Study On The Data Processing Method Of Triaxial Induction Logging

In recently 10 years, people pay much attention to a new induction logging technology called fully triaxial induction logging. It can detect and characterize resistivity anisotropy and provide the information of formation dips and azimuths that are essential in charactering and developing low-resistivity reservoirs such as thinly laminated sand-shale sequences. A newly developed fully triaxial induction tool by Schlumberger is designed to great minimize the effects of borehole environments, deep invasion and tool eccentricity by using conductive elements in tool body and great improve the practical of the instrument.The fully triaxial induction logging responses simultaneously depend on formation horizontal conductivityσh, vertical conductivityσv, borehole (or formation) dipαand the tool azimuth? and are highly non-linear functions, in addition to the strong effects by borehole and adjacent layers that make the responses more complex, until now, people generally agree that it is hard to process the logging data with visual explanation and the reliable results can only be obtained by nonlinear multi-parameter inversion.However, the fully triaxial induction logging is a 3D problem and no analytic forward modeling while the tool traverses a dipping TI layered formation with borehole and invasion. The 3D finite-difference modeling is generally used to solve the numerical solution that is now difficult to adapt to the requirements of data inversion for slow computing speed and very large workload. Hence, people always remove the borehole effect from the actual field logs first and then inverse the borehole-effect-corrected data with one-dimensional horizontal layered TI forward modeling.In this paper, we derive an analytic solution in horizontal layered anisotropic formation with no borehole and invasion by using TE and TM wave decomposing method to simulate the response of a fully triaxial induction tool in dipping well, which provides a high efficient forward modeling for logging data inversion. However, it is difficult to provide a reasonable initial model for inversion when we can't make the visual explanation of logging data. It increases the difficulty even for vertical 1D inversion and also can not meet the real-time interpretation in scene that restricts the extensive application and further development of fully triaxial induction tool. So, it is still a challenging problem for fully triaxial induction logging data processing and interpretation, and the key problem is the visual interpretation of the logging data.Now, people commonly determine the azimuth? first, and then transform the nine actual field logging curves in the tool coordinate system to the five logging curves in the borehole coordinate system, and make logging data processing and interpretation with them at last. We found that there exists the uncertainty to determine the azimuth? or( ? +π)using the existing method and the precisions of obtained five response curves depend on the precision of the extracted? . In this paper, we develop a fine algorithm to directly obtain five response curves independent of the precision of the extracted ? from nine actual field logging curves in the tool coordinate system.Because of each response curve simultaneously depending onσh,σvandαin addition to some complex factors such as borehole and invasion effects, it is hard to make the visual explanation of logging data. The most meaningful result in this paper is that we found a fine combination curve with the principle components and cross-component via different weight coefficient relevant toαin borehole coordinate system calledσha, which has high vertical resolution and can reconstruct the distribution of horizontal conductivityσhwith a quasi-block response curve. We also provide an algorithm to determine the apparent dipαavia the scanning of dipαusing the combinationσha.The vertical resolution of the combination curveαacan be characterized by L cosα, where, L means the distance between transmitter and receiver. In highly deviated well, the combination curveσhahas high vertical resolution to visually determine the location of the bed boundary including thinly laminated sand-shale sequences with short transmitter-to-receiver structure. The value ofσhaon electric midpoint each layer is also very close to the true horizontal conductivity in the layers with thickness greater then L cosα.The combination curveσha fully reflects the vertical resolving power of the triaxial induction tool with visual display, significantly improves its ability to data visual explanation. In this paper, we develop a data visual explanation method and process with the combination curveσhaas the core for trixial induction logging. This visual explanation results are also considered as the initial value for nonlinear multi-parameter inversion. Some vertical 1D inversions of typical horizontal layered TI dipping bed have been done using simulating logging data and obtained satisfactory results. The iteration of inversion is fast and high precision. This is largely due to a very good vertical resolution ofσhathat provides a good initial value.The fully triaxial induction logging data processing and interpretation method with the core of a combination curveσhain this paper not only significantly improve the capacity and effectiveness of the logging data visual interpretation, but also makes the real-time logging data interpretation possible. That helps promote the extension and utilization of the equipment, and provide the support of data processing and interpretation methods for the further development of the triaxial induction tool to logging-while-drilling (LWD) and array induction logging. For geosteering, logging data interpretation in real-time is extremely important in order to arrange and adjust the location and direction of the borehole. The results in the section 3.8 show the preliminary possibility for geosteering of a fully triaxial induction tool. In addition, during a certain period, a large number data inversion of triaxial induction logging using 3D numerical modeling is unrealistic, however, making a similar data processing and interpretation as array induction tool is a more realistic way. As long as there are several different probes to detect the different formation depth and correcting the borehole effect of each measured data from corresponding probe, the vertical 1D inversion can be implemented first using our method mentioned in this paper in order to meet the match of the vertical resolution for different logging curve. The difference of the electric parametersσhandσvbetween the results of inversion of different detecting depth helps to explain the inhomogeneity of formation in radial, that is, borehole, invasion and raw formation.Some work in this paper needed to improvement and further study. First of all, we only study the combination curveσhawith one formation model including borehole and invasion, and the accuracy of its data is also not high enough. There still need higher precision simulating logging data and more formation model to improve it. Secondly, the logging data studied in this article is noise-free data, as a result of combination curveσhastudied in this article including the cross–coupling component that has lower signal to noise ratio than the main component in the middle point of bed, especially the near the central of isotropic thick bed, its anti-noise ability derive our attention and also need to be tested in the final through the actual logging data. In short, the results of this paper are still preliminary and lots of work remains to be done. We will continue to work hard.