Multiphase Flow Meters Trial Testing in High GOR/GVF Environment

Nasri, Ahcene; Al-Anizi, Abdulaziz; Al-Amri, Meshal A.; Al-Khelaiwi, Faisal T.; Al-Anazi, Ammal Fannoush

doi:10.2523/iptc-17422-ms

Cited by 1 publication

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“…However, the challenge associated with MPFMs is the accuracy of the reading for fluids with a high gas–oil ratio (GOR) or high gas volume fraction (GVF). The MPFM accuracy decreases as the GVF increases more than 85% . In addition, Ganat et al compared MPFM measurements vs testing separators, and they found that MPFMs were unreliable in a high water-cut environment with an error that can reach 50% at a water cut of 61% .…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence to Predict Oil Rate for High Gas–Oil Ratio and Water-Cut Wells

et al. 2021

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Measuring oil production rates of individual wells is important to evaluate a well’s performance. Multiphase flow meters (MPFMs) and test separators have been used to estimate well production rates. Due to economic and technical issues with MPFMs, especially for high gas–oil ratio (GOR) reservoirs, the use of a choke formula for estimating well production rate is still popular. The objective of this study is to implement different artificial intelligence (AI) techniques to predict the oil rate through wellhead chokes. Support-vector machine (SVM) and random forests (RF) were used to generate different models to predict the production rates for high GOR and WC wells. A set of data (548 wells) was obtained from oil fields in the Middle East. GOR varied from 1000 to 9351 scf/stb, and WC ranged from 1 to 60%. Around 300 wells were flowing under critical flow conditions, while the rest were subcritical. Hence, two cases were studied using each AI model. Seventy percent of the data was used to train both RF and SVM models, while 30% of the data was used to test and validate these models. The developed RF and SVM models were then compared against the previous empirical formulas. The RF model in both critical and subcritical flow conditions was able to perfectly match the actual oil rates. SVM was able to predict the general trend for the oil rates but missed some of the sharp changes in the oil rate trend. The average absolute percent error (AAPE) values in the subcritical flow for SVM and RF were 1.7 and 0.7%, respectively, while in the critical flow, the AAPE values were 1.4 and 0.75% for SVM and RF models, respectively. SVM and RF models outperform the published formulas by 34%. The results from this study will help to estimate the real-time oil and gas rates based on the available data from wellhead chokes without the need for field intervention.

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Section: Introductionmentioning

confidence: 99%