通过构造建模和岩石物理分析探索数字化测井和地震的意义:以几内亚湾里约热内卢Del Rey盆地新近系-古近系储层为例喀麦隆

Mbouemboue Nsangou Moussa Ahmed , Olugbengha Ajayi Ehinola , Wakwenmendam Nguet Pauline , Marie Joseph Ntamak Nida , Anatole Eugene Djieto Lordon
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引用次数: 0

摘要

地球物理学进入油气勘探领域的历史可以追溯到很早以前,测井和地震资料就证明了这一点。本研究试图利用喀麦隆里约热内卢Del Rey盆地的模拟测井和地震数据来描述储层特征。使用Neuralog软件2018包将测井和地震图从模拟格式转换为数字格式。测井L1、L2、L4和L5测井曲线,以及LongiviNeuralog和Neuramap分别生成的5张数字地震图,可用于本次研究。其中一个非常重要的储层绘制了5口井的远景图。在Interactive岩石物理软件中随机生成图;孔隙度图、页岩体积和油气储层图。在不同厚度的井况下随机圈定储层。储层岩性图显示,储层由砂岩、灰岩和白云岩组成,为含4 ~ 6个砂单元(S1.0、S1A2、S1A3、S1A4、SB)的压陷巨型层序。从高断裂带(7条盘状断裂)推断出复杂的近海砂环境中存在油气。从构造、岩石物理和地层各向异性观察中发现储层分区;L1、L2、L4、L5井孔隙度分别为0.37 ~ 0.45、0.08 ~ 029、0.1624 ~ 0.30、0.10 ~ 0.35,分别为4.099 ~ 133.4 mD、2068.9 ~ 10000 mD以上、1.4228 ~ 227.3726 mD、12.5237 ~ 454.8518 mD。因此,对于研究区西部和东部边缘的井,不鼓励在研究区中心钻井。该研究扩展了对新近系-古近系储层特征的理解,证明了使用Neuralog, Petrel 2017进行的有效数字化,以及使用Interactive岩石物理,Techlog和Petrel进行的有效储层研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Significance of Digitalized Logs and Seismics through Structural Modelling and Petrophysical Analyses: Case study: Neogene-Paleogene reservoirs of the Rio Del Rey Basin, Gulf of Guinea. Cameroon
The advent of geophysics into the world of hydrocarbon exploration has been proven to date very far back in time demonstrated by information from well logs and seismics. This study tries to characterize the reservoir using well logs and seismics originally digitized from analogue from the Rio Del Rey basin of Cameroon. Well logs and seismic maps were transformed from analogue to digital format using the Neuralog software 2018 package. Five well logs: Log L1, L2, L4, and L5, and five digital seismic maps were available for this study generated from LongiviNeuralog and Neuramap respectively. One very important reservoir was mapped for the five well scenarios. Plots were produced randomly in Interactive petrophysics software; Porosity plots, shale volume, and petroleum play maps. Reservoirs were delineated randomly in all the well scenarios with different thicknesses. Lithological plots of these formations indicated that reservoirs consist of sand, limestone, and dolomite and a Ypression mega sequence of deposition containing 4 – 6 sand units (S1.0, S1A2, S1A3, S1A4, SB). The presence of hydrocarbon in a complex paralic sand environment was inferred from the highly faulted area (7 listric faults). Finding saw reservoir compartmentalization from the structural, petrophysical, and stratigraphic anisotropy observed; 0.37–0.45 well L2, 0.08–029 well L4, 0.1624–0.30 well L1, and 0.10–0.35 well L5 for porosity, 4.099–133.4 mD, 2068.9 – above 10000 mD, 1.4228–227.3726 mD, 12.5237–454.8518 mD, For L1, L2, L4, L5 respectively. So, drilling for wells at the center of the study area is discouraged for those at the western and eastern edge of the study area. This study extends the understanding of the reservoir characterization of the Neogene-Paleogene formation proving efficient digitization using Neuralog, Petrel 2017, and an efficient reservoir study using Interactive petrophysics, Techlog, and Petrel.
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