The prediction of continued profile for static Poisson’s ratio is quite expensive and requires huge experimental works, and the discontinuity in the measurement and the limited applicability and accuracy of the present empirical correlations necessitated the utilization of artificial intelligence with its prosperous application in oil and gas industry. This work aims to construct different artificial intelligence models for predicting static Poisson’s ratio of complex lithology at real time during drilling. The functional networks (FN) and random forest (RF) approaches were utilized using the mechanical drilling parameters as inputs. This study uses a vertical well with 1775 records from complex lithology containing shale, sand, and carbonate for model building. Besides, a different dataset from another well was used to check the models’ validity. The results demonstrated that both FN- and RF-based models predicted static Poisson’s ratio with significant matching accuracy. The FN technique results’ correlation coefficient (R) value of 0.89 and average absolute percentage error (AAPE) values of 10.23% and 10.28% in training and testing processes. While the RF technique is outperformed, as illustrated by the highest R values of 0.99 and 0.94 and the lowest AAPE values of 1.89% and 5.19% for training and testing processes, the robustness and reliability of the developed models were confirmed in the validation process with R values of 0.94 and 0.86 and AAPE values of 11.23% and 5.12% for FN- and RF-based models, respectively. The constructed models developed a basis for inexpensive static Poisson’s ratio prediction in real time with significant accuracy.

中文翻译：

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人工智能在钻井时静态泊松比预测中的应用

静态泊松比的连续轮廓的预测非常昂贵并且需要大量的实验工作，并且测量的不连续性以及当前经验相关性的适用性和准确性有限，因此必须在石油和天然气行业中广泛使用人工智能。 。这项工作旨在构建不同的人工智能模型，以在钻井过程中实时预测复杂岩性的静态泊松比。使用机械钻探参数作为输入，利用功能网络（FN）和随机森林（RF）方法。这项研究使用了一个垂直井，其中包含1775个来自复杂岩性的记录，其中包含页岩，沙子和碳酸盐，用于模型构建。此外，使用与另一口井不同的数据集来检查模型的有效性。结果表明，基于FN和RF的模型都可以预测静态泊松比，并且具有很高的匹配精度。FN技术结果的相关系数（R）值为0.89，在培训和测试过程中的平均绝对百分比误差（AAPE）值为10.23％和10.28％。尽管射频技术的性能优于传统技术，如训练和测试过程的最高R值分别为0.99和0.94，最低AAPE值为1.89％和5.19％，但在验证过程中证实了所开发模型的鲁棒性和可靠性。对于基于FN和RF的模型，R值分别为0.94和0.86，AAPE值分别为11.23％和5.12％。所构建的模型为实时的廉价静态泊松比预测提供了基础，而且精度很高。