利用多元线性回归预测伊拉克石油项目绩效指标

Q3 Environmental Science Tikrit Journal of Engineering Sciences Pub Date : 2023-06-25 DOI:10.25130/tjes.30.2.10
Nadal Adnan Jasim, A. Ibrahim, W. A. Hatem
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引用次数: 0

摘要

许多石油和天然气项目都面临着严重的成本超支和进度延误,这是石油行业决策者关注的主要问题。本文旨在建立三个数学模型来估计挣值指标,即进度绩效指数(SPI)、成本绩效指数(CPI)和完成成本绩效指标(TCPI),以减少伊拉克石油项目的成本和时间估计误差。研究方法采用人工智能技术,利用多元线性回归技术(MLR)预测挣值(EV)指数,得到标准的局部方程,以衡量伊拉克石油项目的绩效。数据基于2015年6月26日至2022年8月25日从Karbala炼油厂项目收集的83份月度报告,并被选为案例研究。它是石油项目公司(SCOP)之一-伊拉克石油部的大型和现代化项目,它将几个项目合并为一个项目。结果显示,CPI、SPI和TCPI的平均准确率(AA%)分别为95.194%、92.195%和83.706%,相关系数(R)分别为92.4%、98.4%和93.7%。结果表明,理论计算结果与实际计算结果相差不大。因此,本文利用MLR技术推导出预测模型,使其更准确地预测出挣值。
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Forecasting the Performance Measurement for Iraqi Oil Projects using Multiple Linear Regression
Many oil and gas projects have been subjected to significant cost overruns and schedule delays, which is a major concern for the decision-makers in the oil industry. This paper aims to develop three mathematical models to estimate earned value indicators, the Schedule Performance Index (SPI), Cost Performance Index (CPI), and To-Complete Cost Performance Indicator (TCPI), to reduce the cost and time estimation error in Iraqi oil projects. The research methodology adopted artificial intelligence techniques using Multiple Linear Regression technology (MLR) to predict Earned Value (EV) Indexes to get standard local equations to measure the performance of Iraqi oil projects. The data is based on (83) monthly reports from 26 June 2015 to 25 August 2022 collected from the Karbala Refinery Project, selected as a case study. It is one of the Oil Projects Company (SCOP)- the Iraqi Ministry of Oil’s massive and modern projects, and it combines several projects into one project. The results showed numerous significant points, such as the average accuracy (AA%) for the CPI, SPI, and TCPI was 95.194%, 92.195%, and 83.706%, respectively, while the correlation coefficients (R) were 92.4%, 98.4%, and 93.7%. It was shown that there were relatively few differences between the theoretical and actual results. Therefore, the MLR technique was utilized in this paper to derive the prediction models for its more correct earned value predictions.
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CiteScore
1.50
自引率
0.00%
发文量
56
审稿时长
8 weeks
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