利用机器学习技术和启发式优化算法从测井曲线预测密度曲线:比较研究

Q1 Earth and Planetary Sciences Petroleum Research Pub Date : 2024-06-01 DOI:10.1016/j.ptlrs.2024.01.008
Mehdi Rahmati , Ghasem Zargar , Abbas Ayatizadeh Tanha
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引用次数: 0

摘要

在石油工业中,岩石物理参数分析对于高效的储层管理、生产优化、开发战略和准确的碳氢化合物储量估算至关重要。近年来,机器学习方法的集成给这一领域带来了革命性的变化,即使在数据有限或不完善的情况下,也能解决地质学、地球物理学和石油工程中的难题。本研究的重点是密度测井预测,这是评估储层碳氢化合物体积的关键因素。值得注意的是,在测井过程中,特定深度的测井数据可能会缺失或不正确,这给我们带来了巨大的挑战。为解决这一问题,我们采用了自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN),并结合先进的优化算法,包括粒子群优化算法(PSO)、帝国主义竞争算法(ICA)和遗传算法(GA)。这些方法在根据伽马射线、中子、声波和光电测井数据预测密度测井方面表现出良好的性能。值得注意的是,我们的研究结果表明,基于遗传算法的人工神经网络(GA-ANN)方法优于所有其他方法,其平均平方误差(MSE)仅为 0.0013,令人印象深刻。相比之下,ANFIS 的 MSE 为 0.0015,ICA-ANN 为 0.0090,PSO-ANN 为 0.0093,ANN 为 0.0183。
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Predicting density log from well log using machine learning techniques and heuristic optimization algorithm: A comparative study

In the petroleum industry, the analysis of petrophysical parameters is critical for efficient reservoir management, production optimization, development strategies, and accurate hydrocarbon reserve estimations. Over recent years, the integration of machine learning methodologies has revolutionized the field, addressing challenges in geology, geophysics, and petroleum engineering, even when confronted with limited or imperfect data. This study focuses on the prediction of density logs, a pivotal factor in evaluating reservoir hydrocarbon volumes. It is important to note that during well logging operations, log data for specific depths of interest may be missing or incorrect, presenting a significant challenge. To tackle this issue, we employed the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) in combination with advanced optimization algorithms, including Particle Swarm Optimization (PSO), Imperialist Competitive Algorithms (ICA), and Genetic Algorithms (GA). These methods exhibit promising performance in predicting density logs from gamma-ray, neutron, sonic, and photoelectric log data. Remarkably, our results highlight that the Genetic Algorithms-based Artificial Neural Network (GA-ANN) approach outperforms all other methods, achieving an impressive Mean Squared Error (MSE) of 0.0013. In comparison, ANFIS records an MSE of 0.0015, ICA-ANN 0.0090, PSO-ANN 0.0093, and ANN 0.0183.

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来源期刊
Petroleum Research
Petroleum Research Earth and Planetary Sciences-Geology
CiteScore
7.10
自引率
0.00%
发文量
90
审稿时长
35 weeks
期刊最新文献
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