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Spectral Noise Logging SNL as a Key Tool to Identify Water Source in a Deviated Production Well on ESP

测井 噪音(视频) 登录中 修井 石油工程 环境科学 地质学 计算机科学 生态学 生物 图像(数学) 人工智能
作者
Al-Rujaibah Ali Ahmed,Ramkamal Bhagavatula,Sergey Prosvirkin
标识
DOI:10.2118/187561-ms
摘要

Abstract Well RAx1 is a 55-deg deviated oil well completed as ESP producer in the sandstone formation. Current production was 1,300 BLPD with 95% water cut. Open-hole logs indicated a clear oil zone whereas well performance was contrary to the log response. The challenge was to identify the water source and subsequently plan for remedial well workover. As part of the investigative process, it was necessary to flow the well and make logging runs to measure both temperature and pressure, as well as zonal production criteria to gain a full understanding of the production issues. Conventional production logging tools cannot determine flow behind pipe. There was a need for specialised and sophisticated technology to identify flow behind pipe and to accurately determine the true source of water in this well. A combination of spectral noise logging (SNL) and high precision thermometer (HPT) tool was deployed as a solution to this challenge. After logging data analysis, clear evidence of communication was seen with water zones above and below the pay zones. This communication was observed only in flowing conditions with noise range in the spectrum of 0.1 – 30 kHz. The shut-in survey did not show any noise response from above or below the pay zones. Shut-in SNL captured noises caused by the fluid moving within the pay zone with a frequency 6.5-16 kHz. Static and flowing pressures of 3710.8 psi and 2,880.6 psi were recorded at the top of pay zone. The gas-oil contact depth was determined at 1,900 ft from static temperature gradient. The production profile was analysed in detail by modelling the temperature profile. This method is based on construction of the actual temperature profile for the entire well to detect convective temperature perturbations due to wellbore heat transfer by flow. From the temperature modelling results, 41% of the fluid contribution was from the pay zone, 5% from above and the rest from below. The application of SNL tool was successful in providing qualitative assessment of flow behind pipe. Its use could be extended to identifying fluid encroachment from below oil-water contact or lateral breakthroughs in nearby wells. It aids in better understanding of fluid movement near the wellbore enabling better well diagnosis, including leak detection behind multiple barriers and checking for swell packers integrity in ICD completions etc., which cannot be decisively determined by conventional production logging tools.
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