Accurate real-time pore pressure prediction is crucial especially in drilling operations technically and economically. Its prediction will save costs, time and even the right decisions can be taken before problems occur. The available correlations for pore pressure prediction depend on logging data, formation characteristics, and combination of logging and drilling parameters. The objective of this work is to apply artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to introduce two models to estimate the formation pressure gradient in real-time through the available drilling data. The used parameters include rate of penetration (ROP), mud flow rate (Q), standpipe pressure (SPP), and rotary speed (RS). A data set obtained from some vertical wells was utilized to develop the predictive model. A different set of data was utilized for validating the proposed artificial intelligence (AI) models. Both models forecasted the output with a good correlation coefficient (R) for training and testing. Moreover, the average absolute percentage error (AAPE) did not exceed 2.1%. For validation stage, the developed models estimated the pressure gradient with a good accuracy. This study proves the reliability of the proposed models to estimate the pressure gradient while drilling using drilling data. Moreover, an ANN-based correlation is provided and can be directly used by introducing the optimized weights and biases, whenever the drilling parameters are available, instead of running the ANN model.

Equivalent circulating density (ECD) is considered a critical parameter during the drilling operation, as it could lead to severe problems related to the well control such as fracturing the drilled formation and circulation loss. The conventional way to determine the ECD is either by carrying out the downhole tool measurements or by using mathematical models. The downhole measurement is costly and has some limitations with the practical operations, while the mathematical models do not provide a high level of accuracy. Determination of the ECD should have a high level of accuracy, and therefore, the objective of this study is to employ machine learning techniques such as artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs) to predict the ECD from only the drilling data with a high accuracy level. The study utilized drilling data from a horizontal drilling section that includes drilling parameters (penetration rate, rotating speed, torque, weight on bit, pumping rate, and pressure of standpipe). The models were built and tested from a data set that has 3570 data points, and another data set of 1130 measurements was employed for validating the models. The accuracy of the models was determined by key performance indices, which are the coefficient of correlation (R) and the average absolute percentage error (AAPE). The results showed the strong prediction capability for ECD from the two models through training, testing, and validation processes with R greater than 0.98 and a very low error of 0.3% for the ANN model, while ANFIS recorded R of 0.96 and AAPE of 0.7, and hence, the two models showed great performance for ECD estimation application. Also, the study introduces a newly developed equation for ECD determination from drilling data in real time.

Real-time prediction of the formation pressure gradient is critical mainly for drilling operations. It can enhance the quality of decisions taken and the economics of drilling operations. The pressure while drilling tool can be used to provide pressure data while drilling, but the tool cost and its availability limit its usage in many wells. The available models in the literature for pressure gradient prediction are based on well logging or a combination of some drilling parameters and well logging. The well-logging data are not available for all wells in all sections in most wells. The objective of this paper is to use support vector machines, functional networks, and random forest (RF) to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters. The used parameters are mud flow rate (Q), standpipe pressure, rate of penetration, and rotary speed (RS). A data set of 3239 field data points was used to develop the predictive models. A different data set unseen by the model was utilized for the validation of the proposed models. The three models predicted the pore pressure gradient with a correlation coefficient (R) of 0.99 and 0.97 for training and testing, respectively. The root-mean-squared error (RMSE) ranged from 0.008 to 0.021 psi/ft for training and testing, respectively, between the predicted and the actual pore pressure data. Moreover, the average absolute percentage error (AAPE) ranged from 0.97% to 3.07% for training and testing, respectively. The RF model outperformed the other models by an R of 0.99 and RMSE of 0.01. The developed models were validated using another data set. The models predicted the pore pressure gradient for the validation data set with high accuracy (R of 0.99, RMSE around 0.01, and AAPE around 1.8%). This work shows the reliability of the developed models to predict the pressure gradient from both mechanical and hydraulic drilling parameters while drilling.

Formation resistivity is crucial for petrophysics, and formation evaluation. Laboratory measurements and/or well logging can be used to provide resistivity data. Laboratory measurements are time-consuming and costly, limiting their use. Furthermore, certain log records may be missing in some segments for a variety of reasons, including instrument failure, poor hole conditions, and data loss due to storage and incomplete recording. The purpose of this study is to apply support vector machines (SVM), and functional networks (FN) to introduce intelligent models for formation resistivity prediction using other available logging parameters. The well logs include gamma ray, density, neutron and sonic data. The predictive models were built using a data collection of roughly 4,300 data points collected from vertical sections of complex reservoirs. For model training and testing, the data set was split at random in a 70:30 ratio. The predictive models were validated using a different set of data (around 1,300 points) that had not been seen by the model. The models predicted the target with a good correlation coefficient (R) of around 0.93 and accepted root mean squared error (RMSE) of 0.3 for training and testing. The suggested methods for estimating formation resistivity from available logging parameters are shown to be reliable in this study. Resistivity prediction can fill the missing gaps in log tracks and may save money by removing resistivity logs running in all offset wells in the same field.

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