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首页> 外文期刊>Journal of Energy Resources Technology >A Robust Rate of Penetration Model for Carbonate Formation
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A Robust Rate of Penetration Model for Carbonate Formation

机译：稳健的碳酸盐岩渗透模型

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During the drilling operations, optimizing the rate of penetration (ROP) is very crucial, because it can significantly reduce the overall cost of the drilling process. ROP is defined as the speed at which the drill bit breaks the rock to deepen the hole, and it is measured in units of feet per hour or meters per hour. ROP prediction is very challenging before drilling, because it depends on many parameters that should be optimized. Several models have been developed in the literature to predict ROP. Most of the developed models used drilling parameters such as weight on bit (WOB), pumping rate (Q), and string revolutions per minute (RPM). Few researchers considered the effect of mud properties on ROP by including a small number of actual field measurements. This paper introduces a new robust model to predict the ROP using both drilling parameters (WOB, Q, ROP, torque (T), standpipe pressure (SPP), uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. In addition, the relative importance of drilling fluid properties, rock strength, and drilling parameters to ROP is determined. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower (HP), where the coefficient of determination (T-2) was 0.71, 0.87, 0.70, and 0.92 for WOB, RPM, T, and HP, respectively. ROP also showed a strong function of mud fluid properties, where R-2 was 0.70 and 0.70 for plastic viscosity (PV) and mud density, respectively. No clear relationship was observed between ROP and yield point (YP) for more than 500 field data points. The new model predicts the ROP with average absolute percentage error (AAPE) of 5% and correlation coefficient (R) of 0.93. In addition, the new model outperformed three existing ROP models. The novelty in this paper is the application of the clustering technique in which the formations are clustered based on their compressive strength range to predict the ROP. Clustering yielded accurate ROP prediction compared to the field ROP.
机译：在钻探操作期间，优化渗透率（ROP）非常重要，因为它可以显着降低钻探过程的总体成本。 ROP定义为钻头打破岩石以加深孔的速度，以英尺/小时或米/小时为单位进行测量。在钻探之前，ROP预测非常具有挑战性，因为它取决于许多应优化的参数。文献中已经开发了几种模型来预测ROP。大多数已开发的模型都使用了钻井参数，例如钻压（WOB），抽速（Q）和每分钟的绳柱转数（RPM）。很少有研究人员通过包括少量实际现场测量来考虑泥浆性质对ROP的影响。本文介绍了一种新的鲁棒模型，可使用7000的两个钻井参数（WOB，Q，ROP，扭矩（T），竖管压力（SPP），单轴抗压强度（UCS）和泥浆特性（密度和粘度）来预测ROP实时数据测量，此外，还确定了钻井液特性，岩石强度和钻井参数对ROP的相对重要性，所得结果表明，ROP受WOB，RPM，T和马力（HP）的影响很大，其中WOB，RPM，T和HP的测定系数（T-2）分别为0.71、0.87、0.70和0.92； ROP还显示出强大的泥浆流体特性函数，其中R-2为0.70和。塑性粘度（PV）和泥浆密度分别为0.70，在500多个现场数据点上ROP和屈服点（YP）之间没有发现明确的关系，新模型预测ROP的平均绝对百分比误差（AAPE）为5 ％，相关系数（R）为0.93。此外， del优于三个现有的ROP模型。本文的新颖之处在于聚类技术的应用，其中基于地层的抗压强度范围对地层进行聚类以预测ROP。与现场ROP相比，聚类产生了准确的ROP预测。

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