New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks

Real-time prediction of formation pressure gradient while drilling

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.

Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters

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.

New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

Rate of penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the petroleum industry, most of the well cost is taken by the drilling operations. Therefore, it is very crucial to drill carefully and improve drilling processes. Nevertheless, it is challenging to predict the influence of every single parameter because most of the drilling parameters depend on each other and altering an individual parameter will have an impact on the rest. Due to the complexity of the drilling operations, up to the present time, there is no reliable model that can adequately estimate the ROP. Artificial intelligence (AI) might be capable of building a predictive model from a number of input parameters that correlate to the output parameter. A real field dataset, of shale formation, that contains records of both drilling parameters such as, rotation per minute (RPM), weight on bit (WOB), drilling torque (τ), standpipe pressure (SPP) and flow pump (Q) and mud properties such as, mud weight (MW), funnel and plastic viscosities (FV) (PV), solid (%) and yield point (YP) were used to predict ROP using artificial neural network (ANN). A comparison between the developed ANN-ROP model and the number of selected published ROP models were performed. A novel empirical equation of ROP using the above-mentioned parameters was derived based on ANN technique which is able to estimate ROP with excellent precision (correlation coefficient (R) of 0.996 and average absolute percentage error (AAPE) of 5.776%). The novel ANN-based correlation outperformed three published empirical models and it can be used to predict the ROP without the need for artificial intelligence software.