Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R²) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes.

One of the most common problems in drilling operations is lost circulation, which can significantly increase well costs and lead to issues such as pipe sticking, blowouts, and even well closures. Identifying thief zones using analytical models is especially difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid. This study presents an artificial intelligence-based model designed to predict lost circulation zones. It investigates the underexplored potential of WV-curves for feature selection. Traditionally used to represent the spectral characteristics of training data, their role in feature selection has not been widely examined in the literature. The presentation of WV-curves is modified, and their effectiveness in identifying the optimal number of input and hidden neurons is evaluated. In this research study, a total of 15,000 data points were used and collected from oil wells in the Middle East. The artificial neural network (ANN) model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. In addition, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, offering a broader approach compared to other available ANN models. This advancement will also greatly facilitate future studies, enabling the prediction of lost circulation zones, and enabling advanced planning of appropriate prevention and remediation methods during the well planning phase to reduce the risk of lost circulation. Nevertheless, it should be noted that one limitation of the proposed methodology relates to data availability, as comprehensive formation parameters were not fully accessible; the inclusion of additional formation data may offer opportunities for further improvement in future studies.