BBO DTM-Pade半分析技术估算钻屑滑移速度的可行性研究

Abdualhakim Dbair, Mazen Elfergani
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摘要

本文综述了DTM-Pade的Basset-Boussinesq-Oseen方程(BBO)近似方法在预测粒子滑移速度中的应用。研究中使用了一个假设的例子,假设3、5和7mm三种不同直径的球形颗粒代表了广泛的钻屑尺寸范围。此外,假定钻井液在静止模式下为不可压缩流体。根据文献,在计算2秒时间跨度内的滑移速度时,使用了Pade近似(K=20,[8 8])。结果表明,该技术是利用Matlab在短时间内(0.1 ~ 0.2秒)预测高Pad阶粒子滑移速度的实用方法。然而,BBO方程的适用范围如单个颗粒落在无限不可压缩流体中、颗粒尺寸范围小(小于3mm)、颗粒与流体密度比低(大于0.38)等限制了其在油井钻井作业中的应用。此外,在模拟钻井作业时,需要考虑更多因素,包括非牛顿流体、壁面效应和大范围的不规则形状钻屑密度和尺寸,这促使我们使用广义BBO方程。简而言之,在钻井作业中,钻屑通常会混合在井筒环空中,为了实现更可靠的预测,机器学习(MA)和人工智能(AI)等更复杂技术的应用变得必不可少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feasibility Study of Drill Cuttings Slip Velocity Estimation using BBO DTM-Pade’ Semi-analytical Technique
The study reviewed the use of DTM-Pade’ approximation method of Basset–Boussinesq–Oseen equation (BBO) in predicting particle slip velocity. A hypothetical example is used in the study with three different diameters, 3,5 and 7mm, of spherical particle assumed to represent wide rang of drill cuttings’ sizes. In addition, the drilling fluid is assumed to be incompressible fluid in stationary mode. The Pade’ approximation (K=20, [8 8]) is used, based on literature, in calculating slip velocities through 2 seconds time span. It is concluded that this technique is a practical mean of particle slip velocity prediction at high Pad’ orders using Matlab for a short period of time, around (o.1 to 0.2 seconds). However, BBO equation application range such as, single particle fall in infinite incompressible fluid, small particle size rang (less than 3mm), low particle to fluid density ratio (higher than 0.38), limits its application in oil well drilling operations. Moreover, the necessity of more factors considerations in simulating drilling operations including Non-Newtonian fluids, wall effect, and wide range of irregular shaped drill cuttings densities and sizes urges for the use of generalized BBO equation. In drilling operations, concisely, drill cuttings are usually mixed in the wellbore annulus and for more reliable predictions the application of more sophisticated techniques such as Machine Learning (MA) and Artificial Intelligence (AI) become indispensable.
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