Wellbore scaling is a complex and one of the common problems encountered during the depletion of an oilfield. Many studies have been conducted on general scale mechanisms, scale predictions, and removal measurements. However, the detailed study of the scaling characteristics and mechanisms in Huanjiang oilfield is limited. The objective of this work is to investigate the scaling mechanisms and characteristics to provide guidance for scale inhibitor selection, synthesis, and testing in the Huanjiang oilfield. Ion chromatography (IC) was used to test the composition of 100 water samples, and energy dispersive spectroscopy (EDS), scanning electron microscope (SEM), and X-ray diffraction (XRD) were utilized to analyze the composition of 120 wellbore scale samples that were collected from the Huanjiang oilfield. The results show that the water types of formation and groundwater are CaCl2 and Na2SO4, respectively. The oil wells produced from Chang
4
+
5
, Chang 6, and Chang 8 reservoir layers in the development of Yanchang group are mainly calcium-based scale (CaCO3 and CaSO4), supplemented by wax deposition scale, corrosion scale, and NaCl and KCl crystal scale. In contrast, the oil wells in Yan’an group (Yan 6, Yan 7, Yan 8, Yan 9, and Yan 10 reservoir layers) are mainly wax deposition scale and corrosion scale.
Steam flooding is
a complex process that has been considered as
an effective enhanced oil recovery technique in both heavy oil and
light oil reservoirs. Many studies have been conducted on different
sets of steam flooding projects using the conventional data analysis
methods, while the implementation of machine learning algorithms to
find the hidden patterns is rarely found. In this study, a hierarchical
clustering algorithm (HCA) coupled with principal component analysis
is used to analyze the steam flooding projects worldwide. The goal
of this research is to group similar steam flooding projects into
the same cluster so that valuable operational design experiences and
production performance from the analogue cases can be referenced for
decision-making. Besides, hidden patterns embedded in steam flooding
applications can be revealed based on data characteristics of each
cluster for different reservoir/fluid conditions. In this research,
principal component analysis is applied to project original data to
a new feature space, which finds two principal components to represent
the eight reservoir/fluid parameters (8D) but still retain about 90%
of the variance. HCA is implemented with the optimized design of five
clusters, Euclidean distance, and Ward’s linkage method. The
results of the hierarchical clustering depict that each cluster detects
a unique range of each property, and the analogue cases present that
fields under similar reservoir/fluid conditions could share similar
operational design and production performance.
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