Method for obtaining overall logging data based on automated reasoning model

A method for obtaining overall logging data based on an automated reasoning model, comprising:

• step (1), acquiring imaging logging data and lab observing data of a stratum;
• step (2), performing data normalization on the imaging logging data and the lab observing data, to form dimensionless data;
• step (3), denoising continuous data obtained by the step (2), to obtain denoised data;
• step (4), automatically marking a to-be-supplemented data point location of the denoised data obtained by the step (3) according to an interval between known point locations of a same type data as the denoised data;
• step (5), performing reasoning on the to-be-supplemented data point location marked in the step (4), to automatically generate data for the to-be-supplemented data point location, comprising:
• generating a bigram (Ŷ, P) for each to-be-supplemented data point location, wherein Ŷ represents a potential value of a current to-be-supplemented data point location, and P represents a probability of taking a value of the current to-be-supplemented data point location as Ŷ;
• taking Ŷ with a maximum probability in the bigram as a prediction value of the to-be-supplemented data point location, to complete data supplementation;
• and wherein the generating a bigram (Ŷ, P) for each to-be-supplemented data point location comprises:
• (a) selecting values of data items in the normalized imaging logging data and values of data items in the normalized lab observing data, to form a list of Ŷ in the bigram;
• (b) taking other known data items of the to-be-supplemented data point location v to form a set DSv={D1, D2, . . . , Dm}, wherein m represents a number of supplemented data items;
• (c) taking, from a current logging data set, data of R/20 point locations with a smallest distance away from the set DSv, to form a set ITEMa;
• taking, from historical data, data of R point locations with a smallest distance away from the set DSv to form a set ITEMb, wherein R is the number of point locations in the current logging data set, and a distance between another point location and a current point location is a sum of absolute values of differences between respective known data items of the two point locations;
• and
• (d) calculating by a following equation:
• PŶ=(Number of times Ŷ appearing in ITEMa*20+Number of times Ŷ appearing in ITEMb)/2R;
• and
• step (6) performing data post-processing to restore a data dimension, to obtain supplemented overall logging data.