Application of GPC-UV Method for Reservoir Fluid Analysis From Drill Cuttings

钻屑 演习 石油工程 地质学 钻井液 钻探 工程类 机械工程
作者
Tao Yang,Alexandra Cely,Julian Moore,Eric Michael
出处
期刊:Petrophysics [Society of Petrophysicists and Well Log Analysts (SPWLA)]
卷期号:65 (4): 593-603
标识
DOI:10.30632/pjv65n4-2024a12
摘要

Drill cuttings are readily available for all wells we drill. Geochemical analysis has been employed to estimate key oil properties, such as American Petroleum Institute (API) gravity and viscosity. However, most studies focus on drill cuttings with water-based mud (WBM). Identifying reservoir fluids in highly contaminated drill-cutting samples becomes a significant challenge when drilling wells with oil-based mud (OBM). Consequently, there is a high business demand for predicting reservoir fluid properties from drill cuttings with OBM. Recently, gel permeation chromatography (GPC) coupled with ultraviolet (UV) absorbance detection was introduced in the upstream industry. Elias and Gelin (2015) and Elias et al. (2017) demonstrated the capability of the GPC-UV method to predict API gravity from drill-cutting samples with OBM in unconventional reservoirs. This study extended the GPC-UV approach to conventional reservoirs across multiple fields from the Norwegian Continental Shelf and other global assets. We developed a multiple-wavelength method instead of fixed wavelength detection to explore correlations between GPC-UV detections and reservoir fluid properties. The drill cuttings used in this study are from multiple fields from the Norwegian Continental Shelf and other global assets, where OBM was consistently used for well drilling. Consequently, extracts from these cutting samples are contaminated by OBM. Utilizing the GPC-UV method revealed clear oil peaks with the OBM response appearing on the baseline. Correlating these results with GPC-UV data from stock tank oil samples and known reservoir fluid properties enables qualitative determination of fluid type (gas, oil, or water) and estimation of API from new drill-cutting samples.A digital solution based on machine learning, leveraging broad GPC-UV measurements, is needed to improve prediction accuracy further. While ongoing studies aim to establish a comprehensive database of GPC-UV measurements for stock tank oil and drill-cutting extracts, the GPC-UV method demonstrates impressive potential for analyzing reservoir fluids in challenging drill-cutting samples. Given the widespread availability of drill-cutting samples, this new method offers a cost-efficient and accurate means of determining reservoir fluid properties without resorting to downhole measurements or sampling. This method could fulfill the vision of considering “every piece of cutting as a PVT sample,” with applications ranging from well placement and reservoir management to production optimization, flow assurance, and plug and abandonment (P&A).

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