Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models

The interest in artificial intelligence (AI) predictive models in the domains of petrophysics and well logs has been rapidly growing as it prevails as a powerful tool, given the relative data abundance. Formation resistivity prediction, despite its existing necessity, remains a challenge. The objectives of this study are to provide a framework of considerations and limitations of resistivity prediction and to introduce AI models to predict resistivity in horizontal low formation quality well. Logging while drilling data were obtained for the study from a 12 '' section of a horizontal low formation quality well. Statistical analyses were carried out to identify and remove insignificant features. RHOB, DTC, DTS, NPHI, and GR logs were used as input to build and train the model. Data scaling and transformation techniques were applied to improve the model's accuracy and accelerate the rate of convergence. Four models were built and trained using artificial neural network (ANN), adaptive neuro-fuzzy inference system types 1 and 2 (ANFIS 1 & ANFIS 2), and Support vector machine. Cross plots, coefficient of determination (R-2) and mean absolute percentage error (MAPE) were used to evaluate the effectiveness of the prediction. All of the four predictive models yielded comparable results, where R-2 values ranged between 0.90 and 0.95 for the training data set, and 0.89, to 0.91 for testing dataset. ANN model had an inherent complexity with two hidden layers, 30 neurons each. The main applications of resistivity predicted values are to be used qualitatively for geo-steering applications and to estimate the saturation profile of logged intervals. For those two applications, the resistivity prediction accuracy is subject to the relative significance of the value magnitude.

Automated summary

The interest in AI predictive models in petrophysics and well logs is growing rapidly as it prevails as a powerful tool, given the relative data abundance. This study provides a framework for resistivity prediction and introduces AI models for predicting resistivity in horizontal low formation quality wells.