Geomechanical parameter estimation from mechanical specific energy using artificial intelligence

Abstract

A comprehensive knowledge of geomechanical characteristics of the formations above a reservoir (cap rock, in particular) can prevent a large spectrum of problems during drilling operation. Furthermore, access to such information during drilling operation greatly contributes to proper adjustment of controllable parameters and enhanced rate of penetration (ROP). Unfortunately, due to the high costs associated with the acquisition of petrophysical logs, these sets of data are commonly acquired within the reservoir interval only, i.e. at most 20% of total drilled length. As such, the present contribution is an attempt to use the concept of mechanical specific energy (MSE) to estimate geomechanical parameters of rock. For this purpose, data was collected from two vertical wells drilled into an oilfield in southwest of Iran. Firstly, based upon available drilling reports and Tukey method, outlier data points were detected and omitted. Then, MSE was evaluated using different models proposed for this purpose previously. The evaluation results indicated that, the results obtained from Dupriest and Koederitz (2005) model were in good agreement with actual conditions at the considered wells. Therefore, the obtained MSE from this model along with flow rate (FR), bit tooth wear (CT), and Depth logs were used as input for multivariate nonlinear regression (MNLR) method as well as multi-layer perceptron neural network combined with cuckoo optimization algorithm (MLP-COA) and also with particle swarm optimization (MLP-PSO) to estimate confined compressive strength (CCS), uniaxial compressive strength (UCS), internal friction angle (ϕ), and Poisson's ratio (ν). Models were trained on data from one well and then validation-tested on the other well's data. Results of adopting these models indicated that, as far as the estimation of geomechanical parameters was concerned, intelligent models were of higher accuracy and reliability than the regression model. A comparison between the results of MLP-COA and MLP-PSO models showed that, COA outperforms PSO algorithm in achieving a model of higher accuracy and reliability. Results of the three models indicated that, the presented method in this research possesses large potentials for estimating CCS, UCS, and ϕ parameters, and it can be stipulated with certainty that, the proposed method can be used to estimate the parameters at other wells across the field under study provided further data is available from more wells and even the formations overlying the reservoir. However, application of this method for estimating ν shall be practiced with care.