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The current research introduces a new success case toward monitoring the drilling fluid rheology with an automation system by implementing the machine learning application. For the first time, four developed models were developed to predict the fluid rheology of Micromax oil-based mud type by implementing the artificial neural network to overcome the conventional field lab tests for the mud rheology monitoring that take more time and have human errors. The developed models predict the mud rheological properties as the plastic and apparent viscosities, yield point, and flow behavior index to be in real-time for evaluating the mud functionality. Mud weight and Marsh funnel were utilized as the only inputs for the rheology prediction models as these inputs have easy high-frequency measurements during the drilling operation. Real field data set of 244 complete measurements are used to train the models and another unseen set of 138 points for testing the models' accuracy. The correlation coefficient (R) and average absolute percentage error were calculated between the predicted values and the actual measurements to determine the models performance. Through the models' development, the models perform a high accuracy level that has a coefficient of correlation higher than 0.96 for all models except the yield point model which shows 0.92, besides, an accepted average absolute percentage error that ranges from 4 to 9.3% for the training and testing phases. Furthermore, the study proposes model-based correlations for predicting the drilling fluid rheology for easy application in the rig site without the need for running the codes. [ABSTRACT FROM AUTHOR]

The rock porosity is considered a key petrophysical property for the rock due to its great impact on the hydrocarbon reserve estimation and petroleum economics. The conventional methods for determining the rock porosity either from the logging tools, lab measurements for the cored samples, or using empirical correlations from other parameters are costly, time-consuming, or did not provide the required level of accuracy. The new horizon for implementing machine learning techniques as a new approach for predicting the rock porosity overcomes all of the above drawbacks. Therefore, the objective of this research is to develop a new model based on an artificial neural network (ANN) for predicting rock porosity from only drilling parameters that include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. The study used two data sets for building the model (3767 data points) and the second one for validating the developed ANN model (1676 data points). ANN model was built and optimized with deep sensitivity analysis for the ANN model parameters to achieve strong prediction results. ANN model showed a correlation coefficient (R) between the predicted and actual porosity values of 0.97 and 0.92 with average absolute percentage errors (AAPE) of 6.2 and 9.3% for training and testing, respectively. The model validation enhanced the high prediction performance as ANN achieved R of 0.95 and AAPE of 8.5%. The study provides new contributions as predicting the rock porosity for complex lithology formations (sandstone, shale, and carbonate), developing an ANN porosity model with a high level of accuracy, and a newly developed ANN-based equation for estimating the porosity from only the surface drilling data. [ABSTRACT FROM AUTHOR]

The dynamic Young's modulus (Edyn) is an important factor for evaluating the static Young's modulus which is an important parameter influences hydraulic fracturing design, drilling performance, in situ stresses estimation, and wellbore stability evaluation. Currently, the prediction of the Edyn requires the knowledge of the shear and compressional velocities and bulk density, which may not always be available. For the first time, an equation for evaluating Edyn in real-time from the weight on bit, the rate of penetration, torque, standpipe pressure, drill pipe rotation speed, and the drilling fluid flow rate is introduced. This new correlation was developed from the artificial neural networks (ANN) which were trained on 2054 data points from Well-A. The developed correlation was tested and validated on 871 and 2912 data points from Well-B and Well-C, respectively. The results showed that the ANN model evaluated Edyn with average absolute percentage error (AAPE) of 3.09%, for the training data. The developed equation estimated the Edyn with AAPE of only 3.38% and 3.73%, for the testing and validation data, respectively. [ABSTRACT FROM AUTHOR]