Since LWD deep azimuthal resistivity service was first introduced a decade ago followed by the ultra-deep azimuthal resistivity a few years ago, the new service has been under spotlight and drawn great attention from operators. The azimuthal propagation resistivity tools all use the concept of multi-spacings, multi-frequencies, and multi-components. The measurements acquired by the tool are much richer than that of the conventional omni-directional propagation resistivity. The applications of the new service are widely ranged from the well placement, the reservoir mapping, the geo-stopping, the landing, the fault detection to the salt edge detection, etc. However, due to the complexity of the measurement physics, the tool response characteristics and the data processing/inversion, without the well understanding the uncertainty of the service, operators do not have sufficient confidence to use the service as much as expected.

To promote the understanding of the technology and clear many questions surfing around the industry, in this paper, we systematically study the sensitivity and quantify the uncertainty of the azimuthal propagation resistivity technology in various formation model.

The sensitivity of the measurements to the dip angle, the anisotropy, the layer boundaries, and the formation resistivity is essential to assess the capability of the technology for practical applications such as the reservoir boundary mapping, the formation evaluation and the well placement. A group of studies is conducted to evaluate the sensitivity under several common situations including homogeneous isotropy formation, homogeneous anisotropy formation, and layered formation. The information content of the measurements and the proper use of the measurements is clearly demonstrated.

The depth of detection (DoD) in a two-layer formation presented in the format of "Picasso" plot is studied. The common practice to produce "Picasso" plot is based on the noise threshold of the measurement which is not always realistic. It only reflects the quality of the measurements rather than the quality (error bar) of the distance to boundary (D2B) resulting from the inversion processing. D2B-error-based DoD is investigated in the paper. The comparison between these two methods reveals the commonly used, noise-based DoD is considerably overestimated.

A set of 1D formation models, proposed by SPWLA Resistivity Special Interest Group (RtSIG) chapter, is used to quantify the uncertainty of the bed boundary position, the formation resistivity, the dip angle through a novel statistical analysis, the trans-dimensional Markov Chain Monte Carlo (tMCMC) method. The probability maps of the boundary interface and the distribution of the resistivity profile can be extracted from the statistical characteristics of the posterior probability distribution (PPD). The exercise of the statistical solver on the formation models recommended by SPWLA RtSIG demonstrates that the uncertainty quantification techniques can be crucial to assess the azimuthal propagation resistivity technology.

A field example from a subsea gas well of Wheatstone liquefied-natural-gas project in Western Australia is used to confirm the importance of the uncertainty quantification in evaluating the capacity of the azimuthal propagation resistivity measurements.

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