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A robust inversion of logging-while-drilling responses based on deep neural network

Zhu Gaoyang, Gao Muzhi, Wang Bin

Acta Geophysica

2024

Vol. 72, no. 1

129--139

Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations.

Significance of real-time petro-physical data for an optimal remote geosteering operation in complex geological reservoirs

Amer Muhammad Attique, Ehsan Muhsan, Akhtar Muhammad Javid

Geology, Geophysics and Environment

2021

Vol. 47, no. 3

125--130

In the last decade, the oil industry has transitioned from primarily drilling vertical wells to a majority of extended reach complex horizontal wells with help of geosteering techniques for better reservoir and production efficiencies. The overall objective of geosteering and well placement has helped to maximize reservoir contact by the keeping well trajectory in the pay zone with the help of quality real-time data, especially in these uncertain times of pandemic due to COVID-19. This paper illustrates the immense impact of real-time data feeds (special logging tools/images) at operation centers with the full remote expert support of multidisciplinary teams. The proper data communication mechanism is also helpful to share the information across asset teams in a timely manner to drill complex wells remotely. To achieve the desired geosteering objectives, experts monitor and evaluate the real-time data in a landing well accurately in the pay horizon by designing the well path which considers geological variables like (reservoir architecture, permeability and porosity distribution, and fluid contacts) which further helps update the reservoir models in real-time based on the latest subsurface information for better reservoir management and timely decision making.