Real-Time Down-Hole Monitoring of Gas Injection Profile Using Fibre-Optic Distribute Temperature and Acoustic Sensing in Tarim

Chen, Defei; Huang, Kun; Meng, Xiangjuan; Ju, Liu; Bao, Zhang; Sun, Tao; Shen, Jianxin; Wei, Jie; Wu, Hongjun; Teng, Quincy

doi:10.2523/iptc-21177-ms

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…At the same time, its cost is also lower than that of traditional logging methods. These provides a new idea for analyzing the downhole water absorption profile [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with previous studies, the method of identifying and classifying suction profiles by distributed fiber-optic vibration signals can reflect the dynamic changes of suction profiles with high accuracy. It is no longer dependent on a large amount of data such as suction profile information, reservoir geological information, and production information of injection and extraction wells, and is more inclined to research on injection wells with little suction profile information [2][3][4][5][6][7]. In this paper, the distributed fiber optic data are firstly preprocessed to remove noise by variational mode decomposition (VMD) method, and then the preprocessed vibration signals are transformed into spectrograms.…”

Section: Introductionmentioning

confidence: 99%

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Identification and classification of water absorption profile of distributed optical fiber vibration signal based on XGBoost algorithm

Wang

et al. 2022

SN Appl. Sci.

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Aiming at the problem that the layered water injection cannot be accurately obtained in the process of oilfield development, this paper proposes an optical fiber vibration signal recognition and classification algorithm based on XGBoost integrated learning. Firstly, the optical fiber vibration signal collected by the optical fiber vibration sensor is denoised by variational mode decomposition and reconstruction. Then generate spectrograms corresponding to different water absorption layers at different times. A dataset containing 3000 spectral images is obtained. The data sets are imported into support vector machine, random forest classifier and XGBoost integrated classifier based on decision tree for recognition and classification. The parameters of the obtained model are optimized and the model is evaluated by cross validation. Finally, the obtained models are compared and tested. The experimental results show that the XGBoost ensemble learning algorithm with decision tree as the base classifier can effectively identify different vibration signals. This method has a certain significance for the identification of layered relative water absorption of water injection profile.

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“…At the same time, its cost is also lower than that of traditional logging methods. These provides a new idea for analyzing the downhole water absorption profile [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification and classification of water absorption profile of distributed optical fiber vibration signal based on XGBoost algorithm

Wang

et al. 2022

SN Appl. Sci.

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Malaysia’s First Real-Time Fiber Optic Logging with High-Fidelity Distributed Acoustic and Temperature Survey Conveyed Via Slickline: A Solution for Uncertain Downhole Integrity Condition

Shahril¹,

Johan²,

Munir³

et al. 2022

SPE Asia Pacific Oil &Amp; Gas Conference and Exhibition

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One of the challenges for brownfield operators managing over 20 year-old wells is the uncertainties of well integrity impacting the effort to access the remaining oil in place. EnQuest known as the operator of choice for maturing and underdeveloped hydrocarbon assets, sees this challenge as an opportunity to grow by exploring the best approach in the market to meet our objective. This paper presents one of EnQuest’s wells that may have a crossflow interzone between water to the oil bearing reservoir. Well ‘Z’ is a single oil producer completed in 1997 but shut in since 2001 due to a high water cut. This well produces from three zones namely A, B and C. Zone A is expected to produce oil but well test results showed a 100% water cut. Offset well suggest the water bearing is contributed by Zone B. The High-fidelity Distributed Acoustic and Temperature Survey (‘DAS’ and ‘DTS’) was evaluated to determine the possibility of crossflow behind the casing. The unique data solution using a combination of DAS and DTS technology based on engineered fibers, allowed for continuous and wide coverage logging of the well. Real-time data acquisition and displays of the entire wellbore led to a better understanding of the well’s dynamic and transient behavior and ultimately to a rapid and complete well integrity assessment. The abnormal fluid movement detection during shut-in was achieved through the highly sensitive sensor array, within the low acoustic frequency range, something conventional logging techniques would have missed. This service was deployed via a normal slickline unit with additional hardware required for real-time monitoring. Twelve hours of data were recorded, under a baseline shut in condition, followed by a flowing condition and then a hard shut-in. Real-time data processing and interpretation were performed onsite during logging operations by a service provider’s experts. An unexpected result was discovered with the water contribution identified as coming from Zone B through a leaking Sliding Sleeve Door (‘SSD’) which was in a closed position, as cyclic liquid movements inside the tubing originating from Zone B and past Zone A were detected and tracked from a low frequency DAS signal. Moreover, clear acoustic activity was measured at two gas lift orifice valves during the shut-in condition; these were likely allowing the passage of the reservoir fluids into the annulus. Finally, during flowing condition, all production clearly showed that crossflow originated from Zone B to A, by both DTS and DAS measurements. This explained the water production observed at the surface. Results obtained were well received and immediate was planned action to isolate the water source resulting in 0% water produced afterwards establish movement via slow strain DAS and noise logging analysis.

