Accurate estimation of rock elastic and failure parameters plays a vital role in petroleum, civil and geotechnical engineering applications. During drilling operations, continuous logs of rock elastic and failure parameters are considered very helpful in optimizing geomechanical earth models. Commonly, rock elastic and failure parameters are estimated using well logs and empirical correlations. These are calibrated with rock mechanics laboratory experiments conducted on core samples. However, since these samples are expensive to get and time-consuming to test, artificial intelligence (AI) models based on available petrophysical well logs such as bulk density, compressional wave and shear wave travel times are utilized to predict the static Young's modulus (Estatic) and the unconfined compressive strength (UCS) – with an emphasis on carbonate rocks. We present two AI techniques in this study: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The dataset used in this study contains 120 data points obtained from a Middle Eastern carbonate reservoir from which we develop an empirically correlated ANN model to predict Estatic and an ANFIS model to predict the UCS. A comparison between the UCS, predicted by the proposed ANFIS model, and the published correlations show that the ANFIS model predicted the UCS with less error and with a high coefficient of determination. The error obtained from the ANFIS model was 4.5%, while other correlations resulted in up to 30% error on a published dataset. On the basis of the results obtained, we can say that the developed models will help geomechanical engineers to predict Estatic and the UCS using well logs without the need to measure them in the laboratory. Thematic collection: This article is part of the Naturally Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/naturally-fractured-reservoirs

中文翻译：

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使用人工智能工具预测碳酸盐静态杨氏模量和无侧限抗压强度的新视角

岩石弹性和破坏参数的准确估计在石油、土木和岩土工程应用中起着至关重要的作用。在钻井作业期间，岩石弹性和破坏参数的连续测井被认为对优化地质力学地球模型非常有帮助。通常，岩石弹性和破坏参数是使用测井和经验相关性来估计的。这些是通过对岩心样品进行的岩石力学实验室实验进行校准的。然而，由于这些样本的获取成本高昂且测试耗时，因此利用基于可用岩石物理测井记录（例如体积密度、纵波和横波传播时间）的人工智能 (AI) 模型来预测静态杨氏 s 模量 (Estatic) 和无侧限抗压强度 (UCS) – 重点是碳酸盐岩。我们在本研究中介绍了两种 AI 技术：人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS)。本研究中使用的数据集包含从中东碳酸盐岩储层获得的 120 个数据点，我们从中开发了一个经验相关的 ANN 模型来预测 Estatic 和一个 ANFIS 模型来预测 UCS。由提议的 ANFIS 模型预测的 UCS 与已发布的相关性之间的比较表明，ANFIS 模型以较小的误差和较高的决定系数预测了 UCS。从 ANFIS 模型获得的误差为 4.5%，而其他相关性在已发布的数据集上导致高达 30% 的误差。根据获得的结果，我们可以说，开发的模型将帮助地质力学工程师使用测井预测 Estatic 和 UCS，而无需在实验室中对其进行测量。专题合集：本文是自然断裂水库合集的一部分，可从以下网址获取：https://www.lyellcollection.org/cc/naturally-fractured-reservoirs