A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Abstract

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

Time-Resolved Electromagnetic Near-Field Scanning: Dual Sparse Sampling in Time and Space

“…The Kriging method, also recognized as Gaussian process regression, embodies nonparametric probabilistic models reliant on kernels [9], [27]. Initially conceived within the domain of geology and mining [27], this statistical modeling approach has seen diverse applications, encompassing reconstruction of EM fields [9], [16], [28], [29]. In our approach, the Kriging method plays a central role in spatially sparse sampling by informing the probe's movement during near-field scanning.…”

A Comparative Study on the Performance of Covariance Functions in Gaussian Process Regression model: Application to Global Wheat Price

“…Next, GPR has had a significant impact on energy sustainability, with research being performed specifically for power system design optimization utilizing GPR based on Multi-Objective Bayesian Optimization (GPR-MOBO) [22]. Meanwhile, work on increasing the performance of GPR and Gradient Descent (GD) utilizing the inversion approach has been done to reduce computing time by implementing predictions on hydrocarbon depth in Seabed logging (SBL) [23]. Additionally, research comparing the RBF, RQ, Mat 3/2, and Mat 5/2 covariance functions from GPR has been conducted on NASA's lithium-ion battery data to forecast battery health issues [24].…”

Development of a Gaussian Process Model as a Surrogate to Study Load Bridging Performance in Racked Pallets

“…Although the developed finite element model represents a robust baseline to use in studying the characteristics of a unit load and their influence on pallet performance, a full analysis of the trends and interactions of all the identified factors increases the computing requirements exponentially. A surrogate model of a complex finite element model is commonly utilized in such scenarios, providing not only a more efficient analysis tool but also an accessible method for industry practitioners to design unit loads using acceptable approximations [21][22][23][24][25][26][27].…”