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Combining DAS and DTS Measurements for Oil-Water Two-Phase Flow Profiling in Sucker Rod Pump Wells

Song,

Sui,

et al. 2023

Day 1 Mon, October 02, 2023

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Distributed temperature and acoustic sensing techniques (DTS and DAS) have been widely used in oil and gas wells of higher rates for flow profiling. However, current interpretation models usually cannot be directly applied to sucker rod pump (SRP) wells. Due to the existence of the sucker rod, the contact mode between the optical fiber and wellbore fluids is different from the pumpless wells. Furthermore, high water cut and pump noises also greatly increase the difficulty of DAS data analysis. This paper presented an improved theoretical model and interpretation method for oil-water two-phase profiling in SRP wells and a field case was used for illustration. The DTS data analysis was mainly based on a well-established numerical wellbore-reservoir coupled flow/thermal model that considers transient temperature behavior during production tests. Considering the different contact modes between optical fiber and wellbore fluids in SRP wells, a segmented function was adopted to describe the variation of heat transfer flux in different well sections. The DAS data were prefiltered to eliminate the pump noises based on the on-site test results. The possibility of determining the volume proportions of the oil and water phase in the mixed fluid through the utilization of the f-k plot was explored. The strength and energy of DAS data were used for preliminary flow profiling. The DAS flow profiles were used as the initial guess and the final interpretation results were inversed by combining DTS and DAS data. Levenberg-Marquardt algorithm was applied for the inverse procedure. Compared with pumpless wells, the wellbore temperature in the upper section of SRP wells was closer to the formation temperature, which was successfully characterized by this improved thermal model. The water-cut variation has a limited impact on temperature and it might be further obscured by the oscillation of temperature data to some extent. The flow profile calculated from DAS data served as the initial guesses in the DTS inverse modeling part, which greatly reduced the uncertainty of DTS data analysis and computation cost. The presented model and interpretation method have been applied to an SRP well in Liaohe oil field, China. The interpreted flow profiling by combining DTS and DAS data showed consistent results with that measured by production logging tools. Furthermore, the major oil-production zones were identified, and they are interestingly not the major liquid-production zones. An improved model and interpretation method to interpret the oil and water allocation in SRP wells was presented. The combining DAS and DTS measurements resulted in more accurate interpretation of oil and water allocation than using the DAS alone. In addition, this method may provide guidance for water plugging treatment in commingled wells.

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Real-Time Down-Hole Monitoring of Gas Injection Profile Using Fibre-Optic Distribute Temperature and Acoustic Sensing in Tarim

Cited by 4 publications

References 8 publications

Identification and classification of water absorption profile of distributed optical fiber vibration signal based on XGBoost algorithm

Identification and classification of water absorption profile of distributed optical fiber vibration signal based on XGBoost algorithm

Malaysia’s First Real-Time Fiber Optic Logging with High-Fidelity Distributed Acoustic and Temperature Survey Conveyed Via Slickline: A Solution for Uncertain Downhole Integrity Condition

Combining DAS and DTS Measurements for Oil-Water Two-Phase Flow Profiling in Sucker Rod Pump Wells

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