Production data-based facies analysis for well placement in thinlayered reservoir

作     者：Xuequn Tan Shiwang Chen Taiyuan Hong Yuliang Feng Qiang Ding 

作者机构：SINOPEC Petroleum Exploration and Production Research InstituteBeijing100026China SINOPEC International Petroleum Exploration and Development CorporationBeijing100029China Goldensun Petroleum Technologies IncBeijing100107China 

出 版 物：《Energy Geoscience》 (能源地球科学(英文))

年 卷 期：2022年第3卷第3期

页      面：219-234页

核心收录：

学科分类：0820[工学-石油与天然气工程] 081803[工学-地质工程] 08[工学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 0903[农学-农业资源与环境] 

基　　金：the China National Science and Technology Major Project(No.2016ZX05002-006) 

主　　题：Tide-dominated estuary Well-log facies Sand-body connectivity Well performance Oriente Basin 

摘      要：The exploitation of a thin-layered reservoir with thickness less than 5 m(16 ft)needs to rely upon sedimentary facies analysis,due to the low resolution of seismic data and sparse well ***,the ambiguity of facies interpretation creates uncertainty in facies and reservoir *** study aims to better define facies distribution,predict sand-body architecture,and consequently reduce risks in planning development wells,by integrating production data(dynamic)with conceptual geological models(static).An example is shown from the M1 Member of the Napo Formation in the B and G Oilfields(Block J),Oriente Basin,*** M1 reservoir is a thin-layered sandstone reservoir below seismic *** and well-log facies interpretation indicates depositional facies of a tidedominated *** data during the water injection phase of field development can delineate the detailed geometry of the sand *** injector-producer rate responses can determine the connectivity of sand *** performance during primary recovery can capture the scale of single reservoirs because the initial production rates and the cumulative oil production of individual wells are generally proportional to the size of sand bodies.A facies model was thereby *** step-out wells were planned using this model and drilled *** results demonstrate that the integration of dynamic and static data is critical to understanding reservoir facies distribution.