Use of machine learning and data analytics to increase drilling efficiency for nearby wells

Rate of penetration (ROP) is one of the most important parameters of the drilling operation. Optimizing the ROP will reduce the overall cost of the drilling process. ROP depends on many variables such as drilling parameters [flow rate (Q), RPM, torque (T), weight on bit (WOB), stand pipe pressure (P)], fluid properties (mud density and plastic viscosity), and formation strength (UCS). The objectives of this paper are (1) to evaluate the effect of the drilling parameters and the drilling fluid parameters on rate of penetration (ROP), (2) construct a new artificial neural network (ANN) technique to estimate the ROP as a function of drilling parameters and fluid properties using actual field measurements (3333 data points), and (3) converting the black box of the ANN model to a white box by developing a new ROP correlation based on the optimized weights and biases of the developed model. The optimization process of the ANN model showed that the optimum number of neurons was 20, which resulted in the lowest average absolute percentage error (AAPE) and the highest correlation coefficient (R). The developed ANN model was able to estimate ROP with high accuracy (R of 0.99 and AAPE of 5.6%). The developed empirical correlation for ROP prediction outperformed the previous models. The high accuracy of the developed correlation (AAPE of 4%) confirmed the importance of compiling the drilling parameters and the drilling fluid properties.

New Approach to Optimize the Rate of Penetration Using Artificial Neural Network

A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking

Application of Artificial Intelligent Systems in ROP Optimization: a Case Study

Developing a new rigorous drilling rate prediction model using a machine learning technique

A rate of penetration (ROP) prediction equation for an insert roller cone bit is determined from laboratory drilling tests as a function of bit weight, well depth and laboratory measured rock properties. A complete description of each of the seven rock types used in the study is presented. A comparison of ROP at different flow rates for each rock shows minimal hydraulic cleaning problems at the base hydraulic energy levels chosen. The predictive equation will be valuable in predicting drilling rate and understanding how the rock properties affect it.

M. Khodaverdian

Papers

Roller-Bit Penetration Rate Response as a Function of Rock Properties and Well Depth