水平井两相流产出剖面光纤监测反演方法

doi:10.13809/j.cnki.cn32-1825/te.2025051

油气藏评价与开发

水平井两相流产出剖面光纤监测反演方法

熊瀚澜¹, 罗红文¹, 李海涛¹, 艾文斌², 黄雅妮¹, 马佳林¹, 陈柄杞¹, 冉飞飞¹, 潘晓艺¹

1.西南石油大学油气藏地质及开发工程全国重点实验室,四川成都 610500;
2.中国石油长庆油田分公司,陕西西安 710021

收稿日期:2025-01-07
通讯作者: 罗红文（1990—）,男,博士,讲师,硕士研究生导师,主要从事油气井分布式光纤监测（DTS/DAS/DSS）解释、注采动态评价及完井优化方面的研究工作。地址：四川省成都市新都区新都大道8号,邮政编码：610500。E-mail： rojielhw@163.com
作者简介:熊瀚澜（2001—）,男,在读硕士研究生,主要从事井下分布式光纤监测解释及物理模拟实验方向的研究工作。地址：四川省成都市新都区新都大道8号,邮政编码：610500。E-mail：18883078943@163.com
基金资助:
中国石油科技创新基金项目“水平井压裂声波响应机理实验及DAS大数据智能反演方法研究”（2022DQ02-0305）

Inversion method for fiber optic monitoring of two-phase flow production profiles in horizontal wells

XIONG HANLAN¹, LUO HONGWEN¹, LI HAITAO¹, AI WENBIN², HUANG YANI¹, MA JIALIN¹, CHEN BINGQI¹, RAN FEIFEI¹, PAN XIAOYI¹

1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation , Southwest Petroleum University, Chengdu, Sichuan 610500, China;
2. PetroChina Changqing Oilfield Company, Shaanxi, Xi’an 710021, China

Received:2025-01-07

摘要/Abstract

摘要： 分布式光纤传感（Distributed Temperature Sensing,DTS）技术在油气井生产动态的智能化监测中应用广泛。针对水平井油水两相流产出剖面定量解析难题,本文构建了综合考虑焦耳-汤姆逊效应（Joule-Thomson effect）等多种微热效应、适用于水平井油水两相流的温度剖面预测模型,并对1口油藏中的水平井的温度剖面进行了模拟与敏感性分析。同时,本文采用粒子群优化（Particle Swarm Optimization,PSO）算法建立了DTS数据反演模型,创新性地实现了基于单一DTS数据源的井下多维未知参数反演,进而实现水平井两相流产出剖面的定量解释。研究表明：①油水两相流水平井温度剖面主要影响因素及其影响程度排序为：单井产量（Q）>渗透率（k）>含水率（F_w）>井筒半径（R_w）>原油密度（ρ_o） >伤害带半径（R_d）>储层导热系数（K_t）;②单井产量、渗透率和含水率是影响温度剖面的关键主导因素,在对实测DTS数据进行反演时,可优先将地层渗透率作为核心目标参数进行反演,次要因素可设定为固定值或合理范围以简化问题;③利用PSO反演模型对现场井DTS温度数据进行反演,能够精准识别两相流体的产出位置,反演得到的产液剖面解释结果与现场PLT（生产测井）测试结果高度吻合,各产出段平均产液量的平均绝对误差低于10%,充分验证了PSO反演模型的可靠性。未来可从提升模型对复杂流动的刻画能力以及对多相流拓展等方向来进行发展。

关键词: 产出剖面, 反演方法, 分布式光纤传感, 粒子群优化算法, 两相流水平井, 敏感性分析

Abstract: Distributed temperature sensing (DTS) technology is widely applied in intelligent monitoring of production dynamics in oil and gas wells. To address the challenges in quantitatively analyzing oil-water two-phase flow profile in horizontal wells, a temperature profile prediction model applicable to oil-water two-phase flow in horizontal wells was constructed, comprehensively considering multiple micro-thermal effects, including the Joule-Thomson effect. Simulation and sensitivity analyses of the temperature profile of a reservoir horizontal well were conducted. Meanwhile, the particle swarm optimization (PSO) algorithm was used to establish a DTS data inversion model, innovatively enabling the inversion of multi-dimensional unknown downhole parameters based on a single DTS data source, thereby achieving a quantitative interpretation of the oil-water two-phase flow production profile in horizontal wells. The results showed that: (1) The main influencing factors of the temperature profile in oil-water two-phase horizontal wells, ranked by impact degree from high to low, were single-well production (Q)> permeability (k)> water cut (F_W)> wellbore radius (R_w)> crude oil density (ρ_o)> damage zone radius (R_d)> reservoir thermal conductivity (K_t). (2) Single-well production, permeability, and water cut were the key dominant factors affecting the temperature profile. When inverting measured DTS data, formation permeability could be prioritized as the core target parameter for inversion, and secondary factors could be set as fixed values or assigned reasonable ranges to simplify the problem. (3) By using the PSO inversion model to invert the DTS temperature data of the field well, the production positions of the two-phase fluids could be accurately identified. The interpreted liquid production profile obtained from inversion showed high consistency with field production logging tool (PLT) test results, with a mean absolute error of the average liquid production per section of less than 10%, fully verifying the reliability of the PSO inversion model. Future research can focus on enhancing the model's ability to characterize complex flows and expanding its application to multiphase flow scenarios.

Key words: production profile, inversion method, distributed temperature sensing, particle swarm optimization algorithm, two-phase flow in horizontal well, sensitivity analysis

中图分类号:

TE33.2

熊瀚澜,罗红文,李海涛, 等. 水平井两相流产出剖面光纤监测反演方法[J]. 油气藏评价与开发, 0, (): 2025051-2025051.

XIONG HANLAN,LUO HONGWEN,LI HAITAO, et al. Inversion method for fiber optic monitoring of two-phase flow production profiles in horizontal wells[J]. Petroleum Reservoir Evaluation and Development, 0, (): 2025051-2025051.

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