Fault automatic identification method and system based on ant colony optimization algorithm

Abstract

The invention discloses a fault automatic identification method and system based on an ant colony optimization algorithm. The fault automatic identification method comprises the following steps of: step 1, inputting a coherent attribute data volume, and setting an ant number and an iterative threshold; step 2, determining an ant moving direction according to the transition probability matrix; step 3, judging whether ants pass through all input data or not, if so, entering the step 4, and if not, returning to the step 2; step 4, judging whether the ant serial number is smaller than the ant number or not, if so, increasing the number of iterations and the ant serial number, returning to the step 2 until all ants pass through all input data, and entering the step 5; step 5, calculating a pheromone tau value; and step 6, judging whether the number of iterations reaches an iteration threshold, if so, outputting a fault profile, and if not, repeating the step 2-6 until the number of iterations reaches the iteration threshold. The coherent attribute data volume is tracked through the fault ant colony optimization algorithm, and a clear and continuous fault profile is obtained.

Description

technical field [0001] The invention relates to the field of petroleum exploration and development, and more specifically, to an automatic fault identification method and system based on an ant colony optimization algorithm. Background technique [0002] With the increasing complexity of oil and gas fields and the refinement of oil and gas exploration targets, accurately judging the distribution and shape of faults plays an important role in accurately identifying and describing oil and gas reservoirs. [0003] Conventional data interpretation of 3D data can obtain stratigraphic and lithological information, but this process requires a lot of work and it is difficult to obtain direct information about faults and special lithology in the data, so a series of fault-enhanced attribute algorithms have been developed to obtain more For direct and accurate tomographic images. [0004] At present, there are mainly several coherent volume algorithms for extracting fault features. M...

AI Technical Summary

Problems solved by technology

Randen (2001) proposed to use the "artificial ant" algorithm for noise suppression; S.I.Pedersen (2003) processed seismic data through "artificial ants" to obtain fault enhancement attribute volumes to identify faults, which has a better effect on noise suppression; Dorm and James (2005) realized the automatic extraction of faults by combining geological information to process seismic signals. This method enhanced the fault characteristics of the coherent data volume and suppressed the noise well, but it was not very good for the continuity of the coherent volume. Improvement, making it difficult to combine fault planes

Zhao Wei (2009) used coherent data volumes as grayscale data to perform clustering analysis through ant colony algorithm, but the modified method could not judge the fault position well, and the calculation efficiency was low, so it could only be applied to small data

Images