通过基于钻井参数的强大的机器学习算法来确定透率
Seyed Vahid Alavi Nezhad Khalil Abad1, Omid Hazbeh2, Meysam Rajabi3
1Department of Civil Engineering, Birjand University of Technology, Birjand 97198 66981, Iran.
ACS omega
|December 18, 2023
概括
本研究使用混合算法预测碳化合物钻探透率 (ROP). LSSVM-PSO方法获得了97%的准确性,在改善地下资源开采方面超过了LSSVM-GA.
科学领域:
- 石油工程是石油工程中的一个.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 碳化合物是全球重要的经济资产.
- 钻探对于提取地下能源资源至关重要.
- 透率 (ROP) 是一个关键的钻井参数.
研究的目的:
- 用伊朗井的钻井数据来预测ROP.
- 评估混合最小平方支持向量机 (LSSVM) 算法的性能.
- 评估基因算法 (GA) 和粒子群优化 (PSO) 在增强噪音钻探数据的LSSVM方面的有效性.
主要方法:
- 利用了来自伊朗三个井的钻井数据.
- 应用混合LSSVM-GA和LSSVM-PSO算法用于ROP预测.
- 杆GA和PSO用于降低噪音和提高LSSVM的准确性.
主要成果:
- LSSVM-PSO表现出卓越的性能,准确度约为97%.
- 在测试数据上,LSSVM-PSO显示了改进的准确性 (RMSE = 1.92,R2 = 0.9516).
- 在50次代后,LSSVM-PSO的准确性得到了改善,而在10次代后,LSSVM-GA稳定了下来.
结论:
- 混合LSSVM-PSO是一种精确有效的ROP预测方法.
- 拟议的算法成功地减少了钻探数据中的噪声.
- 优化的算法提高了地下钻探参数预测的准确性.
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