摘要
地质导向系统基于井下实时测量的地质、地球物理和钻井参数,优化三维井轨迹到油气藏指定位置以获得最大的泄油面积和最佳的采收率,成为提高单井油气产量和油田开发效益的前沿技术,面临低孔-低渗-强非均质储层等地质难题和高温-高压-强振动等钻井工程难题.本文在梳理地质导向系统硬件、软件和人员等组成的基础上,将地质导向系统划分为基于地层构造的轨迹地质导向、识别储层岩性的油藏地质导向和面向地层成分的产能地质导向三个阶段;以分辨率更高、更直观的露头为例,阐述了地质导向系统“构造-岩性-成分”三个层次的逻辑关系.本文提出“数据采集-信息融合-态势感知-地层智能评价”的地质导向智能评价方案:按照学科门类,考虑数据的来源、采集时间和空间分辨率等因素,梳理出地层智能评价所需的数据类型与数据特征;提出地质导向多源异构数据“数据级-特征级-决策级”信息融合分类,并梳理了相关算法;根据数据模型与先验经验对当前钻井状态、地质环境进行态势评估,采用机器学习等算法对钻井模型和地质模型进行态势预测;提出“初导”和“精导”的理念,按照“构造-岩性-成分”三个层次厘定了地层智能评价的“精导”技术要点.该地质导向智能评价方案应用到实际油田的地质导向作业,验证了技术方案的可靠性和实用性,可为未来的井下智能闭环研究提供借鉴.
Geosteering system is the optimal placement of a three-dimensional well trajectory based on the results of real-time downhole geological,geophysical and drilling parameters,aiming to obtain the largest oil drainage area and the best recovery factor.It has become the cutting-edge technology to improve the oil and gas production of a single well and the benefits of oilfield development,confronting geological problems such as low porosity,low permeability,and strong heterogeneous reservoir,combined with drilling engineering problems such as high temperature,high pressure,and strong vibration.Based on sorting out the hardware,software,and team of the geosteering system,this paper divided the geosteering system into three stages:trajectory geosteering based on formation structure,reservoir geosteering based on reservoir lithology,and productivity geosteering oriented to formation composition.Taking the outcrop with higher resolution and more intuitive as an example,the logical relationship in three levels of"structure-lithology-composition"for the geosteering system was expounded.In this paper,a formation intelligent evaluation solution for geosteering with the scheme of"data acquisition,information fusion,situational awareness,and formation intelligent evaluation"was proposed:According to the discipline categories,combined with the data sources,acquisition time and spatial resolution,the data types and characteristics required for formation intelligent evaluation were sorted;The information fusion classification of multi-source heterogeneous data of geosteering was proposed into"Data level-Feature level-Decision level",and the relevant algorithms were sorted out;According to the data model and prior experience,the situation assessment for the current drilling state and the geological environment was carried out,and the situation forecast for the next drilling state and the geological environment was carried out by machine learning algorithms;According to the"structure-lithology-composition"classification,the concepts of"Preliminary Geosteering"and"Fine Geosteering"was put forward,and the technical points of"Fine Geosteering"were also determined.An application of the geosteering intelligent evaluation solution to the actual oilfield geosteering operation was carried out,verifying its reliability and practicability,which can provide a reference for future downhole intelligent closed-loop research.
作者
田飞
底青云
郑文浩
葛新民
张文秀
张江云
杨长春
TIAN Fei;DI QingYun;ZHENG WenHao;GE XinMin;ZHANG WenXiu;ZHANG JiangYun;YANG ChangChun(CAS Engineering Laboratory for Deep Resources Equipment and Technology,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China;Innovation Academy for Earth Science,Chinese Academy of Sciences,Beijing 100029,China;College of Earth and Planetary Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;School of Geosciences,China University of Petroleum(East China),Qingdao Shandong 266580,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2023年第9期3975-3989,共15页
Chinese Journal of Geophysics
基金
中国科学院A类战略性先导科技专项(XDA14050101)
中国科学院青年创新促进会(2021063)
国家重点研发计划课题(2019YFA0708300)联合资助。
关键词
地质导向系统
构造-岩性-成分
数据融合
地层智能评价
解决方案
Geosteering system
Structure-Lithology-Composition
Data fusion
Formation intelligent evaluation
Solution
