Munir Muhammad Nofal, Zafar Mohammad, Ali Abid, Ehsan Muhsan, Abdelrahman Kamal, Radwan Ahmed E, Al-Awah Hezam
Department of Earth and Environmental Sciences, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad 44000, Pakistan.
Research and Development GVERSE Geographix, LMK Resources, Islamabad 44000, Pakistan.
ACS Omega. 2024 Jul 25;9(31):33397-33407. doi: 10.1021/acsomega.3c08403. eCollection 2024 Aug 6.
To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. K-means cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.
为了构建一个强大的储层模型,岩石类型识别是一项至关重要的任务。在非均质储层(如帕布组)中,利用测井资料识别具有良好储层质量的层段对于更好地进行勘探至关重要,因为由于时间和成本限制,取心方案往往不切实际。岩石类型由特定的测井响应来描述,最终借助电相进行区分。本研究采用聚类分析技术来评估已识别砂岩单元中的储层岩石类型。采用K均值聚类分析来定义电相,最终根据储层质量从差到优将其分为四种岩石类型。已对四口井进行了基于聚类分析的岩石分类,并进行了对比以描述电相的变化。通过井间对比可以推断,赞扎马 - 02井和05井下部的帕布组储层质量极佳,由岩石类型4定义。另一方面,西南部地区的赞扎马 - 03井储层质量良好至中等,分别以占主导的岩石类型3和2为特征。应用于研究区域的预测技术可为整个储层提供连续的岩石类型识别。使用这种方法来定义岩石类型具有成本效益,在划定感兴趣区域时所需时间较少,并且比人工分析非均质且厚度较大的帕布组更准确。所研究的方法不仅对非均质帕布组的开发有用,而且还可应用于其他地方的非均质砂岩储层。