The infiltration of the drilling fluids into the drilled formation causes significant alterations in the rock properties due to the interaction between the drilling fluid and the rock pore system. The objective of this study is to evaluate the impact of the most common weighting materials used in water‐based mud (WBM) on carbonate pore system and rock characteristics. Rock‐mud interaction was imposed by using a customized high‐pressure high‐temperature (HPHT) filtration test cell under 2068 kPa differential pressure and 93°C temperature to simulate downhole conditions. For filtration properties, ilmenite WBM showed the maximum values (10.9 cm3 filtrate volume and 8.7 mm thickness), while baryte recorded the lowest filtrate volume (6.2 cm3) and thickness (4.2 mm). Nuclear magnetic resonance (NMR) profiles illustrated the changes in the rock pore system due to two aspects: precipitation and dissolution. A general porosity reduction was recorded with all formulations, namely 7.5% and 10.1% for hematite and ilmenite, respectively. The rock permeability showed severe damage after mud exposure that caused the rock average pore size to decrease from macro to meso‐porous. After the mud invasion, the rock electrical resistivity showed alterations with all drilling fluids. Compressional wave velocities (Vp) showed an increasing trend that ranged from 2.52% increase for baryte‐WBM to 6.35% increase by Micromax‐WBM. A general reduction was found for shear wave velocities (Vs) after mud exposure, Micromax‐WBM showed no changes in Vs, while hematite and ilmenite showed the largest decreases of 6.71% and 5.56%, respectively.

Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper delineates an alternative solution for the prediction of sonic log data by means of Machine Learning (ML). This study takes advantage of an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) ML techniques to predict compressional and shear wave slowness from drilling data only. In particular, the network is trained utilizing 2000 data points such as weight on bit (WOB), rate of penetration (ROP), standpipe pressure (SPP), torque (T), drill pipe rotation (RPM), and mud flow rate (GPM). Consequently, acoustic properties of the rock can be estimated solely from readily available parameters thereby saving both costs and time associated with sonic logs. The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs. The developed ANFIS model was able to predict compressional and shear wave slowness with correlation coefficients of 0.94 and 0.98 and average absolute percentage errors (AAPE) of 1.87% and 2.61%, respectively. Similarly, the SVM model predicted sonic logs with high accuracy yielding to correlation coefficients of more than 0.98 and AAPE of 0.74% and 0.84% for both compressional and shear logs, respectively. Once a network is trained, the approach naturally lends itself to be integrated as a real time service. This study outlines a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters. Importantly, the model has been verified for wells drilled in different formations with complex lithology substantiating the effectiveness of the approach.

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