3D Localization for Internet of Underground Things in Oil and Gas Reservoirs

Magnetic Induction (MI) is an efficient wireless communication method to deploy operational internet of underground things (IoUT) for oil and gas reservoirs. The IoUT consists of underground things which are capable of sensing the underground environment and communicating with the surface. The MI-based IoUT enable many applications, such as monitoring of the oil rigs, optimized fracturing, and optimized extraction. Most of these applications are dependent on the location of the underground things and therefore require accurate localization techniques. The existing localization techniques for MI-based underground sensing networks are two-dimensional and do not characterize the achievable accuracy of the developed methods, which are both crucial and challenging tasks. Therefore, this paper proposes a novel three-dimensional (3D) localization technique based on Isometric scaling (Isomap) for future IoUT. Moreover, this paper also presents the closed-form expression of the Cramer Rao lower bound (CRLB) for the proposed technique, which takes into account the channel parameters of the underground magnetic-induction. The derived CRLB provides the suggestions for an MI-based underground localization system by associating the system parameters with the error trend. Numerical results demonstrate that localization accuracy is affected by different channel and networks parameters such as the number of underground things, ranging error variance, size of the coils, and the transmitting power. The root mean square error performance of the proposed technique shows that increase in the number of turns of the coils, transmitting power, and the number of anchors improves the performance. Results also show that the proposed technique is robust to the ranging error variance in the range of 10 to 30 %; however, a further increase in the ranging error variance does not allow to achieve acceptable accuracy. Also, the results show that the proposed technique achieves an average of 30 % better localization accuracy compare to the traditional methods.