Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model...

https://library.seg.org/doi/pdf/10.1190/geo2020-0389.1

Modeling extra-deep electromagnetic logs using a deep neural network

Hydrocarbon reservoirs are often located in spatially complex and uncertain geologic environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geologic information, known as well geosteering. To effectively place the well in an updated geomodel and support well geosteering decisions in real time, we apply an iterative inversion approach based on the Levenberg-Marquardt form of the ensemble randomized maximum likelihood method. The method estimates geomodel properties together with their uncertainties by reducing the statistical misfit between the measurements acquired with well-logging tools and the predicted measurements from numerical simulations. Analyses of synthetic cases indicate that the method’s reliability and computational speed depend on the distance from the logging tool to formation boundaries, the contrast of model properties, and the thickness of formation layers. Our method delivers reliable estimates of model properties with only 40 ensemble members and 2–10 iterations; hence, it is approximately 10–125 times faster than Metropolis-Hastings Monte Carlo, which we use as a baseline condition given its proven track record. Likewise, our method is amenable to parallelization to further reduce computational times. Implementation of the method with a synthetic example inspired by a historical well geosteering operation yields accurate formation evaluation and verifies its accurate and reliable performance under complex geologic conditions. 

Ensemble-based well-log interpretation and uncertainty quantification for well geosteering

This paper proposes a new tool design of ultra-deep azimuthal electromagnetic (EM) resistivity logging while drilling (LWD) for deeper geosteering and formation evaluation, which can benefit hydrocarbon exploration and development. First, a forward numerical simulation of azimuthal EM resistivity LWD is created based on the fast Hankel transform (FHT) method, and its accuracy is confirmed under classic formation conditions. Then, a reasonable range of tool parameters is designed by analyzing the logging response. However, modern technological limitations pose challenges to selecting appropriate tool parameters for ultra-deep azimuthal detection under detectable signal conditions. Therefore, this paper uses grey relational analysis (GRA) to quantify the influence of tool parameters on voltage and azimuthal investigation depth. After analyzing thousands of simulation data under different environmental conditions, the random forest is used to fit data and identify an optimal combination of tool parameters due to its high efficiency and accuracy. Finally, the structure of the ultra-deep azimuthal EM resistivity LWD tool is designed with a theoretical azimuthal investigation depth of 27.42–29.89 m in classic different isotropic and anisotropic formations. This design serves as a reliable theoretical foundation for efficient geosteering and formation evaluation in high-angle and horizontal (HA/HZ) wells in the future.

Study on Ultra-deep Azimuthal Electromagnetic Resistivity LWD Tool by Influence Quantification on Azimuthal Depth of Investigation and Real Signal

Boundary Detection Ahead of the Bit – A Sensitivity Study of Extra Deep Azimuthal Resistivity

Quantitative workflows utilizing real-time data to constrain uncertainty have the potential to significantly improve geosteering. Fast updates based on real-time data are particularly important when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geological modelling and mathematically robust parameterization. We propose a generative adversarial deep neural network (GAN), which is trained to reproduce geologically consistent 2D sections of fluvial successions. Offline training produces a fast GAN-based approximation of complex geology parameterized as a 60-dimensional model vector with standard Gaussian distribution of each component. Probabilistic forecasts are generated using an ensemble of equiprobable model vector realizations. A forward-modelling sequence, including a GAN, converts the initial (prior) ensemble of realizations into EM log predictions. An ensemble smoother minimizes statistical misfits between predictions and real-time data, yielding an update of model vectors and reduced uncertainty around the well. Updates can then be translated to probabilistic predictions of facies and resistivities. This paper demonstrates a workflow for geosteering in an outcrop-based synthetic fluvial succession.In our example, the method reduces uncertainty and correctly predicts most of the major geological features up to 500 m ahead of drill-bit.The condensed summary is also submitted for presentation at the 3rd EAGE/SPE Geosteering Workshop to be held 2–4 November 2021, online.

Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network

Extra-Deep Azimuthal Resistivity for Enhanced Reservoir Navigation in a Complex Reservoir in the Barents Sea

Navigating the Horizontal Section in a Heterogeneous Formation While Using Extra Deep Azimuthal Resistivity for Optimizing the Wellbore Placement within a Narrow TVD Window

Well Placement and Reservoir Characterization Challenges on Peregrino Mitigated by Development of Extra-Deep Azimuthal EM Technology

The Importance of Extra-Deep Azimuthal Resistivity in Reservoir Development: An Update on Recent Field Experiences

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