A method for identifying the lithology of clastic rocks in high-salt and potassium-rich areas developed by water flooding

Abstract

The invention discloses a method for identifying lithology of high-salt potassium-rich region water-flooding oil reservoir clastic rocks. Statistics is performed on rock core analytical assay data of known lithology and the lithology is classified, and n logging parameters sensitive in reflection to lithology are selected; and different lithology identification equations are established based on the principle of mathematical statistics, then single well no-coring segment logging data cubes are respectively substituted in all the lithology identification equations, the identification equation with the maximum function value has the highest probability of corresponding lithology according to the identification criterion, and the lithology is determined as the corresponding lithology. The lithology of the high-salt potassium-rich region water-flooding oil reservoir clastic rocks is identified through fitting of an integrated discrimination curve so that accurate discrimination of multiple types of lithology can be realized, continuous stratum lithological sections can be established, self-check coincidence rate can be 90%, the identification effect is great and thus the method has certain reference meaning for lithology identification of similar regions.

Description

technical field [0001] The invention belongs to the technical field of reservoir evaluation, and relates to a method for identifying the lithology of clastic rocks in water injection development oil reservoirs in high-salt and potassium-rich areas. Background technique [0002] Well logging lithology identification is the basis and key to accurately obtain reservoir parameters and carry out reservoir evaluation. The lithology of clastic rocks in water injection reservoirs is generally identified by logging data such as spontaneous potential (SP), natural gamma ray (GR), acoustic time difference (AC), density (DEN) and resistivity (RT). [0003] Conventional identification methods mainly include crossplot method, neural network method, and curve overlapping method, etc., but in high-salt and potassium-rich areas, the identification rate is low, and the formation lithology cannot be continuously identified. The geological conditions in the beach area are complex, and have the...

AI Technical Summary

Problems solved by technology

[0003] Conventional identification methods mainly include crossplot method, neural network method, and curve overlapping method, etc., but in high-salt and potassium-rich areas, the identification rate is low and the formation lithology cannot be continuously identified.

The geological conditions in the beach area are complex, and have the characteristics of high salt and rich potassium. The salt slurry base fluid makes the natural potential reverse positive anomaly, and the resistivity value is lower than normal, so it cannot truly reflect the original formation characteristics; there are high potassium in the formation. Minerals, which in turn reduce the identification accuracy of the natural gamma ray curve

Therefore, a single logging curve cannot effectively reflect the lithological characteristics of clastic rocks, and conventional methods cannot accurately distinguish different types of lithology.

Images