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

Abstract

The opportunity to detect boundaries ahead of the bit has long been a desire within the oil and gas industry as it would allow precise geo-stopping prior to entering unwanted formations and/or fluids. There are solutions already available such as the use of seismic applications. However these are for use on a different resolution scale, the accuracy needed for geo-stopping within meters of a formation boundary is lacking. Extra Deep Azimuthal Resistivity (EDAR) has been widely used for geosteering and landing applications around the world utilizing the common methodology of looking around and predicting ahead (see, for example, Larsen et. al., 2016). This methodology has been very efficient in a horizontal drilling environment with fairly low dipping layers, typically when incident angles do not exceed 10-15 degrees. In such wells, the environment ahead of the bit is generally not changing abruptly and therefore provides only a minor contribution to tool response compared to the volume around the tool. For higher incident angles, the zone of interest is likely to be ahead of the bit. Here traditional looking around the wellbore and predicting ahead is no longer sufficient. The steep angle of approaching beds requires sensitivity ahead of the bit in order to detect and predict upcoming resistivity boundaries. In this paper we will study the extended capabilities of the interpretation method currently used only in high angle and horizontal (HA/HZ) well applications. We will present sensitivity analysis of the theoretical capability of detecting ahead of bit with the different key factors determining the capability, such as sensor placement behind bit, formation resistivity and incident angle. The study will show that the application range of EDAR can be extended from the 15° incident angle range to up to 90° incident angle. The extension allows using the service for low angle geo-stopping applications. The analysis is performed on theoretical data as well as a case study from a well in the Middle East.

SLF/ELF Fields From VED Solved by Normalized Spherical Harmonic Functions

“…The Schumann resonance has been observed at frequencies as 7.5 Hz, 14.5 and 20.5 Hz (Larsen et al., 2018). It could also be used to verify the performance of the proposed method.…”

Modeling extra-deep electromagnetic logs using a deep neural network

“…The most useful and used measurements for geosteering are electromagnetic (EM) measurements, which combine reliability with deep sensitivity (depth of investigation). The current generation of extradeep EM tools (also referred to as ultra-deep EM) can detect remote boundaries up to 60 m away from measurement location (Wu et al, 2019), and recent publications have also revealed sensitivity ahead of bit (Larsen et al, 2018).…”

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

“…Third, a forward deep neural network (FDNN) converts the 1D inputs to synthetic EM logs: a full suite of extra-deep EM log traces transmitted in real-time during drilling(Alyaev et al, 2021). The extra-deep EM logs include 22 measurements with a sensitivity of up to 20 meters from the tool(Larsen et al 2018). For one realization, the entire modelling sequence takes around 0.12s to model nine logging positions along the well path on a 3.3 GHz (4.5 GHz turbo) core.…”