Calculation of fluid-producing profiles from mine fiber-optic acoustic data: Model and application

doi:10.3969/j.issn.2096-1693.2025.02.006

Abstract

Abstract:

The real-time and accurate monitoring of the production well fluid profile is crucial for guiding dynamic adjustments in oilfield development. It plays a critical role in evaluating the proportion of fluid produced from different production layers, optimizing parameters for horizontal well fracturing, and adjusting production dynamics. In recent years, fluid profile testing based on distributed acoustic sensing (DAS) technology has emerged as a new method with high accuracy and strong real-time capabilities. This technique is particularly suitable for the high-temperature, high-pressure, and narrow-complex downhole environments commonly found in oil and gas fields. However, most current research on distributed fiber-optic profile monitoring is focused on theoretical studies and laboratory experiments, with limited application to actual production conditions of the mining field operations. In actual production, downhole fiber-optic signals are often subject to interference from complex noise, and their response characteristics can be highly variable. Furthermore, there is a lack of mature analysis processes and models for using DAS technology to analyze downhole fluid events and calculate production profiles. This paper proposes a model and calculation process for fluid profile analysis that can be applied to mining field production scenarios. The method involves deploying distributed fiber-optic acoustic sensing to collect fiber-optic data under various operational conditions. Frequency analysis is then performed to identify effective frequency bands, and the fluid profile is calculated from the perspective of acoustic energy. This approach addresses the challenge of limited analysis methods for calculating production profiles from distributed acoustic fiber-optic data. To validate the proposed model and process, the paper analyzes data from three wells in a mining field. The results indicate that in the 400-800 Hz frequency range, the maximum difference in Fiber-Based Energy (FBE) energy occurs during well switching. This allows the system to filter out most background and environmental noise while retaining important flow-related information. After opening the wells, all three wells showed a delay in energy response. During the production phase, the production layers did not extend to all depths, and a dominant influx region was observed. However, after the second well opening, the overall fluid profile distribution became more uniform. Additionally, the intensity of the FBE energy varied between the first and second well openings, with stronger absolute FBE energy observed during the first production phase. These findings provide valuable insights into optimizing oilfield operations and improving the accuracy of fluid profile monitoring through distributed acoustic sensing technology.

Key words: distributed acoustic fiber optic sensing, heavy oil well, mine data, data analysis

摘要：

生产井产液剖面的实时准确监测对油田开发动态调整具有重要指导意义，有助于评估产层产液占比、优化水平井压裂改造参数、调整生产动态。而基于分布式光纤声波技术的产液剖面测试是近年来兴起的一种新技术，具有准确度高、实时性强的优点，适用于油气田井下高温高压、狭小复杂环境。然而现有分布式光纤产业剖面监测研究多集中于理论研究及室内实验，而在矿场实际生产过程中，光纤井下信号受复杂噪声干扰，响应特征多变，缺乏利用DAS技术分析井下流动事件及产量剖面计算的成熟分析流程与模型。本文提出了一套适用于矿场产液剖面计算的模型与计算流程，在生产井开展分布式光纤声波传感监测采集不同工况光纤数据，对其进行频率分析确定有效频段，并从声波能量的角度计算产液剖面，有效解决了分布式声波光纤数据产量剖面计算的分析处理方法较少的问题。针对3 口矿场实测数据开展分析，结果表明：此次测试在400~800 Hz频率范围内，开关井状态下的FBE能量差值最大，能够在保留较多流动信息的情况下滤除大多数背景或环境噪声。在开井后，3口井均存在一定的能量响应滞后情况，且在生产过程中，产层位置并不是覆盖所有深度，存在优势进液区域，但第二次开井后，整体产液剖面分布更为均匀。先后两次开关井的FBE能量强度不同，第一次生产过程的绝对FBE能量更强。

关键词: 分布式声学光纤传感, 稠油井, 矿场数据, 数据分析

CLC Number:

E345
TP274

HU Xiaodong, JIANG Zongshuai, WANG Xiaowei, ZHOU Fujian, ZHAO Yang, GONG Haonan, WANG Yajing, YU Diming. Calculation of fluid-producing profiles from mine fiber-optic acoustic data: Model and application[J]. Petroleum Science Bulletin, 2025, 10(3): 553-564.

胡晓东, 蒋宗帅, 王小玮, 周福建, 赵杨, 龚浩楠, 王雅晶, 余迪明. 矿场光纤声波数据计算产液剖面：模型与应用[J]. 石油科学通报, 2025, 10(3): 553-564.

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URL: https://sykxtb.cup.edu.cn/EN/10.3969/j.issn.2096-1693.2025.02.006

https://sykxtb.cup.edu.cn/EN/Y2025/V10/I3/553

Figures/Tables 11

Fig. 1 Schematic diagram of continuous tubing truck-mounted fiber optic equipment and well completion structure

Fig. 2 Flowchart of fiber-optic acoustic wave data to compute a model of the fluid-producing profile

Table 1 Basic data and test parameters for three test wells

井序号	人工井底/m	光纤下入深度/m	筛管段/m	产液量/m³
1	931.35	825	725.9~931.8	18.6
2	887.00	844	690.1~884.1	8.5
3	1015.75	923	724.8~1015.8	8.0

Fig. 3 Overall spectral waterfall map of the sensing fiber and spectral map at 600 m (non-production layer) depth

Fig. 4 Fiber-optic spectral waterfall and spectral map at depth of 810 m (production layer) in the production layer section

Fig. 5 Comparison of FBE energy waterfall plots for difference frequency bands

Table 2 The total energy values and differences of FBE energy spectra for different frequency bands

频率成分/Hz	0~100	0~400	400~800	800~1200	1200~1600	1600~2000
关井状态FBE能量总值/dB	879 202.1	1 004 296.1	886 598.5	876 113.5	868 661.1	864 898.6
开井状态FBE能量总值/dB	897 519.2	1 034 345.1	934 100.3	909 771.6	895 150.9	891 864.3
FBE能量差值/dB	18 317.1	30 049	47 501.8	33 658.1	26 489.8	26 965.7
FBE能量变化比例	2.1%	3.0%	5.4%	3.8%	3.0%	3.1%

Fig. 6 FBE waterfall map of the production in the 3 wells

Fig. 7 Production location analysis of 3 wells

Table 3 Statistics of energy changes in 3 wells

井号	第一次开井能量和/dB	第二次开井能量和/dB	能量变化值/dB	变化比值
1#	344 578.7	135 700.0	208 878.7	60.6%
2#	617 996.5	430 200.0	187 796.5	30.4%
3#	669 080.3	493 053.6	176 026.7	26.3%

Fig. 8 Distribution of fluid production locations at different moments of the first and second well openings

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