, portfolio/asset management, project valuation, and petroleum economics.
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