A technique for automatic dip determination is described which uses novel methods frequently employed in solving artificial intelligence problems. Formation dip computations can be separated into three stages:
Separate pre-processing and analysis of each of the
Determination of formation dip through the combi-.
With the new technique, pre-processing involves the use of frequency and statistical analysis of each individual microresistivity measurement through the application of techniques paralleling those used in image processing. Curve-to-curve matching is performed by dynamic programming optimization, providing the large number of curve-to-curve correlations required for detailed stratigraphic interpretation. A number of ordered pad combinations are used to produce a picture of all possible dips. The resulting sum of all possible dips may, however, be contradictory due to noise included in the original data acquisition. To alleviate this problem, mathematical optimization is applied to select the proper geologically-meaningful dip computations. The resulting computation provides a very detailed and precise series of dip computations presented in a format which lends itself to meaningful stratigraphic interpretation. four microresistivity measurements nation of the above correlations.