Fast hierarchical inversion for borehole resistivity measurements in high-angle and horizontal wells using ADNN-AMLM

Numerical geometrical factors (GFs) are developed for array laterolog measurements in one-dimensional (1D) cylindrically and planarly layered formations. The kernel of GFs assumes that laterolog responses can be expressed in terms of linear superposition of formation resistivities at different units. Two techniques, namely physics-driven modeling and data-driven approximation, have been employed to ensure both computational accuracy and efficiency of GFs. Physics-driven modeling utilizes a three-dimensional finite element algorithm to generate high-precision and fully labeled GFs data and library. Subsequently, these predetermined data are trained offline using classical neural network algorithm to establish an implicit relationship between formation parameters and GFs. The joint physics and data-driven GFs presents three main advantages. Firstly, it enables the direct representation of the spatial sensitivity of layered formations both laterally and horizontally in vertical and horizontal wells. Secondly, GFs can be used to rapidly calculate the array laterolog responses. The computation speed can be enhanced by thousands of times in comparison with traditional physic-driven modeling, allowing for real-time data processing. Another noteworthy aspect is that GFs forward modeling can handle formations with arbitrary layers, thereby resolving the fixed-layer modeling issue inherent in traditional data-driven methods. Thirdly, the GFs can be employed to approximate the derivative of logging response with respect to formation resistivity, significantly reducing the complexity of Jacobian matrix calculation for deterministic inversion. These numerical GFs not only serve as an excellent alternative simulator for array laterolog, but also will be helpful to accelerate the modeling and inversion of other logging measurement in layered structures.

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Detection Performance and Grading-Selection-Based Inversion of Low-Frequency Alternating Current Resistivity Logging in Moderate-High Resistance Formations

Fan

Cai

et al. 2022

SPE Journal

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Summary With the development of high-angle/horizontal well technology, unconventional tight reservoirs with moderate/high resistivity have gradually become the focus of exploration and development. However, in moderate-high-resistance reservoir environments, distorted responses can be observed regularly in the array laterolog curves, which will lead to misjudgment for the oil-gas reservoirs. Therefore, the detection performance of the array lateral instrument powered by low-frequency alternating current urgently needs to be clarified and the true resistivity of the formation is necessary to be analyzed and extracted. In this paper, the reasons for distorted responses are investigated as well as the qualitative and quantitative assessments of the detection performance of low-frequency alternating current lateral instrument in moderate-high resistance formations. Moreover, a rapid inversion method is proposed based on the initial value grading optimization strategy to invert the anisotropic resistivities from the observed data with outliers. Numerical simulation results show that the current flow characteristics vary significantly across stratigraphic models and the distorted responses are primarily caused by the high-resistance characteristics and borehole enlargement. The combination of the initial value grading optimization strategy and the regularization inversion algorithm improves the accuracy of the inverted resistivities even further. Synthetic and field examples are analyzed to illustrate the detailed implementations and the performance of the proposed inversion method.

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Fast hierarchical inversion for borehole resistivity measurements in high-angle and horizontal wells using ADNN-AMLM

Cited by 3 publications

References 23 publications

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Semi-supervised learning seismic inversion based on Spatio-temporal sequence residual modeling neural network

The joint physics and data-driven geometrical factor of array laterolog in layered formations

Detection Performance and Grading-Selection-Based Inversion of Low-Frequency Alternating Current Resistivity Logging in Moderate-High Resistance Formations

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