Ibrahim Ahmed Farid, Al-Dhaif Redha, Elkatatny Salaheldin, Shehri Dhafer Al
College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Saudi Arabia.
ACS Omega. 2021 Jul 20;6(30):19484-19493. doi: 10.1021/acsomega.1c01676. eCollection 2021 Aug 3.
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.
测量单井的产油率对于评估油井性能至关重要。多相流量计（MPFM）和测试分离器已被用于估算油井产率。由于MPFM存在经济和技术问题，特别是对于高气油比（GOR）油藏，使用节流公式估算油井产率仍然很普遍。本研究的目的是应用不同的人工智能（AI）技术通过井口节流器预测产油率。使用支持向量机（SVM）和随机森林（RF）生成不同的模型，以预测高气油比和含水率油井的产率。从中东油田获取了一组数据（548口井）。气油比从1000到9351标准立方英尺/桶不等，含水率在1%到60%之间。约300口井在临界流条件下流动，其余为亚临界流。因此，对每个AI模型研究了两种情况。70%的数据用于训练RF和SVM模型，而30%的数据用于测试和验证这些模型。然后将开发的RF和SVM模型与先前的经验公式进行比较。RF模型在临界流和亚临界流条件下都能够完美匹配实际产油率。SVM能够预测产油率的总体趋势，但错过了产油率趋势中的一些急剧变化。SVM和RF在亚临界流中的平均绝对百分比误差（AAPE）值分别为1.7%和0.7%，而在临界流中，SVM和RF模型的AAPE值分别为1.4%和0.75%。SVM和RF模型比已发表的公式性能高出34%。本研究结果将有助于根据井口节流器的可用数据估算实时油气产量，而无需进行现场干预。
