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Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm

机译：基于模糊理论和PSO-SVR算法的钻井泄漏风险动态评估

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摘要

In recent years, artificial intelligence has gradually penetrated into various fields, and has become a research hotspot. The modern industrial upgrades and transformation of the petroleum industry, makes it closer to the direction of intelligence. For the research of drilling risk evaluation, choosing the right evaluation model to achieve real-time risk dynamic evaluation which is important for risk judgment and response time. However, drilling system never considered as a complex system in the research of drilling risk assessment. When the sensor of the well site collects the relevant parameters, the remote monitoring system carries on the real-time data analysis, because of the instrument or transmission process, the drilling parameters appear fuzziness and randomness. To realize real time dynamic evaluation of drilling risk this paper proposed a fuzzy multilevel algorithm based on Particle swarm optimization (PSO) to optimize Support vector regression machine(SVR), and takes drilling leakage risk as an example. And two main objectives has been achieved. The first is to establish a fuzzy multi-level drilling leak risk evaluation system. The second is to use the PSO-SVR algorithm to study the risk evaluation results and realize the real-time dynamic risk evaluation. This paper first summarizes the characterization phenomena and laws of the occurrence of acquisition and loss parameters, and uses this as an indicator to establish a multilevel index system for risk assessment. Second, combined with fuzzy theory, a risk assessment model is established. And in final, the parameters C and g of the SVR model are optimized by using the SVR algorithm improved by PSO, which solves the problem that the parameters such as penalty factor c. kernel function k and sensitivity coefficient e are difficult to select in the traditional SVR model, improves the accuracy of the model, and realizes more accurate real-time dynamic evaluation of risk. The algorithm proposed in this paper achieves two goals. Taking the XX oilfield as an engineering example, the results show that the accuracy of the PSO-SVR model can reach 99.99%, with high convergence degree, which is obviously higher than that of the multilayer perceptron neural network model. (C) 2019 Elsevier B.V. All rights reserved.
机译：近年来，人工智能已逐渐渗透到各个领域，并已成为研究热点。石油工业的现代产业升级和转型，使其更加接近智能化方向。对于钻探风险评估的研究，选择合适的评估模型来实现实时风险动态评估，这对于风险判断和响应时间至关重要。然而，在钻井风险评估研究中，钻井系统从未被视为复杂的系统。当井场传感器收集相关参数时，远程监控系统进行实时数据分析，由于仪器或传输过程的原因，钻井参数会出现模糊性和随机性。为了实现钻井风险的实时动态评估，提出了一种基于粒子群算法（PSO）的模糊多级算法，以优化支持向量回归机（SVR），并以钻井漏水风险为例。并且已经实现了两个主要目标。一是建立模糊多层次钻井泄漏风险评估系统。二是利用PSO-SVR算法研究风险评估结果，实现实时动态风险评估。本文首先总结了获取和损失参数出现的特征现象和规律，并以此为指标建立了风险评估的多级指标体系。其次，结合模糊理论，建立了风险评估模型。最后，采用PSO改进的SVR算法对SVR模型的参数C和g进行优化，解决了惩罚因子c等参数的问题。传统的SVR模型难以选择核函数k和灵敏度系数e，提高了模型的准确性，实现了更加准确的实时风险动态评估。本文提出的算法实现了两个目标。以XX油田为例，结果表明，PSO-SVR模型的精度达到99.99％，收敛度高，明显高于多层感知器神经网络模型。 （C）2019 Elsevier B.V.保留所有权利。

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