Without doubt, one of the most frequently occurring problems in production facilities with dire consequences is sand production. Sand production refers to the continuous flow of formation grains alongside reservoir fluids during production. It is a problem that is more associated with unconsolidated reservoirs such as those present in the Niger Delta. Some of the problems associated with sand production include stabilization of emulsion, vessel blockage, erosion of vessels and reduction in separation effectiveness (Bibobra et al, 2015) all of which have economic consequences, thus, can render companies bankrupt. In a bid to avoid the aforementioned problems, many sand control measures have been developed, however, with increase in effectiveness in handling sand comes a corresponding increase in cost, hence, necessitating a feasibility study to ascertain their viability. Many authors have developed mathematical models useful in predicting sand production in reservoirs. These models have proved to be useful tools in sand control viability studies. Geomechanical parameters like principal stresses have been useful in these models. However, many of these models developed have turned out complex, requiring difficult-to-obtain parameters or having low level of accuracy when compared to observed field data. In this paper, a mathematical model was developed by modifying the work of Oluyemi and Oyeneyin (2010) who developed a simple mechanistic model requiring few and easy-to-obtain input parameters. A simple simulator, named Cassandra, was then designed using Python Programming Language so as to aid the estimation process. After validation with field data from different reservoirs, it was found that Cassandra gave results very close to observed field data, in fact, it only possesses about 8% absolute error. The model also performed excellently when compared to existing models. This software, thus, proves to be a valuable tool in any sand production analysis.
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.