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Clustering-Based Optimal Perforation Design Using Well Logs

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In an effort to better understand the well performance in one of the Chevron's assets in San Joaquin Valley, a study was conducted to evaluate the perforation strategies and capture best practices. Well completion through perforation is typically performed using bare essential technology such as wireline logs and perforation guns. For basic reservoir formations, simple rules-of-thumb are used for perforation spacing and interval lengths. These are rarely validated by other methods, such as production logging and micro-seismic monitoring. For more challenging lithology, a more appropriate approach would be to place perforation clusters in target formations with similar properties. The research presents an efficient use of fuzzy clustering technology for identification of the optimum perforation strategy in a challenging waterflood diatomite reservoir. The methodology was applied on all newly drilled wells in the reservoir (within the last two years), and we found that this new approach improved our understanding over previous practices, not only by designing optimum perforations, but also an increased production was observed. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other (in some sense or another) than to those in other clusters. There are two commonly used types of clustering methods: hard and fuzzy clustering. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels. The fuzzy clustering algorithm, also known as Fuzzy C-Mean (FCM) algorithm, was applied to log data of wells from different areas of a reservoir. Based on the clustering results, the workflow then identified whether the perforation was performed on "good" regions (sand) or on "bad" regions (shale bedding). This information allowed the evaluation of the perforation jobs executed and also allowed capturing best practices and design changes for future well completions. The case study presents a simple, yet efficient workflow to extract additional information from logs and improve completion strategies and perforation design. The methodology is flexible and can be applied to any well where complex lithology creates a challenge in defining the optimum perforation intervals
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Keywords: FUZZY C-MEAN; HYDRAULIC FRACTURING; LOG ANALYSIS; WELL PERFORATION DESIGN

Document Type: Research Article

Publication date: 2016-06-01

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