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首页> 外文期刊>International Journal of Innovative Computing Information and Control >INTELLIGENT DRILLING RATE PREDICTOR
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INTELLIGENT DRILLING RATE PREDICTOR

机译：智能钻孔速率预测器

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摘要

Drilling rate prediction is crucial for improving the performance of drilling. However, large number of unforeseen factors and events influence the drilling rate and make it a complex and stochastic process. Consequently, prediction of drilling rate has remained challenging during last decades. Many different techniques have been introduced for this mission. Among those, Bourgoyne and Young model (BYM) has been widely used during last decades. BYM has been made up of eight functions. Each function represents the effect of some drilling parameters. Although the relationship between drilling rate and mentioned eight functions is nonlinear and very complex, Bourgoyne and Young simply multiplied all eight functions with each other to attain the drilling rate. In this research, after determining constant coefficients of Bourgoyne and Young model using Genetic Al gorithm, a General Regression Neural Networks (GRNN) is employed hierarchically in order to uncover the complex relation saof drilling rate and mentioned eight functions of BYM. The data sets used in this study are nine wells of an Iranian gas field called "Khangiran". Simulation results show that the proposed approach is more accurate than a GABYM in drilling rate prediction.
机译：钻速预测对于提高钻探性能至关重要。但是，许多不可预见的因素和事件会影响钻速，并使其成为一个复杂而随机的过程。因此，在过去的几十年中，对钻速的预测一直充满挑战。为此任务引入了许多不同的技术。其中，布尔戈因和杨（Bourgoyne and Young）模型（BYM）在过去的几十年中被广泛使用。 BYM由八个功能组成。每个函数代表一些钻孔参数的影响。尽管钻速与上述8个函数之间的关系是非线性且非常复杂的，但Bourgoyne和Young仅将所有8个函数彼此相乘即可获得钻速。在这项研究中，在使用遗传算法确定Bourgoyne和Young模型的常数系数之后，分层使用通用回归神经网络（GRNN）来揭示钻井速率的复杂关系，并提到了BYM的八个功能。本研究中使用的数据集是伊朗一个名为“ Khangiran”的气田的9口井。仿真结果表明，该方法在钻速预测中比GABYM更为准确。

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