Fractured-vuggy oil reservoirs, with the decrease of formation pressure during the exploitation process, lead to the collapse of caverns or the closure of sizeable fractured oil channels, which seriously affects oil well production and the recovery rate of oil reservoirs. True three-dimensional geomechanical model tests were carried out to avoid the impact of cave collapse and fracture closure on oil well production. Taking the Tahe Oilfield in Xinjiang area of China as the engineering background, we researched the collapse failure mechanism of the karstic caves in fractured-cavity oil reservoirs and the evolution of fracture closure through a true three-dimensional geomechanical model test and the numerical simulation software RFPA. The collapse failure modes of caverns with and without prefabricated cracks were revealed, along with the displacement and stress changes during cave collapse and the mechanism of cave collapse failure. Our study revealed the mechanism of the interaction between cracks and the cave. The research results show that prefabricated cracks reduce the roof of the cave’s bearing capacity, making the karst cave collapse with incomplete cracks. The impact of the collapse is much smaller than the cavern without prefabricated cracks. The crack closure extends from the near end to the far end. The research results will provide necessary theoretical support for the large-scale safe extraction of deep petroleum resources, increase oil production in China, and have important theoretical significance and engineering application value.
Carbonate rock oilfields account for two-thirds of proven marine carbonate oilfield reserves, which are the primary way to increase future oil and gas energy reserves. Cave collapses occur during the process of oil reservoir development, seriously affecting oil production. In order to reveal the collapse failure mechanism of carbonate karst caves and predict whether the fracture cave type oil reservoir will collapse before drilling, a binary depth reduction method for determining the critical collapse depth of karst caves is proposed based on the Tahe fracture cave type oil reservoir. The sensitivity of karst cave collapses to multiple factors is analyzed, and a prediction formula for the critical collapse depth of karst caves with changes in the deformation modulus, the internal friction angle, and the cohesion is established through multiple regression analysis. By calculating and analyzing the numerical values of a large number of operating conditions under different mechanical parameters, the failure process, failure mode, and the change law of collapse depth during the Tahe oilfield destruction process were obtained. We used the established formula for predicting the collapse depth of karst caves to predict and analyze the actual distribution of karst caves in the Tahe oilfield. The calculation and analysis results showed that in the karst cave failure mode characterized by vertical shear failure, the cohesive force is the most sensitive factor affecting cave collapse, followed by the internal friction angle. The deformation modulus is hardly sensitive to the influence of the karst collapse. Through the geomechanical model test, the result verified the accuracy and reliability of the calculation results. The research results will provide necessary theoretical support for the large-scale safe extraction of deep petroleum resources, increase oil production in China, and have important theoretical significance and engineering application value.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.