Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network

Abstract

Rate of penetration (ROP) enhancement serves as a key factor in reducing drilling time and hence drilling costs. ROP enhancement requires identification of the parameters affecting this rate. However, the large number of effective parameters, which are further immersed in noise, makes it difficult to present a highly accurate and comprehensive model. In the present research, in order to predict the drilling ROP in one of the vertical wells drilled into the Karanj Oilfield, a hybrid model composed of a multilayer perceptron (MLP) neural network together with either a particle swarm optimization (PSO) algorithm or a cuckoo optimization algorithm (COA) was used. For this purpose, first petrophysical logs and drilling data were denoised using the Savitzky–Golay filter. Then, the 'plus-l-take-r' method was used to select superior features. Feature selection results indicated that an increase in the number of input parameters tends to reduce the error associated with the estimator model; however, the error reduction rate was seen to be negligible for models with five or more input parameters. Therefore, five parameters were considered as input parameters in MLP-COA and MLP-PSO hybrid models: rotary speed, weight on bit, shear wave slowness, compressional wave slowness, and flow rate. A comparison of errors and coefficients of determination in the training phase of the two models indicated that MLP-COA model tended to converge way faster and more accurately. The small difference in generated error using this model between training and testing phases indicated the high reliability and generalizability of the model. Comparing the results of the model trained with raw and denoised data against the same set of selected features clearly showed the positive effect of the denoising step on the accuracy of the model. Validation of the proposed model via the multilinear regression method was indicative of the superior performance of the MLP-COA model, so that it could be confidently stipulated that this model can be used to estimate the ROP at other vertical wells near the studied well. Further, provided the required information is available, this method can predict the ROP to high accuracy in vertical oil and gas wells.