Developing tight sandstone across vast area requires proper data collection and analysis. Due to the tight nature and heterogeneity of these reservoirs, several vertical and horizontal wells need to be drilled and completed with multistage hydraulic fractures to assess their potential. Initial post-frac flowback tests, in addition to long-term pressure build-ups, have already been conducted on several of the wells. Data Analysis have assisted in characterization of the tight hydrocarbon reservoirs and evaluating of hydraulic fracture geometry. The results have aided to investigate the drainage radius and well interference, to determine the optimal frac and well spacing design. These information are highly needed to build and calibrate single and full field dynamic models to estimate and address the uncertainty on the ultimate recovery and to come up with an optimized development strategy of the field. The paper presents findings and key lessons learned to efficiently design pressure build-up tests in tight sandstone reservoirs.
All producing wells experience reservoir pressure depletion which will ultimately cause production to cease. However, the accumulation of wellbore liquid known as liquid loading can reduce production at a faster rate bringing forward the end of well life. In theory, there are many works written on liquid loading in unconventional wells however, these assumptions are challenged when implemented in the field. The aim of this paper is to investigate the relationship between empirical and mechanistic methods used to determine liquid loading critical rates for volatile oil and gas condensate wells, improving liquid loading forecast workflow for future wells. The study was carried on a wide Pressure, Volume, and Temperature (PVT) window with varying compositions ranging from gas condensate to volatile oils. Wells with liquid loading exhibit sharp drops and fluctuations in production. Due to the wide variation in composition however, correlations used must be varied whilst accounting for both composition and horizontal configuration of the well. Using Nodal Analysis methods, Inflow Performance Relationships (IPR) and Vertical Lift Profile (VLP) curves were created from different correlation models fitted for multiple wells selected for this study to optimize well performance. By combining theoretical analysis and field practices for estimating liquid loading critical rate, the appropriate workflow was determined for the volatile oil and gas condensate wells. When comparing the critical rate for liquid loading calculated from theoretical methods against actual rates seen in the field, an inconsistency was observed between the two values for several wells. By establishing a relationship between field estimate and theoretical calculations, liquid loading was forecasted with greater certainty for varying PVT windows. When the liquid loading rate is determined earlier on, the production efficiency can be improved by deploying unloading measures, increasing the well’s producing life, and ultimately alleviating economic losses. By investigating, we were able to establish a suitable process to predict liquid loading critical rates for volatile oil and gas condensate wells. This workflow can be utilized by production engineers to arrange for liquid loading mitigation increasing well life and improving well economics.
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.